Modeling of Kraft Mill Chemical Balance

by

Daniel Moreira Saturnino

A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy

Graduate Department of Chemical Engineering and Applied Chemistry University of Toronto

Copyright by Daniel Moreira Saturnino – 2012

ii

Modeling of Kraft Mill Chemical Balance

Daniel Moreira Saturnino

Doctor of Philosophy

Graduate Department of Chemical Engineering and Applied Chemistry University of Toronto

2012

Abstract

The reduction of mill effluent discharge as a result of stringent environmental legislations can

have a significant impact on sodium (Na) and sulfur (S) balances in the kraft pulping process. In

order to maintain a proper balance of Na and S, kraft mills may need to adopt different makeup

strategies. For this purpose, a dynamic model was developed to predict the Na and S balance in

the kraft recovery cycle, as well as the accumulation of undesirable non-process elements such as

chlorine (Cl) and potassium (K).

The model was developed using the CADSIM software and was validated using data obtained

from a Brazilian bleached kraft pulp mill. The calculated data from the model showed good

agreement with mill data with respect to all parts of the mill simulated. Dynamic tests designed

to calculate the white liquor sulfidity over specific periods of time also presented good

agreement. The result indicates that the model is able to describe the balance of chlorine,

potassium, sodium and sulfur in the kraft process.

A study conducted to evaluate the Cl and K accumulation agrees with the expected behaviour

observed in mill data. The presence of ash treatment systems allow to reduce Cl and K contents

in recovery boiler precipitator ash from 4.2 mol% Cl(Na+K) to 1.25 mol % and from 2.25 mol %

K/(Na+K) to 0.8 mol% for 100% ash treated. The tests performed for Na and S balances focused

iii

in the makeup requirement for two situations: ash purging and ash treatment to control Cl and K

levels. The use of ash treatment systems reduced Na and S makeup requirement from 5 to 50%

depending on the amount of ash treated.

A simple mathematical model was then used to estimate the Cl balances around the recovery

cycle. Given that the proper simplifications are applied, the CADSIM model and the CSTR

model presented good agreement in estimating the Cl balances. This result provided not only

another method for the CADSIM model to be validated but also a way to calculate a rough

estimate for Cl balance.

iv

Acknowledgements

I would like to thank Professor Honghi Tran, my supervisor, for his guidance. His admirable

work integrating academy and industry gave me an incredible opportunity to learn how to apply

the knowledge obtaining during these years at the university to solve practical problems. He is

an example of leadership for the students who worked with him.

I thank the financial support of the members of the research consortium on “Increasing Energy

and Chemical Recovery Efficiency in the Kraft Process”, specially the Aracruz mill for

providing me the data needed to complete this project, and the Natural Sciences and Engineering

Research Council of Canada (NSERC). I am grateful for the help provided by Larry Wasik of

Aurel Systems during the course of this work; Larry helped to clarify questions about CADSIM

and provided great advices on model development and validation.

I thank my mother Ana and my father Braz, my sister Miriam and my brother-in-law Angelo for

their encouragement and support in all moments.

Finally, I would like to thank all friends who contributed in some way to this work, specially,

those who helped me with this final document.

v

Table of Contents Abstract ii Acknowledgements iv Table of Contents v List of Tables vii List of Figures ix List of Appendices xii Nomenclature xiii 1. INTRODUCTION 1 1.1 Kraft Pulping 1

1.2 Chemical Balances in the Chemical Recovery Cycle 3 1.3 Ash Purging and Ash Treatment 4 1.4 Objectives 4

2. LITERATURE SURVEY 5 2.1 Introduction 5 2.2 Sodium and Sulfur Balance 5 2.2.1 Sulfur Losses 6 2.2.2 Sodium and Sulfur Makeup 9 2.2.3 Chlorine Dioxide Effluent 9

2.3 Chlorine and Potassium Balance 11 2.3.1 Enrichment Factor 12 2.3.2 Factors Affecting the Enrichment Factors 13 2.3.2.1 Lower Bed Temperature 14 2.3.2.2 Black Liquor Composition 14 2.3.2.3 Concentration of SO2 in the Flue Gases 15 2.3.2.4 Amount of Combustion Air 15 2.3.2.5 Release of Inorganic Compounds 16

2.3.3 Ash Treatment and the Chlorine and Potassium Balance 17 2.4 Process Simulation in the Pulp and Paper Industry 18 2.4.1 Simulation of Kraft Mill Chemical Balance 20 2.4.2 Pulp and Paper Simulators 21 2.5 Analysis of the Literature Survey 23 3. METHODOLOGY 24 3.1 Evaluation of Pulp and Paper Simulators 24 3.1.1 CADSIM Programming 31 3.1.2 Modules Implementation into CADSIM 32 3.1.2.1 Ash Treatment Block 32 3.1.2.2 Lime Kiln Model 34 3.1.3 Chemical Balance Model Validation – Mill Case Study 35 3.1.3.1 Data Availability 35 3.1.3.2 Data Processing 36 3.1.3.3 Steady State and Dynamic Simulation of the Mill 38 3.2 Cl and K Enrichment Factor Simulation 40 3.2.1 EPAC Model 40 3.2.2 Determination of Inorganic Release 42

vi

3.2.3 Entrained Flow Reactor Tests 43 3.3 Summary 45 4. FACTORS AFFECTING THE CL AND K ENRICHMENT FACTOR 48 4.1 Determination of Inorganic Release from Black Liquor 48 4.2 Parameters Evaluated 50

4.2.1 Effect of Bed Temperature 51 4.2.2 Effect of Combustion Air 52 4.2.3 Effect of Cl and K Content in Black Liquor 54 4.2.4 Effect of Sulfur Content in Black Liquor 56 4.2.5 Effect of Inorganic Release 58 4.3 Entrained Flow Reactor Tests 59 4.4 Summary 60

5. MODEL VALIDATION FOR A KRAFT MILL 61 5.1 Validation of Neural Network 61

5.2 Steady State Simulation of Mill Areas 63 5.2.1 Evaporation Plants of Case Study Mill 64 5.2.2 Recovery Boilers of Case Study Mill 70 5.2.3 Causticizing Plants of Case Study Mill 75 5.3 Full Mill Steady State Simulation 80 5.4 Dynamic Simulation of Case Study Mill 81 5.5 Limitations and Model Specifications 83 5.6 Summary 86

6. SENSITIVITY ANALYSIS OF CADSIM MODEL 87 6.1 Accumulation of the Cl and K in the Precipitator Ash 87 6.1.1 Steady-State Definition 88 6.1.2 Effect of Cl and K Input 89 6.1.3 Effect of Soda Inventory 90 6.1.4 Effect of Ash Purging / Ash Treatment 92 6.2 Balance of Na and S in the Recovery Cycle 95 6.2.1 Effect of Ash Purging versus Ash Treatment 95

6.2.2 Effect of Chlorine Dioxide Effluent 99 6.3 Summary 99

7. SIMPLE CSTR MATERIAL BALANCE MODEL 101 7.1 Mathematical Modeling of the Kraft Process 101 7.2 Analysis of the Model Variables 106 7.3 Summary 108

8. CONCLUSIONS 109

9. RECOMMENDATIONS 110 10. REFERENCES 111 11. APPENDICES 119

vii

List of Tables Table 2.1 - Summary of ClO2 Generation Processes 10 Table 2.2 - Amount of By-products from ClO2 Generators 11 Table 3.1 - Input Data for Multiple-Effect Evaporator System 25 Table 3.2 - Comparison of Results of the Simulation of a Multiple-Effect Evaporator Problem

28

Table 3.3 - Input Data for Recovery Boiler Balance Calculation 28 Table 3.4 - Comparison of Results of the Simulation of a Recovery Boiler Balance Problem

30

Table 3.5 - Variables Collected for Different Mill Areas 37 Table 4.1 - Black Liquor Composition (wt% - dry basis) 50 Table 4.2 - Average Value of Experimental Release Factor 50 Table 4.3 - Black Liquor Elemental Analysis 51 Table 4.4 - Black Liquor Elemental Analysis for Cl and K Test 54 Table 4.5 - Black Liquors with Different Sulfur Content 56 Table 4.6 - Comparison of Enrichment Factors Determined by EFR Tests and the Model 60 Table 5.1A - Input Data Used for Evaporation Plant 1 Simulation 65 Table 5.1B - Comparison of Steady State Data for Evaporation Plant 1 65 Table 5.2A - Input Data Used for Evaporation Plant 2 Simulation 66 Table 5.2B - Comparison of Steady State Data for Evaporation Plant 2 66 Table 5.3A - Input Data Used for Evaporation Plant 3 Simulation 66 Table 5.3B - Comparison of Steady State Data for Evaporation Plant 3 67 Table 5.4A - Input Data Used for Evaporation Plant 4 Simulation 67 Table 5.4B - Comparison of Steady State Data for Evaporation Plant 4 68 Table 5.5A - Input Data Used for Evaporation Plant 5 Simulation 68

viii

Table 5.5B - Comparison of Steady State Data for Evaporation Plant 5 69 Table 5.6A - Input Data for Recovery Boiler A Simulation 71 Table 5.6B - Comparison of Steady State Data for Recovery Boiler A 72 Table 5.7A - Input Data for Recovery Boiler B Simulation 72 Table 5.7B - Comparison of Steady State Data for Recovery Boiler B 73 Table 5.8A - Input Data for Recovery Boiler C Simulation 73 Table 5.8B - Comparison of Steady State Data for Recovery Boiler C 74 Table 5.9A - Input Data Used for Causticizing Plant 1 Simulation 76 Table 5.9B - Comparison of Steady State Data for Causticizing Plant 1 76 Table 5.10A - Input Data Used for Causticizing Plant 2 Simulation 77 Table 5.10B - Comparison of Steady State Data for Causticizing Plant 2 77 Table 5.11A - Input Data Used for Causticizing Plant 3 Simulation 78 Table 5.11B - Comparison of Steady State Data for Causticizing Plant 3 79 Table 6.1 - Comparison of Two ClO2 Processes 99

ix

List of Figures Figure 1.1 - Schematic Diagram of the Kraft Process 2 Figure 2.1 - Sodium and Sulfur Losses from the Chemical Recovery Cycle 8 Figure 3.1 - Block Diagram for Six-Effect Evaporator Process Simulated in WinGEMS 26 Figure 3.2 - Block Diagram for Six-Effect Evaporator Process Simulated in CADSIM 27 Figure 3.3 - Recovery Boiler Model for Energy and Material Balance in WinGEMS 29 Figure 3.4 - Schematic Diagram of the Simulated Recovery Boiler 30 Figure 3.5 - Schematic Diagram of a Neural Network 33 Figure 3.6 - Schematic Diagram for the Lime Kiln Model 35 Figure 3.7 - Schematic Diagram of Validation Process Used 39 Figure 3.8 - Schematic Diagram for Model Developed in FactSage 42 Figure 3.9 - Thermogravimetric Apparatus 43 Figure 3.10 - Entrained Flow Reactor and Fume Sampling Device 45 Figure 3.11 - Schematic Diagram of Model Development 45 Figure 3.12 - Schematic Diagram of Thesis Work 47 Figure 4.1 - Release Factor Determination for Black Liquor Sample 49 Figure 4.2 - Chloride and Potassium Enrichment Factors as a Function of Temperature 51 Figure 4.3 - Chloride and Potassium Enrichment Factors as Function of Combustion Air 52 Figure 4.4 - Na, K and Cl Release from Char Bed as a Function of Combustion Air at 1000°C

53

Figure 4.5 - Effect of Higher Cl and K Contents on Cl Enrichment Factor 55 Figure 4.6 - Effect of Higher Cl and K Contents on K Enrichment Factor 55 Figure 4.7 - Effect of Sulfur Content on Cl and K Enrichment Factors 57 Figure 4.8 - Effect of Inorganic Release on Cl and K Enrichment Factors 59

x

Figure 5.1 - Schematic Diagram of the Ash Treatment System Block 62 Figure 5.2 - Comparison of Ash Solubilities Given by OLI and the Neural Network 63 Figure 5.3 - Schematic Diagram of the Mill Case Study 64 Figure 5.4 - Schematic Diagram of the Simulated Evaporation Plant 64 Figure 5.5 - Comparison between Simulated and Mill Values for Evaporation Plants 70 Figure 5.6 - Schematic Diagram of the Simulated Recovery Boiler 70 Figure 5.7 - Comparison between Simulated and Mill Values for Recovery Boilers 74 Figure 5.8 - Schematic Diagram of the Simulated Causticizing Plant 75 Figure 5.9 - Comparison between Simulated and Mill Values for Causticizing Plants 80 Figure 5.10 - Comparison between Simulated Values and Mill Value for Full Mill Simulation

81

Figure 5.11 - First Simulation Test of White Liquor Mill Sulfidity 82 Figure 5.12 - Second Simulation Test of White Liquor Mill Sulfidity 82 Figure 5.13 - Sulfidity Change Test due to a Short Period Shutdown of ClO2 Plant 83 Figure 6.1 - Determination of Steady State Condition for Cl and K Ash Concentration 88 Figure 6.2 - Effect of Cl and K Input on Steady State Concentration for Cl = K = 1kg/ton pulp

89

Figure 6.3 - Effect of Cl and K Input on Steady State Concentration for Cl = K = 2kg/ton pulp

90

Figure 6.4 - Cl Accumulation in Precipitator Ash – Effect of Inventory 91 Figure 6.5 - K Accumulation in Precipitator Ash – Effect of Inventory 91 Figure 6.6 - Time for Cl and K Concentration to Reach Steady State for Different Inventory

92

Figure 6.7 - Accumulation of Cl and K in the Precipitator Ash – 5, 10 and 20% Ash Purging

93

Figure 6.8 - Accumulation of Cl and K in the Precipitator Ash – 5, 10 and 20% Ash Treated

94

xi

Figure 6.9 - Cl and K Levels in the Precipitator Ash for Different Portion of Ash Treated

94

Figure 6.10 - Na and S Makeup Requirement to Maintain 29.5% Sulfidity (AA) – 33% Ash Purging

96

Figure 6.11 - Na and S Makeup Requirement to Maintain 29.5% Sulfidity (AA) – 33% Ash Treated

97

Figure 6.12 - Total Makeup Requirement for Different Fractions of Ash Purged 98 Figure 6.13 - Total Makeup Requirement for Different Fractions of Ash Treated 98 Figure 7.1 - Schematic Representation of Chemical Recovery Cycle as a CSTR Model 102 Figure 7.2 - Schematic Diagram of CSTR 102 Figure 7.3 - Graphic Representation of Equation 7.9 with C0 = 0, Cin = 4kg/m3 and f = 3.5m3/day

104

Figure 7.4 - Comparison of CADSIM data for Cl Accumulation Test and Equation 7.9 assuming C0 = 0, Cin = 4.2kg/m3 and f = 3.5m3/day

105

Figure 7.5 - Effect of Input on Cl Accumulation in Precipitator Ash 106 Figure 7.6 - Effect of Initial Concentration on CSTR Model 108

xii

List of Appendices

Appendix A - FactSage Software 120 Appendix B - OLI Software 122 Appendix C - Preparation of Black Liquor Sample 124 Appendix D - Evaporation Calculation Routine 125 Appendix E - Recovery Boiler Calculation Routine 131 Appendix F - Causticizing Plant Calculation Routine 149

xiii

Nomenclature Qo = Initial amount of Cl dissolved in tank (kg)

V = Tank volume (m3)

f = Flow rate of input stream (m3/s)

Cin = Input concentration of Cl in the tank (kg/m3)

dQ/dt = Rate of change of Cl amount in the tank

Q = Amount of Cl at any time (kg)

t = Unit of time (s)

K = Constant of integration

1

1. Introduction

1.1 Kraft Pulping and the Chemical Recovery Process

Kraft process is the most important chemical pulping process in which the cellulose fibres are

extracted from wood through the use of chemicals. An overview of the kraft process is shown in

Figure 1.1. Kraft mills typically use wood that has been debarked and chopped into chips. In the

first step of the process, the wood chips are sent to a digester where they are mixed with cooking

liquor, also known as white liquor. The white liquor is an aqueous solution mostly composed of

sodium hydroxide (NaOH) and sodium sulphide (Na2S) [1].

After cooking, cellulose fibres are separated from the liquid and washed to make pulp which

corresponds to the second step of the process in Figure 1.1. The liquid obtained after the

separation of the pulp is known as black liquor. Black liquor contains two fractions: organic and

inorganic. The organic fraction is a mixture of lignin, hemicellulose and other dissolved material

from wood, while the inorganic fraction is mostly composed of the residual cooking chemicals.

In order to make kraft pulping economically feasible, the cooking chemicals have to be

recovered, which is accomplished by the chemical recovery process [2].

The chemical recovery process has two parts: the first involves the concentration and combustion

of the black liquor which removes the organic portion of the liquor. The second part of the

recovery process is the causticizing of the inorganic chemicals produced after the combustion of

the liquor.

The concentration of the black liquor is typically done in a multiple effect evaporation system

indicated as step 3 in Figure 1.1. The black liquor is concentrated from 15% to over 70% dry

solids. The concentrated black liquor is then burnt in a recovery boiler in the fourth step of

process. The combustion is used to generate steam and to convert sodium and sulfur compounds

into Na2S and Na2CO3 [3].

The Na2S and Na2CO3 form a molten smelt at the bottom of the recovery boiler. From there the

smelt pours into a smelt dissolver, where it is mixed with diluted white liquor, producing green

liquor. The green liquor is pumped to the slaker where reburned lime (mainly CaO) is added and

slaked lime (Ca(OH)2) is formed. The resulting calcium hydroxide reacts with Na2CO3 in the

2

green liquor, converting it into NaOH and precipitating CaCO3 (lime mud). This reaction takes

place in step 5 of Figure 1.1.

Lime mud is separated from the liquor by filtration or by sedimentation. It is then washed,

dewatered, and fed to a lime kiln where it is calcined to reburned lime (step 6 in Figure 1.1). The

liquor obtained after separation is the white liquor which is the final product of the recovery

process and can be reused as cooking liquor in the digester.

Figure 1.1 – Schematic Diagram of the Kraft process [4].

The challenge of the chemical recovery process is to produce white liquor with proper

concentrations of NaOH and Na2S in order to produce cellulose pulp with desired characteristics.

These concentrations are a result of a material balance between losses from the process and

makeup chemicals added by the mill operators. Due to environmental regulations, many mills

have focused their efforts on reducing losses from the process and reusing as much as possible of

the effluent collected around the mill. As a consequence, the makeup practice needs to account

for this change. Since the amount and composition of effluents reused varies widely, the

proposition of a makeup strategy is difficult as will be pointed out in the next section.

3

1.2 Chemical Balances in the Recovery Cycle

Reducing the chemical losses and recovering liquor spills can have a great effect on the

concentrations of NaOH and Na2S (chemical balance). Usually, the amount of sodium (Na) and

sulfur (S) makeup can be reduced, bringing economic benefits to the mill. On the other hand, the

concentration of unwanted chemicals such as chloride (Cl) and potassium (K), that would be lost

with spills or purges, may increase over time. The concern here is to keep the right balance of

sulfur and sodium which is an important parameter in obtaining high quality cellulose pulp. It is

also desired to keep Cl and K concentrations at low levels to avoid corrosion in the equipment

[5]. If streams rich in Na, S, Cl and K as those from the chemical plant are reused, the strategy to

keep the chemicals concentration close to optimum has to change.

Originally, the right concentration of the pulping chemicals in the mills was obtained by

replacing the chemical losses with sodium sulfate (Na2SO4) and sodium hydroxide (NaOH) [3].

The chemical losses include liquor spills (leaks from process equipment or overflows), boiler and

kiln emissions, and deliberate dumping such as lime mud and precipitator ash purges. On the

other hand, the makeup chemicals now include not only the regular NaOH and Na2SO4 but also

by-product liquors from other processes such as waste effluent from chlorine dioxide (ClO2)

generators.

The composition and amount of the by-product from chlorine dioxide (ClO2) generators vary

widely depending on the technology used. As an example, SVP, R2 and R7 generators produce

effluents containing sodium bisulfate (NaHSO4), while Solvay, R8 and R9 generators produce

by-products that contain sodium sesquisulfate (Na3H(SO4)2). The amount of saltcake effluent

(sodium and sulfur salts) also vary significantly. Mills using R3 generators have to deal with

10% more saltcake effluent than mills using R8 generators, which produces 24% more saltcake

than mills employing R10 generators [6].

If proper measures are not taken, the white liquor will not have the proper sodium and sulfur

concentrations and the mill may not obtain the best cellulose pulp. Other problems may also

happen, if for example the amount of sulfur input exceeds the losses from the process, the sulfur

content of the liquor would increase. Operating at high sulfur contents has many adverse effects

4

including increased TRS (total reduced sulfur) and SO2 emissions, and increased corrosion of

equipment [7].

1.3 Ash Purging and Ash Treatment

To solve a balance problem of excess sulfur many mills purge part of streams containing sulfur

such as the electrostatic precipitator ash from recovery boilers. This practice is usually employed

to reduce Cl and K levels in the mills [8 - 11]. Since the ash also contains sodium and sulfur, it

could also be used to adjust the sodium and sulfur balance. However, the composition of the

precipitator ash varies from mill to mill and from time to time; ash purging is a difficult method

to control sodium and sulfur balance. In some cases, the mill may purge more ash than needed

and makeup has to be added to adjust the sulfur to sodium ratio (S/Na2).

A solution to this problem is to control the composition of the chemicals purged using an ash

treatment system. Since some mills are installing ash treatment systems to control Cl and K

levels [11], the use of an ash treatment system may bring economical benefits to the mills by

reducing makeup requirement. However, to use ash treatment processes to control sulfur and

sodium balance requires detailed information on the operating conditions of the process and its

influence on the composition of the purged stream, which is often not available.

1.4 Objectives

This research project aims at obtaining the necessary information to calculate sodium and sulfur

balances in the kraft recovery cycle. Attention will be given to mills using ash treatment systems,

since they can act as a purge point for sodium, sulfur, chloride and potassium. This work,

however, would require an evaluation of the effects of ash treatment and makeup practices over

time.

The objectives of this study are:

1) to develop a model that can predict the Na, S, Cl and K balances in the chemical

recovery process over time,

2) to evaluate the impact of ash treatment systems on the chemical balances,

allowing a better control of the sodium and sulfur balances.

5

2. Literature Survey

2.1 Introduction

The previous chapter highlights the need for proper concentrations of NaOH and Na2S in the

white liquor to produce pulp with desired characteristics. The challenge in this task lies in two

facts: first, there is no consensus on the proper amounts of NaOH and Na2S, or in other words on

the sulfidity of the white liquor. Here is important to mention that sulfidity is the parameter used

in kraft mills to monitor the balance of NaOH and Na2S in the liquor. Sulfidity is calculated by

dividing the weight of sodium sulfide (expressed in g/l on Na2O basis) by the weight of sodium

hydroxide plus sodium sulfide (also expressed in g/l on Na2O basis) multiplied by 100.

Second, the sulfidity varies from mill to mill and over time due to chemical losses. The proper

concentrations of NaOH and Na2S are then adjusted by adding makeup chemicals to the recovery

area. However, the makeup addition is not a standardized procedure, but varies significantly

from mill to mill. As an example, process supervisors may adopt a strategy based on a shift or

daily basis, the chemicals used may change depending on cost and availability, which makes the

control of sulfidity a difficult challenge.

This chapter provides background on the difficulties of maintaining the proper Na and S balance.

In addition to that, the chapter considers the problem of accumulation of unwanted chemicals

such as Cl and K responsible for fouling and corrosion in mills. Then, a survey of pulp mill

modelling and simulation is presented, including the few studies dedicated to simulate the

balances of Na and S. The final analysis highlights the need for a tool to deal with chemical

balance problems.

2.2 Sodium and Sulfur Balance

Sodium and sulfur are the main chemicals used in the kraft pulping process. In order to make the

process economically feasible, the chemicals used in the cooking have to be recovered

afterwards. This is accomplished by the kraft chemical recovery. Traditionally the degree of

recovery with respect to sulfur and sodium in the process are as high as 97% for a standard

modern bleached kraft pulp mill [12, 13].

6

To keep the right ratio of sodium and sulfur in the mill, chemicals are added in the recovery

cycle as makeup to the losses that happen along the process. Chemical losses from the process

can occur through different streams: liquor spills, pulp, grits, dregs, white liquor used in

scrubbers, liquor swap with other mills, recovery boiler stack gas and lime kiln stack gas, etc.

[14]. The amount and type of makeup chemicals used in a mill depends on many factors: the

availability and cost, the sulfidity of the cooking liquor, the tolerance with regard to odor and

corrosion problems [15].

The “rule of thumb” was to add enough Na2SO4 makeup to maintain the sodium content of the

liquor. The excess sulfur thus introduced would leave the system, mainly as SO2 from the

recovery furnace [16, 17]. However, due to environmental regulations now restricting levels of

many chemical discharges, mills need to reduce effluent by recycling streams back to the process

[18, 19]. One example of stream is the saltcake produced at the ClO2 generators [20].

2.2.1 Sulfur Losses

Sodium and sulfur balance varies from mill to mill and over time primarily due to the fact that

sulfur, in the form of gaseous compounds (most H2S and SO2), is lost from the system

independently of the sodium loss [14]. Sulfur can leave the mill in the form of air emissions,

while sodium losses are usually associated with sulfur, both in liquor streams going out and in

solid wastes. The sulfur-containing gases are typically, the TRS (total reduced sulfur) gases and

the oxidized gases. The TRS gases are usually a mixture of hydrogen sulphide (H2S), methyl

mercaptan (CH3SH), dimethyl sulphide (CH3SCH3) and dimethyl disulphide (CH3S•SCH3). The

oxidized gases are sulfur dioxide (SO2) and sulfur trioxide (SO3) [2]. Usually, hydrogen sulphide

(H2S) and sulfur dioxide (SO2) are the two most important sulfur gases leaving the kraft mill

[21].

Under normal operating conditions TRS are generated in places where liquor is evaporated and

together with other gases form the NCG gases. There are four types of NCG: concentrated NCG

(CNCG), dilute NCG (DNCG), chip bin gases, and stripper gases [1]. CNCG contains 10% TRS,

77% nitrogen, 9% oxygen and 4% water vapour. It is generated in the digester, evaporators and

strong liquor storage tanks. DNCG has less than 0.1% TRS. DNCG are formed in the

brownstock washers, storage tanks, mud filters, and causticizers. Chip bin gases are similar to

DNCG, but also contain turpentine vapour, which makes necessary a treatment prior mixing with

7

DNCG. The stripper gases are typically composed of 50% water vapour and 50% methanol, and

minor portions of TRS, ethanol and turpentine.

Due to the high sulfur content, NCG needs to be completely oxidized before it can be released to

the atmosphere. To burn NCG, three conditions must be met: temperature of 760°C, residence

time of 0.5 s and excess oxygen (3 to 4%) [1]. In a pulp mill these conditions can be found in

four places: lime kiln, power boiler, recovery boiler, and dedicated incinerators. Burning NCG in

the lime kiln is interesting because the lime mud can absorb SO2; however there is a limit [22]. If

the kiln fuel has also high sulfur content, some sulfur gases can be emitted. Power boilers and

recovery boilers can burn NCG; however, this option must be carefully considered to avoid high

emissions [23-25].

Several authors report data regarding sulfur losses as gases from kraft mills. Bergstrom and

Trobeck [26] determined the loss of volatile sulfur compounds associated with digestion and

blow and showed that the total loss is equivalent to 1 kg of sulfur per ton of pulp. By determining

the ratio of sulfur to total solids in the liquor prior to and following evaporation, they also

estimated the sulfur loss in the evaporation plant to be about 10 kg of sulfur per ton of pulp.

Blackwell and Lincoln [14] performed calculations and estimated the sulfur loss from lime kiln

flue gas around 0.02 kg of sulfur per ton of pulp. Grace and Malcolm [3] provided an average

value of 0.47 kg of sulfur lost per ton of pulp in recovery boiler flue gases by a compilation of

data from different sources. Valeur et al. [27] reported the TRS emissions from black liquor heat

treatment and superconcentrators to be 5.56 kg of S / odt in a case study. Figure 2.1 shows the

various points of sodium and sulfur losses in the kraft process.

Although the sulfur losses are measured and known in some mills, the task of keeping the right

concentration of sodium and sulfur in the mill remains difficult. The amount of chemicals to be

added in the recovery cycle as makeup changes over time depending on many factors and a

material balance for the whole recovery cycle would be required to allow for proper makeup.

8

Figure 2.1 – Sodium and Sulfur Losses from the Chemical Recovery Cycle [3]

9

2.2.2 Sodium and Sulfur Makeup

To determine the amount of chemicals needed, a material balance is performed for the process.

An approximate balance considers that the total sodium makeup is equal to the sodium loss, once

a steady state has been established [28]. Thus,

Amount of sodium makeup = kg of sodium lost from black liquor system at washers and

evaporators + kg of sodium lost from green liquor in causticizing

department + other losses

Likewise, the total sulfur makeup would be equal to the sulfur loss for a steady state condition

being reached, but in this case, the sulfur loss is made up of two portions: the loss in combination

with sodium at the various points of sodium loss and the loss in the form of sulfur gases.

Amount of sulfur makeup = kg of sulfur lost as liquor + kg of sulfur lost as gas emissions

Based on the calculated sulfur loss, and in the desired sulfur content in the liquors, the chemical

makeup can be done by the mill personnel. Sodium sulfate has been a preferred makeup

chemical for many years, however, due to the changes in the process resulted from

environmental regulations, other chemicals have also been used.

The most common case is the by-product of the chlorine dioxide (ClO2) generators. The saltcake

generated by the ClO2 plant can contain sodium bisulfate (NaHSO4) or sodium sulfate (Na2SO4)

as main chemical, depending on the type of process used. The reuse of the saltcake becomes an

alternative to reduce the costs associated with purchase of Na2SO4 and a solution to a problem of

solid waste disposal [29].

2.2.3 - Chlorine Dioxide Effluent

The chlorine dioxide-based bleaching process, the so-called Elemental Chlorine-Free (ECF)

technology, dominates the production of bleached chemical pulps [30]. Wood pulp bleaching is

the prime use of chlorine dioxide because it is a unique selective oxidizer for lignin. Unlike other

oxidizing agents, chlorine dioxide does not attack cellulose, and thus, its use preserves the

mechanical properties of the bleached pulp. Chlorine dioxide functions via oxidation rather than

chlorination; hence it avoids the formation of chlorinated organic compounds.

10

ClO2 is always generated on-site because of its unstable nature and risk of rapid decomposition

[31]. In all processes, ClO2 is produced from acid solutions of either sodium chlorite or sodium

chlorate. The chlorate-based processes are preferred due to the lower cost and higher stability of

sodium chlorate compared to sodium chlorite. There are several processes used to generate

chlorine dioxide. Some of them are listed in Table 2.1.

Table 2.1 – Summary of Chlorate-based ClO2 Generation Processes

Main Reaction Involved Process

• SO2 Processes:

2NaClO3 + SO2 + H2SO4 → 2ClO2 + 2NaHSO4

Mathieson

and Holst

• Methanol Processes:

6NaClO3 + CH3OH + 4H2SO4 → 6ClO2 + CO2 + 2Na3H(SO4)2 + 5H2O

Solvay,R8,

R9 , R10

• NaCl Processes:

2NaClO3 + 2NaCl + 4H2SO4 → 2ClO2 + Cl2 + 4NaHSO4 + 2H2O

SVP, R2,

R3 and R7

• HCl Processes:

2NaClO3 + 4HCl → 2ClO2 + Cl2 + 2NaCl + 2H2O

R4, R5,

R6, Lurgi

• Hydrogen Peroxide Processes:

2NaClO3 + H2O2 + H2SO4 → 2ClO2 + O2 + Na2SO4 + 2H2O

SVP-HP

SVP-HA

The salt cake formed by these processes is either sodium acid sulfate (NaHSO4) or sodium

sesquisulfate [Na3H(SO4)2]. Most of the saltcake is reused in kraft mills as makeup chemical. In

recent years, however, mills are facing problems due to the high sulfur content of the by-product.

Furthermore, due to its acidic nature, the saltcake must be neutralized before it can be disposed

of, making the disposal process costly [32].

To avoid disposal, the best option is to use the saltcake generated, which can lead to excess

sulfur in the process depending on the amount recycled to the chemical recovery. The challenge

is that the composition and the amount produced vary widely depending on the process used as

shown in Table 2.2. Increasing sulfur recycle increases sulfur gas emissions and liquor corrosion.

11

Table 2.2 – Amount of By-products from ClO2 Generators [33]

ClO2 Production

Process

NaCl

(t/ tClO2)

Cl2

(t/ tClO2)

Na2SO4

(t/ tClO2)

H2SO4

(t/ tClO2)

Mathieson 0 0 1.42 1.72

Solvay 0 0 1.31 1.60

R2 0 0.57 2.27 3.28

R3 / SVP 0 0.66 2.27 0

R3H / SVP (HCl) 0 0.66 1.36 0

R5 / R6 / Lurgi 0.95 0.90 0 0

R8 0 0 1.36 0.11

R10 0 0 1.06 0

One way to avoid the reuse of the saltcake and the Na/S imbalance generated is to reduce

saltcake byproduct in the ClO2 production process. Many studies are devoted to improve the

ClO2 generation, however only recently some researchers have focused on the possibility of

saltcake free process [32, 34].

In order to manage properly the excess sulfur mills produce chemicals on-site to balance any

excess of sodium or sulfur. The focus here involves two alternatives. One developed at FP

Innovations makes use of a resin to produce NaOH and H2SO4 from saltcake produced by the

ClO2 generators [35]. The other developed by Kvaerner Chemetics involves the production of

sulfuric acid using the NCG collected from the different points in the mills [27].

Finally the most common practice to deal with excess chemicals is to dispose off the precipitator

ash and correct any imbalance by adding makeup for Na or S [11]. This procedure may be costly

due to the increasing cost of chemicals, but with the help of a precise material balance it may

provide a way not only to control Cl and K contents in the recovery area but also reduce Na and

S imbalances.

2.3 Chlorine and Potassium Balance

Potassium enters the mill almost entirely with the wood which makes it reasonably easy to

determine. The typical content measured in as-fired black liquor is 1 to 2 wt% on dry solids,

increasing as the closure of the process increases [36 – 38]. The concentration of Cl in black

12

liquor is usually between 0.5 and 0.7 wt% on dry solids, but may exceed 2 wt% for mills where

wood is floated in sea water [36 - 38]. Other sources of Cl are makeup chemicals and effluent of

the bleach plant that is sent to the recovery cycle.

Because of the negative effect of Cl and K, their concentrations are measured in many different

streams around the recovery cycle. The problem is that many units are employed to report Cl and

K concentrations, and these units vary significantly from one mill to another. In some mills,

grams of NaCl per liter is used to measure Cl content in white liquor; in others, g/L of Cl, parts

per million (ppm) of Cl, ppm of NaCl, wt% of NaCl, fractional units such as Cl/Na and

Cl/(Na+K) ratio are also used. Such a wide variety of units makes comparing Cl and K

concentrations from different mills very difficult, and to assess whether or not Cl and K will be

an issue in a particular case [36].

2.3.1 Enrichment Factor

In order to allow different mills to be evaluated, it was established that the most appropriate way

to express the Cl and K concentrations in both liquors (liquid) and dust/ deposits (solids) is to use

the molar fractional units, i.e. mol% Cl/(Na+K) and mol% K/(Na+K) [36]. The reason behind

this choice is that these units consider the two principal cations (Na+K), as an internal standard,

which makes this ratio independent of other parameters that vary significantly such as liquor

density, solids concentration, inorganic to organic ratio, reduction efficiency, etc.

Furthermore this unit can be easily obtained from the analysis of liquors as well as deposits and

allow monitoring Cl and K contents among liquors, smelt, deposits and dust within a mill.

Results from different mills indicated that the Cl content of the dust, measured using the

fractional units [Cl/ (Na+K)]ash, is higher when compared with the Cl content in the black liquor.

Same argument can be made for potassium.

The explanation for the high Cl and K contents in precipitator dust is that Cl and K compounds

are more volatile than sodium compounds when the black liquor is burned in the recovery boiler.

The volatilized compounds are carried up to the upper part of the recovery boiler by the

combustion gases. As the gases cool down the compounds form fine solid particles that are

captured by the electrostatic precipitator. The Cl and K compounds are therefore more enriched

in the precipitator ash compared to the black liquor. Mill studies indicated that an average

13

enrichment factor for Cl (EFCl) is around 2.5, while for K (EFK) is around 1.5; but can change

depending on the operating conditions of the boiler [36]. The enrichment factor for Cl is given

by equation 2.1 and for K in equation 2.2:

BLSolidsKNaCl

ashKNaCl

ClEF

(2.1)

BLSolidsKNaK

ashKNaK

KEF

(2.2)

The distribution of Cl and K compounds between smelt and gas phase depends on the operating

conditions in the recovery boiler. Char bed temperature, combustion air flow rate, black liquor

composition, SO2 concentration in the gas phase and inorganic release from black liquor droplets

are believed to play a major role in the Cl and K enrichments. Since Cl and K lower the melting

temperature range of the particulate matter in the upper boiler region, making it stickier,

understanding how the boiler parameters affect the enrichment factor would be important in

helping to manage possible plugging problems in the recovery boiler.

2.3.2 Factors Affecting the Enrichment Factors

Enrichment factors of Cl and K have been a topic of research work for many years. The reason

for this interest is that changes in operating parameters in the boiler and on liquor characteristics

may affect the enrichment and furthermore the fouling and plugging. For example, the presence

of ash treatment systems may reduce the Cl input in the as-fired black liquor. This may lead to

different enrichment factors over time.

Among different studies, the most common variables were the char bed temperature, the black

liquor composition and the concentration of SO2 in the flue gas. Other parameters studied

included the combustion air flow rate and distribution and the release of inorganic compounds

during “in flight” combustion of black liquor droplets.

14

2.3.2.1 Lower Bed Temperature

The effect of char bed temperature was studied by Reis et al. and Janka et al. [39, 40]. They

show that a hot bed increases the amount of ash produced, but decreases the Cl and K

enrichments. Although the amount of each inorganic compound (Na, K and Cl) volatilized

increases with increasing temperature, the enrichment of Cl and K is reduced because of the

greater amount of sodium released, which causes a “dilution effect” on Cl and K.

The dilution of Cl and K becomes significant when the reduction of sodium carbonate by the

char present in the smelt start to occur as indicated in the reaction 2.3 [41].

Na2CO3(c) + 2C(c) → 2Na(g) + 3CO(g) (2.3)

This reaction would lead to the release of metallic sodium and carbon monoxide, the metallic

sodium would then react and form sodium sulfate and sodium carbonate that are found in the

ash. At lower temperatures, the enrichment factors would also increase, due to lower amounts of

sodium present in the ash.

2.3.2.2 Black Liquor Composition

The impact of different liquors on dust formation was studied closely by Hupa et al. [42]. In their

work, samples of different mills were evaluated regarding black liquor composition and dust

composition. Although the basic mechanisms for Cl, K and Na release are the same for different

liquors, it was found that the strength of different sub-mechanisms may significantly differ from

one liquor to another. These liquor-specific properties are very important in the final dust

composition, but their influence on the reactions is not clear at the present [43].

According to his study, Hupa et al. concluded that reducing potassium by half in the liquor

would not affect significantly the melting point of the deposits in the upper boiler, which affects

the plugging process. For the liquors Hupa studied, reducing Cl and K together by half also does

not seem to have any impact [42].

15

2.3.2.3 Concentration of SO2 in the Flue Gases

The effect of SO2 on Cl and K enrichment factors was studied by many authors. Frederick et al.

[44] conducted laboratory experiments using a laminar entrained flow reactor with dried liquor

particles (~100 μm). They found that Cl enrichment factor decreased with increasing temperature

at all SO2 concentrations studied. A higher SO2 concentration results in a lower chloride

enrichment factor at a given particle residence time and reactor temperature. On the other hand,

K enrichment factor were nearly constant for different SO2 concentrations used.

The decrease in Cl enrichment factor is a result of the “sulfation” of chlorides present in the dust

formed in the reactor, according to reaction 2.4 [45 - 48]:

2NaCl (g) + SO2(g) + ½O2(g) + H2O(g) → Na2SO4(s) + 2HCl(g) (2.4)

As a result of this reaction, gaseous hydrogen chloride is formed and exits the process as stack

emission. The formation of HCl is limited by the presence of SO2 in the flue gases, which in

turn, is influenced by the sulfur/sodium ratio or sulfidity of the black liquor burned in the boiler.

Temperature also plays an important role in this reaction due to the release of sulfur and chloride

compounds in the gas phase [49].

2.3.2.4 Amount of Combustion Air

The overall process of black liquor combustion requires the addition of heat and air (oxygen) to

the black liquor droplets. The heat is needed to dry and pyrolize the liquor, while the air is

required to oxidize the organic material. When the fraction of air delivered to the lower furnace

is less than the chemically stoichiometric amount, only a portion of the liquor can burn and

release heat [50]. The proper amount of combustion air depends on many factors, among them

the degree of mixing achieved and the reduction efficiency of the boiler.

If the mixing is poor, less liquor combustion would happen and temperature would reduce and

the char content in smelt would increase. Thus, more combustion air is needed to maintain

proper burning conditions. However if too much air is introduced, it would lower the reduction

efficiency of the boiler, resulting in a liquor that would not meet the sulfidity required for

pulping.

16

Although the amount of combustion air is believed to affect the enrichment factors of Cl and K,

no systematic study has been done to evaluate this effect. Reis et al., Janka et al. and Frederick et

al. [39, 40, 44 and 51] mentioned that the amount of combustion air is critical to the enrichment

factors, but how the combustion air amount affects the enrichment factors was not clearly shown.

2.3.2.5 Release of Inorganic Compounds

Recovery boiler precipitator dust is assumed to form as a combination of two fractions; one is the

vaporized compounds coming from the char bed, the other fraction originates from the inorganic

chemicals released from burning droplets of black liquor sprayed into the recovery boiler [40,

52]. This additional release from the droplets during their “in-flight” combustion is added to the

saturated vapors from the lower furnace to produce the total dust compounds. The amount

released depends on many factors being the exact amounts not known.

Efforts to understand the release of inorganics in the recovery furnace are been done by many

research groups [53-58]. Existing models of Na and S release are based on empirical correlations

developed by laboratorial measurements. Because sulfur is predominantly released during

pyrolysis phase, the laboratory experiments can yield S release data which are easier to measure

and apply to the recovery boiler. However for Na, K and Cl the release happen during the

combustion phase and their release depends not only on temperature history of the black liquor

particle, but also on the composition of the gas phase around the particle [59]. This is a more

difficult scenario to be reproduced and explains the large discrepancy in release values reported

in the literature.

According to some studies [60, 61], the main reactions leading to S release are two reactions of

sulfide; namely (i) with lignin methoxyl groups yielding methyl mercaptan and dimethyl

mercaptan, and (ii) with CO2 and H2O yielding H2S. In presence of O2 this sulfur compounds

would react and produce SO2.

Other studies [41, 42 and 62] found that a major part of the sodium in the black liquor could be

volatilized at high temperatures (700-900°C) in an inert atmosphere subsequent to the pyrolysis

phase. Their experiments indicated that Na2CO3 formed during primary pyrolysis is reduced by

char carbon to yield volatile elemental Na as indicated in reaction 2.5:

Na2CO3(c) + 2C(c) ↔ 2Na(g) + 3CO(g) (2.5)

17

where the suffix (c) denotes a condensed phase (solid or liquid) and (g) for gas phase. At the

combustion temperatures and in the presence of O2 and H2O, the Na vapor is rapidly converted

to NaOH as in reaction 2.6:

2Na(g) + 1/2O2(g) + H2O(g) ↔ 2NaOH(g) (2.6)

Potassium is released through analogous reactions from K2CO3. The hydroxide vapors then react

with the SO2 as indicated in reaction 2.7:

2NaOH(g) + SO2(g) + 1/2O2(g) → Na2SO4(g) + H2O (2.7)

Finally the chlorine is present in black liquor mainly in the form of chloride ion. Thus, the

accepted dominant mechanism of Cl release is direct vaporization of NaCl and KCl as in

equation 2.8:

NaCl(c) ↔ NaCl (g) (2.8)

Considering the wide range of values that the variables discussed such as char bed temperature,

liquor composition and combustion air flow can assume and how they interact with each other in

the boiler, it would be helpful to create a model to simulate the ash formation process and

evaluate the impact of each parameter on the Cl and K enrichment factors. This task would

require the use software capable of dealing with the thermodynamic of the systems found inside

the recovery boilers.

2.3.3 Ash Treatment and the Chlorine and Potassium Balance

In order to control K and Cl levels, a purge point needs to be provided. This is accomplished by

purging the electrostatic precipitator ash from the recovery boiler, as discussed previously. While

the purging is widely employed, and requires little capital investment, it may not be the best

solution due to the losses of sodium and sulfur compounds present in the ash.

Several processes are commercially available for treating precipitator ash, to remove selectively

the Cl and K [11]. A common feature in all of these processes is the need to mix the ash with

water to produce a nearly saturated solution, or saturated ash-water slurry. As a result, Cl and K

compounds would largely dissolve in the water and could be discarded, while other chemicals

18

containing sodium and sulfur would remain in the solid phase being recycled to the recovery

area.

Thus, in order to simulate the chlorine and potassium balances for mills using ash treatment

processes, it is necessary to obtain the operating conditions of the process and its influence on

the composition of the purged stream. Due to the complex thermodynamic calculations involved

in this task, ash treatment blocks are unavailable in any pulp and paper process simulator. To

overcome this problem, a databank containing information regarding the ash treatment

performance for different ash compositions, ash-to-water ratios and temperatures was built using

the OLI software [63].

The OLI Software [64] is a commercial package used to calculate thermodynamic properties of

aqueous systems. It is generally used for predicting complex chemical and electrochemical

phenomena in aqueous and mixed solvent environments. The predictive thermodynamic model

in OLI is based on published experimental data for many chemical mixtures over a wide range of

temperature (-50 to 300°C), pressures (0 - 1500 bar), and ionic strength (0 - 30 molal).

Unfortunately, the information gathered from OLI can not be readily incorporated into most pulp

and paper simulators. Thus, an ash treatment block should be developed in order to implement

the ash treatment system into a model for the whole kraft chemical recovery. The block was

developed using a neural network module as described later.

2.4 Process Simulation in the Pulp and Paper Industry

In the pulp and paper industry simulation can be used in four different areas: forest sector,

corporate level, mill and process. In this thesis, the main focus is directed towards mill and

process models. Usually the models can be used for the following purposes: i) mill design with

the intent to select equipment, plan processes capacity, and storage; ii) optimization of specific

areas of the mill regarding preferable chemical concentrations, energy usage, and rate of

production; iii) overall mill simulation to optimize scheduling of processes, utilization of storage,

changes in species being pulped and accumulation of different chemicals.

In all these applications, the level of detail in the simulation models varies significantly. Process

models whose objective is to describe the behaviour of variables inside specific areas of the mill

19

require more thorough and detailed models; on the other hand, mill models that are used to

explain the overall behaviour of the process employ much simpler models. Thus, in the planning

of the simulation studies, the purpose of the simulation and the requirements for the results must

be considered beforehand to make sure that suitable models are being used and the data required

for the model validation is available.

Models for mill simulation should not be designed as a compilation of models employed to study

specific processes within the mill [65]. Because of process models usually present excessive

amount of equations and variables that would be useless for an overall simple balance. As a

result, they increase the margin for error and make a complete balance checking difficult, since

some variables may not be measured by the mills [28].

A vast number of papers have been published concerning the modelling and simulation of the

pulp and paper processes. Johnsson [66] and Wisnewski [67] developed models to simulate the

kraft cooking process. Norden and Jarvelainen [68] modeled the pulp washing process by drum

filters, while Turner et al. [69] and Wasik et al. [70] studied the countercurrent diffusion

washing. Kinetic models of pulp bleaching have been developed by Edwards et al. [71] and a

simulation model developed by Rouda et al. [72]. Bremford et al. [73-75] constructed models for

the black liquor evaporation plants. Galtung [76] and Costa [77] have developed mathematical

models to simulate recovery boilers. Modelling and simulation of causticizing plants have been

built by Beckwith [78] and Swanda et al. [79]. Lime kiln models were presented by Edwards et

al. [80] and Georgallis et al. [81].

On the other hand, whole mill modeling is more difficult due to the fact that two identical pulp

and paper mills do not exist. This forces the programmer to change the available models so that

they can be used for a particular case in question. How successful the model is will depend on

the fitting of calculated parameters to the actual mill measurement. One example involving an

economic study around a kraft cycle is presented by Venkatesh et al. [82]. Another study by

Gunseor and Rushton [83] evaluated alternatives to reduce energy requirements. Jordan and

Bryant [38] studied Cl and K concentrations in the liquor around the kraft cycle. With respect to

the simulation of the kraft mill chemical balance, which is subject of interest in this study, only a

handful of studies were developed and published. Due to the different perspectives adopted by

20

the authors, very different conclusions were drawn from their results as it is shown in the next

section.

2.4.1 Simulation of Kraft Mill Chemical Balances

The effects of sodium and sulfur balance in the operating conditions and performance of the

Kraft mill have been studied for many years [84-88]. Usually, a chemical imbalance was severe

when external sources other than saltcake (Na2SO4) or caustic (NaOH) were used as makeup

[89]. Among the parameters of interest are pulp characteristics, bleaching chemicals usage, dead

load concentration, steam production, reduction efficiency, causticizing efficiency, lime kiln fuel

consumption, etc.

According to Rydholm [90], pulping softwood at a higher sulfidity resulted in pulp with lower

lignin content, which reduced bleaching chemicals consumption. Other researchers have found

that high sulfidity might increase the amount of dead load in the form of Na2SO4 in the chemical

recovery area [91]. This can lead to the formation of burkeite (2Na2SO4*Na2CO3) or dicarbonate

(2Na2CO3* Na2SO4) scaling in the evaporator plant.

From the point of view of steam production and reduction efficiency, mills operating at lower

sulfidity values seem to have advantage to the ones at higher sulfidity as suggested by Boyle and

Treiber [92]. As a result of the pulping process, most of the sulfur fed into the recovery boiler is

present as Na2SO4 and must be reduced to Na2S. This process takes place in the recovery boiler

as indicated by the reaction 2.9:

Na2SO4(l) → Na2S(l) + 2O2(g) (2.9)

This is an endothermic reaction. The energy required for it to occur is taken from the combustion

of black liquor fed into the boiler. Clearly, as sulfur content in the liquor decreases, less energy is

consumed by the reaction and greater proportion of the heat is available for steam generation.

On the other hand, at a low sulfidity the smelt leaving the recovery boiler contains more sodium

carbonate which must be converted to sodium hydroxide in the causticizing plant to complete the

chemical recovery process as indicated by the reaction 2.10:

Na2CO3(l) + Ca(OH)2(s) ↔ 2NaOH(l) + CaCO3(s) (2.10)

21

Most of the lime required is produced by a kiln through calcination of limestone:

CaCO3(s) → CaO(s) + CO2(g) (2.11)

The increased amount of Na2CO3 results in an increase in kiln load and hence in kiln fuel

requirement. Lime losses would also be higher since more material is being handled [93]. Thus

the increased costs in the causticizing plant may offset the gains made in the steam generation at

the recovery boiler as discussed by Baldus and Edwards [94].

Thus the choice of sulfidity levels is done based on economic aspects, which makes the

definition of a single targeted value for sodium and sulfur content not realistic. The parameters

considered in the decisions usually involve the cost of energy, wood, lime kiln fuel, makeup

chemicals and bleaching chemicals. This cost analysis led to very distinct approaches to the

sulfidity levels in different parts of the world. As an example, Boyle and Treiber [92] suggest

that 25% sulfidity (AA basis) would be a favourable operating condition for mills in North

America; while Sondell [95] claim that the optimum sulfidity would be 40% for Scandinavian

mills. Thus in order to analyse the different aspects involved in the sodium and sulfur balance it

is important to have a model that can evaluate the whole mill before any sulfidity level can be

suggested.

2.4.2 Pulp and Paper Simulators

Usually models used in the pulp and paper industry are formulated in a modular way starting

from the process layout [96]. This is beneficial because standard main programs can be utilized.

The most popular modular program packages include WinGEMS, GEMCS and MASSBAL/

CADSIM.

WinGEMS is perhaps the most well-known program package with respect to the pulp and paper

industry. It was developed in the early 1970’s at Idaho University. WinGEMS has one of the

most extensive module libraries for pulp and paper processes and has been extensively used for

material and energy balance calculations [71, 72, 78, 80, 82 and 83].

GEMCS was originally developed at McMaster University and has been used for different

applications. Extensive work was performed by Boyle and Treiber [92, 97] in building an entire

22

kraft mill model using this simulator. WinGEMS and GEMCS are steady-state simulators and

employ a sequential method of solution to calculate the output variables of the models.

MASSBAL MKII was originally developed by SACDA Inc. in cooperation with Energy Mines

and Resources Canada as a generalized simultaneous modular process simulation package for

calculating the steady-state heat and mass balances for industrial processes [98]. The system has

been designed for the modeling of water-based processes such as those found in pulp and paper

and mineral industries. In the 1990’s, MASSBAL MKII became CADSIM after the copyrights of

the program were acquired by Aurel Systems. Further developments permitted CADSIM to

generate models for different processes. As the software continued to evolve, Aurel Systems

developed and released CADSIM Plus [99].

The software now uses a sequential solver for performing dynamic simulations. This means that

each unit and stream is executed in sequence, through an iterative “step in time”. The default

sequence that CADSIM Plus uses when executing the units and streams is usually the order in

which they were created. However, if needed, the software allows for the programmer to interact

with the model and change this sequence. Usually this may happen if modules are not executed

in order of flow, what can create artificial time lags in the model and affect the simulation

response and stability.

In each simulation iteration step in time, the following calculation steps are followed: (i) all

specifications are calculated, except network specifications, (ii) each unit module is calculated in

the order that it appears in the drawing, with the exception of a unit module that has a network

flow [99]. In this case every unit module in that particular network of process flows is iterated in

a loop until a convergence is reached. Once converged, the execution passes on to the next unit

module.

It is important to mention that there all previous work published involving the chemical balance

of kraft mills was developed using steady-state simulators. Since the objectives of this study is to

determine the impact of the ash treatment and the various makeup sources on the chemical

balance, it is necessary to evaluate these changes as they occur over time. In order to achieve

these goals, a dynamic simulation has to be done, which makes the CADSIM Plus package more

suitable to be used in this project. However, the software will need to be used together with other

packages to be able to perform some specific calculations not originally present in its library

23

such as the enrichment factor calculation which is calculated using FactSage simulator described

in Appendix A.

2.5 Summary

As explained previously, one of the difficulties with keeping the proper sulfidity (NaOH and

Na2S concentrations) lies in the fact that the recovery process has many losses. In some of them

sodium is associated with sulfur while in others sulfur independently leaves the process in gas

phase. As a result the balances of Na and S will differ from mill to mill and over time.

Simultaneously, the makeup available for mills to counterbalance the losses is also different in

composition and amount. Therefore, each mill would require an individual evaluation to allow

for a proper makeup strategy.

In addition to the Na and S balance problems, the accumulation of unwanted chemicals such as

Cl and K has become a growing concern to the mills. Since mill processes are being tightened up

to reduce losses and meet environmental regulations, non-processes elements concentrations are

increasing to levels where they become harmful to equipment and processes. In the case of Cl

and K that are soluble in the mill liquors the best option is to use the electrostatic precipitator

ash. Since the ash is enriched in Cl and K and is composed of salts containing Na and S this

stream could be not only a point to purge Cl and K but also to adjust the Na and S

concentrations. This would be possible if the ash is treated to obtain the maximum removal of Cl

and K, with the needed amount of Na and S recycled back to the process.

Finally, evaluating all work developed around the simulation in the pulp and paper industry, it is

clear that most works focus on the understanding and optimization of individual processes. The

few published works on the whole mill involves separate balances of Na and S or Cl and K.

What is required for a deeper understanding of the process is a model to account for the balance

of Na, S, Cl and K together with the alternative to use the ash treatment system as a control point

for Na and S balance and Cl and K purge. In addition to the 4 elements balance, the modeling of

an ash treatment system and the capability of the model to evaluate its impact over time

(dynamic simulation) would make this project a unique contribution to the pulp and paper

industry.

24

3. Methodology

The Methodology section of this work is divided into two parts: the first part describes the

simulation work needed to build the model for a kraft mill chemical balance. Two of the most

common pulp and paper simulators are presented and compared in order to decide which one

would suit better for the task at hand. Once the software is chosen, the simulator programming is

then briefly described and the necessary changes and improvements on the simulator are

presented. Finally the process of model validation is discussed.

The second part involves the simulation of the Cl and K enrichment factor, which is an important

parameter in establishing the balances of Cl and K in the kraft mill liquor cycle. A series of

laboratorial experiments are also performed to validate the model findings as presented in the

later sections of this chapter.

3.1 Evaluation of Pulp and Paper Simulators

There are several features that separate pulp and paper simulators from those used in other

industries (i.e. the oil and chemical industries where simulators were initially developed). The

most important is that they must include solids as well as liquids and vapours in the streams.

WinGEMS and CADSIM are the most used simulators in the pulp and paper industry.

To calculate the mass and energy balances using stream-oriented simulator such as WinGEMS

and CADSIM, the process flowsheet must be replaced by an equivalent simulation block

diagram. The blocks represent the fundamental unit operations occurring at each step in the

process. For example in a paper machine simulation, the primary unit operations are mostly

stream mixing, stream splitting, and pulp dilution.

The translation from the process flowsheet to the block diagram in the simulators requires a clear

understanding of what is, in fact, occurring throughout the entire process. This interpretation of

the process is a critical step in the simulation. Errors at this point mean that the computed mass

and energy balances will be invalid.

After completion of the block diagram, each block and stream is given a unique number for

identification purposes within the simulation. It is generally good practice to number

25

sequentially, although alternate numbering schemes are sometimes appropriate. As an example it

is shown below two tests using these simulators, one involves the simulation of an evaporation

plant and the other a recovery boiler based on the blocks available in WinGEMS and CADSIM.

The evaporation plant simulated consists of 6 effects and 7 evaporator bodies (numbered 1A, 1B

and 2 through 6 respectively). Steam is supplied to evaporators 1A and 1B in parallel and the

weak black liquor feed is split between the 5th and 6th effects. The flow is counter-current with

the vapor liberated in the 1st effect used as the heating medium in the 2nd and so on. The clean

condensate is collected and returned to the boiler while the condensed vapor is collected and

either reused or treated as effluent. The strong black liquor is further concentrated and burned in

the recovery boiler. The process conditions are shown in Table 3.1 and are based on data

provided by Bergstrom [100].

Table 3.1- Input Data for Multiple-Effect Evaporator System

Input Data Parameter Value

Steam Pressure 31.5 psig Steam Flow 51.7 klb/h

Weak B.L. Flow (x1000) 333 lb/hr Weak B.L. Solids 13.93%

Evaporator Area (1A and 1B) 4400 ft2 Evaporator Area (2 to 6) 8800 ft2

6th Effect Vacuum 26 in. Hg

The block diagram representing the problem is shown in Figures 3.1 for WinGEMS simulation

and 3.2 for CADSIM simulation.

Table 3.2 shows the results for the simulations using WinGEMS and CADSIM, as well as the

values calculated by Bergstrom for this problem. The heat transfer coefficients were adjusted to

match evaporator capacity, economy, and strong black liquor solids values. As it is shown the

results are in good agreement between WinGEMS and CADSIM as well as the calculated values

by Bergstrom.

26

Figure 3.1 – Block Diagram for Six-Effect Evaporator Process Simulated in WinGEMS

27

Figure 3.2 – Block Diagram for Six-Effect Evaporator Process Simulated in CADSIM

28

Table 3.2 - Comparison of Results of the Simulation of a Multiple-Effect Evaporator Problem

WinGEMS CADSIM Bergstrom [100] Capacity, lb/hr 244,000 243,980 243,840 Economy, lb/lb 4.76 4.8 4.75 1A U, Btu/ft2 hr °F 187 177 174 1B U, Btu/ft2 hr °F 246 235 242 2 U, Btu/ft2 hr °F 423 395 392 3 U, Btu/ft2 hr °F 391 388 386 4 U, Btu/ft2 hr °F 313 314 316 5 U, Btu/ft2 hr °F 233 236 240 6 U, Btu/ft2 hr °F 187 187 190 1A solids out, % 51.4 51.2 51.1 1B solids out, % 42.3 42.3 42.2 2nd solids out, % 34 34.1 34.6 3rd solids out, % 25.7 25.9 26 4th solids out, % 21 21.1 21.2 5th solids out, % 17.7 17.8 17.8 6th solids out, % 18.8 18.9 19.1

The second test involved the simulation of a recovery boiler. The boiler considered is a bi-drum

model firing 75000 kg/h of black liquor at 70% dry solids and 115°C. Air for the liquor

combustion is provided at 150°C in three levels with 10% excess for the amount of liquor burnt.

Under normal operating conditions, the boiler would be able to achieve 95% reduction efficiency

generating flue gases at 175°C in the stack and smelt at 720°C in the spouts leaving the boiler. It

is assumed 3% heat losses and 10000 ppmv CO at stack gas. The remaining useful data needed

to calculate the balances at given process conditions are shown in Table 3.3 and are based on

data provided by Empie [101].

Table 3.3 - Input Data for Recovery Boiler Balance Calculation

Input Black Liquor Composition (%) Parameters Value Parameters Value

Na 19 CpAir (kJ/kg°C) 1.00 ΔHWater (kJ/kg) 2554 C 39 CpSmelt (kJ/kg°C) 1.67 ΔHRed. (kJ/kg Na2S) 12900 O 33 CpFlue Gas (kJ/kg°C) 1.00 ΔHSmelt (kJ/kg) 185 H 4 CpSteam (kJ/kg°C) 2.00 ΔHCO (kJ/kgCO) 10110 S 4 CpWater (kJ/kg°C) 4.18 Ref. Temp. (°C) 25 Cl 1 CpBLS (kJ/kg°C) 2.09

29

The block diagram used to simulate this problem is shown in Figures 3.3 for WinGEMS

simulation and 3.4 for CADSIM simulation.

Figure 3.3 - Recovery Boiler Model for Energy and Material Balance in WinGEMS

Table 3.4 shows the results for the simulations using WinGEMS and CADSIM.The results show

that CADSIM and WinGEMS are capable of simulating the process even using different model

layouts. Other equipment such as the lime kiln, digester, and clarifiers were also simulated and

the results of both simulators were in good agreement. Thus, choosing one program or the other

depends on the focus of the study. Usually CADSIM is used to study dynamic features, while

WinGEMS deals with steady-state simulation.

30

Figure 3.4 - Schematic Diagram of the Simulated Recovery Boiler

Table 3.4 - Comparison of Results of the Simulation of a Recovery Boiler Balance Problem

Calculations based on 100kg of Black Liquor Solids Input WinGEMS CADSIM Empie [101] Smelt (kg) 40.48 40.05 40.84 Na2S 7.79 6.77 9.26 Na2SO4 0.73 0.65 0.89 NaCl 1.35 1.03 1.65 Na2CO3 30.61 31.60 29.04 Flue Gas (kg) 628.46 626.64 626.86 CO 3.80 3.05 5.08 CO2 126.92 122.92 122.96 O2 7.19 9.17 13.69 N2 401.48 400.72 390.29 H2OComb. 36.0 H2OBL 89.07 90.78 42.86

Based on the models used to simulate the evaporation plant and the recovery boiler, WinGEMS

models can provide more detailed description of the equipment, but due to its programming

31

structure WinGEMS cannot perform dynamic simulations. On the other hand, CADSIM can

perform dynamic evaluation of the process, but cannot provide the same in depth analysis of the

process in each piece of equipment.

For the objectives of this study, CADSIM would be more interesting because it could provide

information regarding Cl and K accumulation over time including changes in the concentrations

for different ash treatment operating conditions or ash purging periods. Simultaneously the

impact on sulfidity levels could be studied as Na and S makeup requirement changes.

3.1.1 CADSIM Programming

In this work, CADSIM Plus is used as the simulation package to develop the material balance for

a generic kraft pulp mill. The program is a Windows application that allows the user to create a

process flowsheet drawing, add process information, and then run the process as a simulation

[99]. It is a modular program that uses a form of Dynamic Link Libraries (DLL) to perform

equipment, stream and physical properties calculations.

According to a general classification of simulation software, CADSIM Plus is a dynamic

platform that uses a sequential method of solution to calculate the unknown variables in a model.

In the sequential method, the process modules are calculated in a fixed sequence, otherwise a

calculation order must be specified. Starting with the first module, the specified streams and

estimated streams are used to calculate the outlet streams. The calculation then goes through the

flowsheet to the last module in the calculation order [96]. When this first pass, or iteration, is

completed, a check is made to see if selected estimated streams are close enough to the

calculated values. This is repeated until the convergence criterion is met.

The sequential method works in a similar way to hand calculations. Therefore, it has two main

advantages: it is fairly easy to follow and debug, and does not require much computer memory.

The main disadvantage is that it can require a large number of passes through the flowsheets, or

iterations, which can make the time to reach a solution much longer. Large problems may also

present difficulties in convergence [96].

In order for CADSIM Plus to build a simulation model, the program needs the process

description in three separate modes: Drawing, Specification and Simulation Mode. The Drawing

32

Mode allows CADSIM Plus to create CAD drawings of a process, and to create mathematical

models for the energy and material balance of the process. The Specification Mode is used to

specify process variables in the simulation model. The program passes through the process

drawing and requests information about how the process relationships are defined based on the

stream chemistry provided and the equipment units drawn [99].

When the drawing is fully specified, it can be run in the Simulation Mode. In this mode,

simulation results can be dynamically displayed on the drawing wherever they are specified. Any

drawing component can be fully evaluated during the simulation through dialog boxes that

contain current simulation information for that object. Some important parameters can be stored

in text files and analyzed in dedicated data mining software.

3.1.2 Modules Implementation into CADSIM

In this work, two changes were introduced in the original CADSIM Plus package used. The first

change is the implementation of an ash treatment block. As already discussed, since the ash

treatment is a relatively new process, no block is available for its simulation. The block was

therefore developed and validated to be used in a full kraft mill balance calculation. The

implementation of the ash treatment block is important because it allows the study of its impact

on the mill balance which is one of the objectives proposed in this work.

The second change was an improvement in the existing lime kiln block to allow the use of

alternative fuel sources. This part was an adjustment to the block to incorporate possible

presence of high sulfur fuels in the kiln.

3.1.2.1 Ash Treatment Block

In recent years, the use of ash treatment systems has increased in order to control Cl and K

levels. Thus, a model that performs the balances for Cl and K needs to simulate the ash treatment

system to provide an accurate picture of the mill balances. In order to simulate the ash treatment

system, a study was conducted using the OLI software [63]. As a result, a databank was built

containing information regarding the ash treatment performance for different ash compositions,

ash-to-water ratios, and temperatures. More details on the OLI software are given in Appendix

B.

33

Unfortunately, due to differences in software language and structure is not possible to

incorporate OLI into CADSIM to perform the calculations needed to simulate an ash treatment

system. Thus, a module was developed based on the databank obtained from OLI and on a neural

network block available in CADSIM that would act as a tool performing the calculations needed

for the ash treatment simulation.

According to the description on CADSIM, “the neural network block is an input-output mapping

module that learns to reproduce functional relationships among variables in a databank” [99]. It

is generally composed of a layer of input cells, 1 or 2 layers of hidden cells (called hidden A and

hidden B in Figure 3.5), and a layer of output cells. Every cell on hidden A is connected to the

input cells through the use of “weights”. Weights also appear between hidden B cells and hidden

A cells, and between output cells and hidden B cells, as shown in Figure 3.5 [99]. These weights

are numeric values that indicate the influence of each cell in the network.

In the process of designing a neural network some important parameters must be decided such as

the topology (number of cells and layers), the learning steps (how “weights” are changed) and

the stopping criteria (average error acceptable). These parameters vary based on the complexity

of the problem [102]. Given the lack of clear guidance in the literature concerning the selection

of the parameters, a trial-and-error procedure is usually followed [103]. Once the neural network

is designed a set of training patterns is submitted to test. These training patterns are just a set of

input values with their expected mapped output values.

Figure 3.5 – Schematic Diagram of a Neural Network [99]

34

Once the training patterns are fed into the module, the network adjusts its “weights” in response

to the different input patterns so that its actual response converges to the desired output response

[102]. The convergence is governed by the minimization of some error criteria between the

input-output data available and the output returned by the neural network. Once the training

patterns can be reproduced by the network within an acceptable error range, the resulting weight

vector is saved and the network is ready to be validated.

In the validation, the network with the adjusted weights receives a set of patterns different from

the ones used in the training step. Usually this set is part of the original databank where some

patterns were randomly extracted [102]. This set would evaluate the capability of the network to

generalize within the domain of data used.

3.1.2.2 Lime Kiln

The new lime kiln block being implemented into whole kraft mill model combines various unit

operation modules to best describe the calcination process. The block is divided in three main

parts: the burner, the heating/drying area and the calcination area. In the burner, the combustion

reaction of the different fuels with the primary air happens. It is required that flow and fuel

characteristics such as the elemental composition and heating value are given. The module is

usually set for a complete combustion of the available fuel.

In the heating/drying area the water vaporization takes place, being assumed that the water is

first driven off before the solid bed is heated. Finally, in the calcination area, the solids are

heated until the calcination reaction starts and calcium carbonate is converted to lime and CO2.

In the calcination area, it is also considered the reaction of SO2 generated from fuel combustion

with lime.

Compared to the old lime kiln block, the new kiln block includes sulphur absorption reaction,

product cooler and fuel characteristics (since the previous block could only be set to use natural

gas or oil). A schematic diagram of the old and new kiln models is seen in Figure 3.6.

35

Figure 3.6 – Schematic Diagram for the Lime Kiln Model

3.1.3 CADSIM Validation Process - Base Case Mill

Once the model has been developed, the validation will be done using data from a bleached kraft

hardwood pulp mill. A set of data covering the entire recovery area was collected from this mill.

The collection was done by accessing the intranet system of the plant and downloading

worksheets containing all available historic data of the mill operation. The presence of high

sulfidity operating conditions was one of the main interests in choosing this mill as a case study.

This particular characteristic make the mill suitable for a comprehensive evaluation of the effect

of makeup strategies on the Na and S balances, as well as on the Cl and K accumulation in the

recovery area. A more detailed description of the kraft mill layout is given in the results section.

3.1.3.1 Data Availability

Assembling all available system information is an important stage in the development of this

work. Modern on-line data measurements on all parts of the mill made the work much faster.

OSIsoft PI system collected and stored process measurements at high sampling rates from

instruments located at strategic locations throughout the mill. Lab records were also part of the

36

process variables used in the assessment of the mill operating conditions. The data resolution for

each variable ranged from hourly-averaged measurements taken by on-line instruments, to mill-

lab data collected as infrequently as a few times per year. A list with the significant parameters

collected for different areas is shown in Table 3.5.

3.1.3.2 Data Processing

The processing of the data consisted of selecting a data resolution, visually inspecting and

removing problematic variables, formatting the dataset for comparison purposes, and creating a

validation dataset for the steady state tests of mill areas and the dynamic tests of sulfidity.

The selection of data resolution was both a practical and a subjective decision. Weekly or

monthly data sampling was close to the size of the time period observed for maintenance in

different areas of the mill and showed large variation in many important parameters. Hourly

intervals over multiple years would have led to lengthy outlier removal and impractical

computation times. The chosen resolution was the daily averages which was sensitive enough to

detect sporadic instrument problems, but able to capture the trends of major variables in the

process such as liquor flows, concentration of chemicals, rate of production, etc.

The inspection of the data obtained was then performed. Missing or suspect data can occur due

to instrument failures, recalibrations, or offline maintenance. The simplest way to detect missing

or suspect data is to plot each variable individually and look for problems. Once this step was

done, an analysis of the presence of outliers was performed.

Outlying data points can be defined as observations inconsistent with the structure or trends of

normal mill operation. They occur in process datasets due to instrument failures or

miscalibration, abnormal periods encompassing start-ups and shutdowns, process interruptions,

and operational time shifts. The easier way to identify these measurements is to calculate the

95% confidence interval for the dataset. Usually, gross outliers lie far outside the interval and

can be removed from the dataset.

37

Table 3.5 - Variables Collected for Different Mill Areas

Digester Recovery Boiler Dregs Black Liquor Recirculation Feedwater Flow to Boiler Dregs Flow from Tanks 3 and 4 Sulfidity of White Liquor High Pressure Steam Flow to Boiler Dregs Flow from Cassette Filters Density of Weak Black Liquor Feedwater Pressure to Boiler Dregs Flow from X-filters Total Flow of Steam to Digester Feedwater Temperature to Boiler Green Liquor Dregs Total Alkali Total Output of Digester Steam Temperature Boiler Exit Dregs Dry Solids from Tank Digester Production Steam Pressure Boiler Exit Dregs Dry Solids from Filters Total Flow of White Liquor Blowdown Flow Dregs+Grits Amount Causticizing B+C Steam Pressures Liquor Firing Load Dregs+Grits Amount Causticizing D Alkali Charge (White Liquor) Liquor Solids Content Causticizing NaOH Consumption Liquor Temperature Lime Flow to Slaker Concentration of Weak Black Liquor Liquor Pressure Green Liquor / Lime Ratio Charge on Wood Liquor Composition Slaker Temperature Weak Black Liquor Temperature Air Flow rates Contaminated Condensate Washing Impregnation Bin Temperature Air Temperatures Causticizing Efficiency in Slaker Wood Chips Flow Air Flow NCG Burner Na2CO3 in Slaker Total Alkali to Digester (White Liquor) Air Pressure NCG Burner NaOH in Slaker ClO2 Generator NCG Flow to Burner Na2S in Slaker NaClO3 Flow NCG Temperature to Burner Total Flow of Condensate H2SO4 Flow Flue Gas Temperatures Lime Consumption Methanol Flow Flue Gas Pressures Liquor +Mud Composition Analysis Steam Flow O2 Concentration Flue Gases Total White Liquor Flow Reboiler Liquor Temperature CO Concentration Flue Gases White Liquor Composition Analysis Measured ClO2 Production SO2 Concentration Flue Gases Cl in White Liquor to Digester Generator Efficiency TRS Concentration Flue Gases CaCO3 in White Liquor to Digester Total Production per Day Opacity Flue Gases K in White Liquor to Digester Generator Pressure Reduction Efficiency Na2CO3 in White Liquor to Digester Saltcake Production per Day Smelt Temperature NaOH in White Liquor to Digester Evaporation Chloride Content in Smelt Na2S in White Liquor to Digester Weak Black Liquor Flow to Evap. Precipitator Ash Composition Lime Kiln Weak Black Liquor Dry Solids Sulfate Makeup Flow Lime Mud Flow to Kiln Weak Black Liquor Temperature Sootblower Steam Flow Lime Mud Density to Kiln Pressure of All Effects Sootblower Steam Pressure Lime Mud Dry Solids to Kiln Pressure Surface Condenser Sootblower Steam Temperature Lime Mud Composition to Kiln Vapor Temperature Surface Condenser Dissolving Tank Fuel Flow to Kiln Dry Solids in the Exit of All Effects Weak Wash Flow Fuel Pressure to Kiln Liquor Temperature at Exit of Effects Weak Wash Temperature Fuel Temperature to Kiln Liquor Flow at Exit of Effects Raw Green Composition Analysis Air Flow To Kiln Cooling Water Temperature Condenser Filtered Green Liquor Total Alkali Air Pressure to Kiln Cooling Water Flow to Condenser Lime Mud Flow to Cassette Filters NCG Burning in Kiln Condensate Flow From Last Effect Raw Green Liquor Temperature Kiln ID-Fan Speed Total Steam Flow to Evaporation Green Liquor Total Flow to Filters Kiln Zone Temperatures Steam Flow to 1st Effect Green Liquor Total Flow to X-filters Reburned Lime Composition Steam Temperature to 1st Effect Total Green Liquor Flow Kiln Production Strong Black Liquor Flow O2 in Kiln Flue Gas Dry Solids Strong Black Liquor TRS in Kiln Flue Gas Storage Tank Temperature CO in Kiln Flue Gas Flue Gas Temperature

38

3.1.3.3 Steady-state and Dynamic Simulation of the Mill

Operating data of the evaporation plants, recovery boilers, causticizing plants and lime kilns

were grouped into specific areas of the mill. The values were then evaluated in order to find a

period of stable operating conditions. The values of all variables collected were then averaged

and the input data fed into the simulations for each individual area. Once the results of a

simulation of an individual area have been obtained, they were compared against the actual

operating data. If the data simulated agreed with the data obtained from the mill, the model is

considered valid for that area of the plant and another area is simulated.

However, if the data does not agree, a material balance calculation is performed using the data

from the area under investigation to identify possible errors within the data used. If the data does

not fit the material balance, it is discarded and another set is chosen. If the data fits the material

balance, the CADSIM model used for that mill area is cross-checked to identify which errors are

present and fix them. Once all the individual parts were simulated and the results confirmed a

full mill model was then built and a similar procedure was done in order to confirm the model

capability for a full cycle calculation. A schematic diagram representing this procedure is shown

in Figure 3.7 and the material balance spreadsheet used for each area is presented in Appendices

D, E and F.

After all the steady-state simulations were performed the model was used to predict the dynamic

behavior of the process. For this part of the study only one operating parameter was examined

which is the liquor sulfidity. Once all other necessary variables have been entered into the

program, the model was then used to calculate the sulfidity over a known period of time. Tests

were then performed involving disturbances in the makeup system observed during mill

operation. These tests help to evaluate the impact of makeup changes on the sulfidity of the

white liquor. All calculated values were compared to actual mill values.

39

Figure 3.7 - Schematic Diagram of Validation Process Used

40

3.2 Cl and K Enrichment Factor Simulation

Cl and K impact on recovery boiler operation is connected to the enrichment factor of these

elements in the precipitator ash. As their content in the ash increases, fouling and plugging

problems may become severe. To reduce boiler problems the ash treatment systems are being

used. However, it is not clear at this moment if the presence of ash treatment systems would

affect the Cl and K enrichment over time and if there is an effect how it would impact the ash

treatment. In order to confirm that, a FactSage model was developed to study the impact of

different variables on the Cl and K enrichment factors. Once the model was developed, lab tests

were performed to check model predictions.

3.2.1 EPAC Model

FactSage is the chemical equilibrium software used to develop a model to predict the ash

chemistry and consequently calculate the enrichment factors for Cl and K. Details about the

FactSage software are presented in Appendix A. The model developed in this work is called

EPAC (Electrostatic Precipitator Ash Chemistry). In this model a recovery boiler is divided into

two zones: a reducing zone in the lower furnace and an oxidizing zone above the black liquor

guns. The calculations are performed in four main steps:

i) the model first uses a mass balance program to calculate the amounts of C, H, S, Na, K, and

Cl released from the as-fired black liquor as a result of devolatilization / pyrolysis;

ii) next it uses FactSage to calculate compositions and amounts of all possible gases in the

reducing zone;

iii) it then calculates the total inputs to the oxidizing zone by adding the amounts obtained in

Step (i) to the amounts obtained in Step (ii)

iv) it finally uses FactSage to calculate the amounts and compositions of flue gas (gas phase)

and precipitator ash (condensed phase) based on total inputs obtained in Step (iii) and an

assumed precipitator inlet temperature of 160oC.

In order to simplify the model calculations, several assumptions are made:

41

All gases are ideal;

Equilibrium is attained in both the reducing and oxidizing zones;

No physical carryover is entrained in the flue gas in the upper furnace;

Smelt is completely separated from the flue gas in the lower furnace;

Water and other releasable components in the black liquor are released immediately as the

liquor is sprayed into the boiler and become vapors or gases which are perfect mixed with the

flue gas in the oxidizing zone.

The FactSage used in the EPAC model is the version 6.2. It consists of a series of modules that

access various updated thermodynamic databases in order to perform calculations using a Gibbs

free energy minimization technique.

As shown in Figure 3.8, the calculation is performed as follows. The amount of combustion air

(stoichiometric air demand) needed to burn the black liquor is obtained based on the as-fired

black liquor composition and flow rate (Stream 1). The calculated amount of air is divided

between the oxidizing zone (Stream 4) and reducing zone (Stream 7) according to the desired air

ratio (Stream 2). A portion of black liquor, which has been released in the reducing zone during

drying and pyrolysis, enters the oxidizing zone (Stream 5), while the rest of the black liquor

(Stream 6) enters the reducing zone.

Using the combined input data from Streams 6 and 7, FactSage calculates the amount and

composition of the smelt in the lower furnace, as well as the amount and composition of the gas

phase at a given bed temperature. This equilibrium gas phase (Stream 8) is transported to the

oxidizing zone where it is mixed with the remaining combustion air (Stream 4), the black liquor

components released during devolatilization and pyrolysis (Stream 5) and the excess air used in

the upper furnace (Stream 3). FactSage then recalculates the amounts and compositions of the

gas (flue gas) and solids (precipitator ash) at 160°C.

42

Figure 3.8 – Schematic Diagram for Model Developed in FactSage.

3.2.2 Determination of Inorganic Release

The amounts of Na, K, Cl and S released during the devolatilization and pyrolysis of the black

liquor droplets (Stream 5 of the model) were determined using a thermogravimetric apparatus as

shown in Figure 3.9. A sample of dried black liquor (0.1 grams) prepared according to the

procedure in the Appendix C was placed in a platinum crucible which was subsequently

suspended from a balance. The crucible was then introduced in a temperature-controlled furnace

preheated at a desired temperature. The sample weight is continuously monitored by the balance

which was connected to a data acquisition system.

The release amounts for Na, K, Cl and S were determined by the difference in mass of each

component before (in the black liquor) and after (in the residue) the test. The tests involved five

black liquor samples burned at temperatures varying from 400 to 900°C in air. The exposure

time of the sample in the furnace was fixed at 60 seconds.

43

Figure 3.9 - Thermogravimetric Apparatus

3.2.3 Entrained Flow Reactor Test

The entrained flow reactor is used to study the formation of deposits in the tube surfaces as well

as the composition of ash formed in conditions that mimic the upper part of a kraft recovery

boiler [104]. In this study, 5 different black liquor samples of known composition were prepared

according to the procedure in the Appendix C and burnt in order to evaluate the findings of the

FactSage model. The fume generated by the combustion process is then analyzed and its

enrichment factor determined. Finally, the enrichment factors measured are compared to the

calculated value predicted by the FactSage model.

A schematic diagram of the equipment is shown in Figure 3.10. It consists of a particle feeder, a

gas combustion chamber, a long vertical heated section and a non-heated sampling section. The

particle feeder consists of a horizontal belt conveyor and a water-cooled particle injector. The

belt conveyor is 0.3 m wide and 1.2 m long and transported particles of dried black liquor at a

44

mass flow rate of approximately 5 g/min to the top of the particle injector. The injector

introduces the particles into the top of the heated section and is used to produce a hot flue gas

stream, which entrains the particles and passes through the reactor. The gas combustion chamber

is located at the top of the heated section and is equipped with a natural gas burner, which

consumes natural gas at flow rate of 1 to 2.5 standard m3/h. The combustion gases produced are

mixed with dilution air to produce gases with temperatures up to 1200°C. The heated section is

an assembly of five tubular furnaces 1.22 m high with 30 cm of internal diameter that can be

electrically heated up to a maximum of 1350°C. Between adjacent furnaces, there is a 10 cm

high observation port installed. These assembly results in heated section 6.5 m long that provides

a hot environment for the black liquor particles to burn. The non-heated section consists of an

insulated chamber located between the bottom of the heated section and the exhaust system. The

chamber has two half cylinders 23 cm high which closes around the probe where samples can be

collected for analysis. In total the entrained flow reactor extends for 9 m from the top of the

particle feeder to the bottom of the exhaust system [105].

The tests performed in this reactor consisted in setting the heated section at the desired

temperature (tests were run at 800°C and 900°) and allowed to reach steady state (usually period

of 12 hours). The 5 black samples, prepared as mentioned in Appendix C, were sieved to ensure

that the particles were smaller than 90 m and then fed to the reactor. The particles burned as

they traveled downward with the hot flue gas stream through the heated section of the EFR. The

fume formed during the combustion process is collected from the reactor using a fume sampling

device showed in Figure 3.10. The fume sampling device consists of a stainless steel tube 70 cm

long and 1.6 cm I.D. with a filter placed at the middle of the device and a vacuum pump at one

end. The device is placed at the non-heated section of the reactor in order to collect the fumes

exiting the heated section. The purpose of the fume sampling device is to reduce contamination

with carryover, which affects significantly the enrichment factor measurement, by sucking the

fume gases from the non-heated section before they condense.

The samples collected are then dissolved in water and analyzed for sodium, potassium, chloride,

sulfate and sulfide content. Each experiment was run in triplicates.

45

Figure 3.10 – Entrained Flow Reactor and Fume Sampling Device

3.3 Summary

As an overview of this chapter it would be interesting to highlight the different steps taken

towards the completion of this work. The first part includes the development of the model

involving the use of CADSIM built-in models, the EPAC model developed using FactSage and

the neural network to generate the ash treatment block using data provided by OLI. A simplified

diagram of this part of the thesis work is shown in Figure 3.11.

Figure 3.11 – Schematic Diagram of Model Development

Chapter 4 focuses on the findings for the EPAC model developed from FactSage, including the

experimental work needed to obtain the release factors and the model validation using the

entrained flow reactor.

46

The next step was the CADSIM-based model validation as described in section 3.1.3.3 and in

Figure 3.7. The model validation results include the steady state simulation of mill areas, the full

mill steady state simulation and the dynamic calculation of the white liquor sulfidity. The results

are discussed in Chapter 5.

After the model was validated, a sensitivity study was performed in some parameters that would

affect the Cl and K balance. The variables chosen included the Cl and K input to the process, the

soda inventory of the mill and the use of ash treatment or ash purging. The impact of ash

treatment or purging was also considered in the study of Na and S balance as well as the impact

of different ClO2 generator effluent. These results are presented and discussed in Chapter 6.

Finally, Chapter 7 presents a simplified mathematical model that can be used to provide

estimates on the balances of Cl and K for a generic recovery cycle. An overall diagram for this

thesis work is seen in Figure 3.12.

47

Figure 3.12 – Schematic Diagram of Thesis Work

48

4. Factors Affecting the Cl and K Enrichment Factors

This chapter presents and discusses the findings of a study conducted to investigate the factors

affecting the Cl and K enrichment factors. As presented in the literature survey, the Cl and K

enrichment factors are a measure of the Cl and K content in the precipitator ash. Depending on

the content, these elements can be harmful to different process equipment leading to corrosion

and plugging. With the advent of the ash treatment systems to control the Cl and K levels, two

questions were raised: how are the enrichment factors of the Cl and K affected as these elements

are purged from the process? Are there other parameters that would affect the enrichment factors

significantly?

To answer these questions a model was built to predict the composition of the precipitator ash

formed during the black liquor combustion in the recovery boiler under various operating

conditions. The model described in the methodology section requires as inputs the compositions

and amount of black liquor and combustion air, the char bed temperature and the amount of

inorganic chemicals released during the black liquor spraying. The model output consists of the

amounts and compositions of smelt, flue gases and the precipitator ash. The enrichment factor is

then calculated based on the precipitator ash obtained and the black liquor input provided.

Considering that the release of inorganic chemicals is not known, the first part of this chapter

describes the findings of an experimental study conducted to identify these values. Then, the

study of the impact of char bed temperature, combustion air, black liquor composition (Cl, K and

S content) and inorganic release on the Cl and K enrichment factors is presented. The last part of

the chapter involves a test conducted in an entrained flow reactor in order to confirm the

capability of the model to simulate the Cl and K enrichment factors.

4.1 Determination of Inorganic Release from Black Liquor

The amount of inorganic chemicals lost during “in-flight” combustion plays a significant role in

the final values obtained for the enrichment factors. In order to gain a better understanding of the

release process and determine the values to be used in the Cl and K enrichment factor model, a

set of experiments were performed to obtain the release amount from different black liquor

49

samples for a set of specific conditions. Once this information was obtained, the final model

could be used in the study of the factors affecting the Cl and K enrichment factors.

The release factors of the inorganic compounds present in the black liquor were determined

experimentally for 5 different black liquor samples with compositions as indicated in Table 4.1.

The tests consisted of exposing the dried samples to a desired temperature (400 to 900°C) for a

period of 60 seconds. The residue remaining after the combustion is then analyzed and the

amount of remaining inorganic chemicals is compared to the initial mass in the black liquor. This

experiment would provide information on the amount of inorganic chemicals lost by the black

liquor droplets during the time they travelled from the black liquor guns to the lower bed. A

graph showing the variation of the release factor as a function of temperature for one sample is

shown in Figure 4.1. As it can be seen, at high temperatures the release values are higher due to

the vaporization of part of the inorganic chemicals, but as the test temperatures are reduced the

release values level off identifying the release factor value. Other curves were obtained for all 5

black liquor samples for the range of temperatures considered.

0

5

10

15

20

400 500 600 700 800 900

Temperature (°C)

Rel

ease

Fac

tor (

%)

Na

Cl

K

Figure 4.1 – Release Factor Determination for Black Liquor Sample

50

Table 4.1 – Black Liquor Composition (wt% - dry basis)

Element Sample 1 Sample 2 Sample 3 Sample 4 Sample 5

Na 18.71 18.58 18.29 18.00 18.04

K 2.46 3.06 3.10 1.06 2.84

Cl 0.54 0.41 0.15 0.18 1.68

S 4.65 3.06 4.01 5.05 5.20

The release factors obtained from the flat part of the curves were averaged among the tests

conducted in triplicates and are shown in Table 4.2. They will be used in the validation tests

conducted in the entrained flow reactor discussed in section 4.3. These values were then

compared to literature values obtained from various sources [40, 52, 55-57, and 100].

Table 4.2 – Average Value of Experimental Release Factor

Element Portion of Black Liquor Input (wt%)

Sample 1 Sample 2 Sample 3 Sample 4 Sample 5

Na 5.1 ± 2.5 8.3 ± 2.5 7.0 ± 2.5 9.1 ± 2.5 7.7 ± 2.5

K 6.2 ± 2.5 9.5 ± 2.5 12.7 ± 2.5 10.3 ± 2.5 10.7 ± 2.5

Cl 7.5 ± 5.0 14.4 ± 5.0 15.1 ± 5.0 13.7 ± 5.0 12.8 ± 5.0

S 24.6 ± 5.0 25.9 ± 5.0 25.0 ± 5.0 25.5 ± 5.0 24.8 ± 5.0

Although on average the release factors are close, the exact values vary from liquor to liquor.

The different release factors found among liquors is attributed to the fact that these elements can

be in the form of different compounds in the black liquor. For example, organically bound Na

and S are expected to be released readily when thermal decomposition starts. On the other hand,

the release of inorganic Na and S will be dependent on which compounds they are. For example,

residual NaOH would be relatively easy to vaporize, while Na2S or Na2SO4 may not be released.

4.2 Parameters Studied

In this study the parameters considered were of two types: boiler operating conditions and black

liquor characteristics. The EPAC model described in section 3.2 will be used to evaluate the

impact of the following boiler operating parameters on the enrichment factors: the char bed

temperature, in the interval from 800 to 1300°C; and the amount of combustion air reaching the

51

lower bed, raging from 30 to 70%. The black liquor characteristics considered are three different

Cl and K contents and two sodium-to-sulfur ratios (S/Na2). In all cases, the excess air was fixed

at 20%, and the pressure throughout the boiler was set to 1 atm.

4.2.1 Effect of Bed Temperature

Table 4.3 shows the composition of the as-fired black liquor used in this study. The amount of

combustion air in equilibrium with the smelt bed is set at 30% of the theoretical combustion air.

The data needed for the upper section included the temperature at the electrostatic precipitator

inlet, 160°C for all tests, and the pressure set at 1atm.

Table 4.3 - Black Liquor Elemental Analysis

Element Composition (wt %) C 33.90 H 3.90 O 35.80 Na 20.30 S 5.10 K 0.80 Cl 0.20

0.0

1.0

2.0

3.0

800 900 1000 1100 1200 1300Temperature (°C)

Enric

hmen

t Fac

tor Cl

K

Figure 4.2 - Chloride and Potassium Enrichment Factors as a Function of Temperature

Combustion Air = 30%; Composition: Cl=0.2%, K=0.8%, S/Na2 = 0.35

52

Figures 4.2 show how bed temperature affects the enrichment factor for Cl and K. The

enrichment factors initially increase as the lower bed temperature increases until a maximum of

2.7 for Cl at 1050°C and 1.7 for K at 1100°C. At higher temperatures, the enrichment factor

decreases due to two factors: the first is that Cl and K are depleted in the char bed due to lower

concentrations; the second is that, at high temperatures, the amount of Na release becomes

significantly larger than the amounts of Cl and K which cause a “dilution” effect resulting in a

lower EFCl and EFK as pointed out by [40].

4.2.2 Effect of Combustion Air

The amount of combustion air affects the reduction efficiency and temperature in the lower

furnace, and hence, has a great effect on the compositions of flue gas and smelt. In this study, the

amount of combustion air was varied between 30 and 70% of the total combustion air needed for

a complete combustion of black liquor. The black liquor composition used is in Table 4.3.

0.0

1.0

2.0

3.0

4.0

30 40 50 60 70Combustion Air (% Theoretical Air)

Enric

hmen

t Fac

tor

Cl

K

Figure 4.3 - Chloride and Potassium Enrichment Factors as Function of Combustion Air

Temperature =1000°C; Composition: Cl=0.2%, K=0.8%, S/Na2 = 0.35

Calculation results show that if the combustion air flow is lower than 30%, a significant amount

of char will be present in smelt due to incomplete combustion. On the other hand, if the

combustion air flow is higher than 70%, it lowers the reduction efficiency, which is undesirable.

53

Figure 4.3 indicates the effects of combustion air on the Cl and K enrichment factors at 1000°C.

As the combustion air increases, the enrichment factors increase. Janka et al. [40] suggested that

combustion air had an effect on Cl and K enrichment factors, but they did not show what the

effect was. In this study, EFCl increases from 2.4 at 30% to 3.5 at 70%, while EFK increases from

1.3 at 30% to 1.4 at 70%.

Based on the results obtained from the model, it seems that the increase in enrichment factors is a

result of lower Na release from the lower bed as in Hupa [48]. As shown in Figure 4.4, the total

amount of Na released at 1000°C decreases with an increase in combustion air flow, whereas the

concentrations of the species containing chlorine (i.e. NaCl and KCl) do not change appreciably.

The release of K is similar to that of Na, explaining the more pronounced variation in the Cl

enrichment factor compared to that in the K enrichment factor.

Although this finding is interesting, it is important to keep in mind that the model used in this

study considers the temperature and combustion air to be independent variables. However, in

recovery boilers, the amount of combustion air also affects the char bed temperature.

0.0001

0.001

0.01

0.1

1

10

100

0 20 40 60 80 100Air Ratio (% Theoretical Air)

% V

olum

e Na

K

KCl

NaCl

Figure 4.4 - Na, K and Cl Release from Char Bed as a Function of Combustion Air at 1000°C

54

4.2.3 Effect of Cl and K Content in the Black Liquor

The combustion of black liquor samples with different compositions was simulated to evaluate

the effect of Cl and K content on the Cl and K enrichment factors. Table 4.4 shows three as-fired

black liquor compositions used in this study. One liquor has a high potassium content (5 wt %),

the other has a high Cl content (2 wt %) and the third has both high Cl and K contents.

Table 4.4 - Black Liquor Elemental Analysis for Cl and K Test

Element Composition (wt %) C 32.3 33.3 31.8 H 3.8 3.9 3.7 O 34.3 35.1 33.5 Na 19.5 19.9 19.1 S 4.9 5.0 4.9 K 5.0 0.8 5.0 Cl 0.2 2.0 2.0

Figures 4.5 and 4.6 indicate the effect of temperature and composition on the Cl and K

enrichment factors. The temperature varied from 800 to 1300°C and the combustion air in the

lower furnace was set to 30%. One curve shows the Cl enrichment factor for a black liquor

sample containing 0.8 wt% K and 0.2 wt% Cl. The second curve shows the Cl enrichment factor

for a black liquor sample containing 0.8 wt% K and 2 wt% Cl. The third curve is obtained from a

sample containing 5 wt% K and 2 wt% Cl. The result indicates that, if only the Cl content is

increased, the Cl enrichment factor decreases, but if both K and Cl contents are increased, the Cl

enrichment factor increases. Similar behavior is seen for the K enrichment factor. If both K and

Cl contents are increased, the K enrichment factor increases.

However both enrichment factors are affected by the Cl and K content in the black liquor, the

increases and decreases observed are not significant in both cases. The changes in Cl (from 0.2 to

2 wt%) and K (from 0.8 to 5 wt%) contents in the as-fired black liquor resulted in changes not

bigger than 0.2 in the Cl and K enrichment factors. This result shows that the presence of ash

treatment systems lowering the concentrations of Cl and K in the liquors would not affect

substantially the enrichment factors.

55

Chloride

0.0

1.0

2.0

3.0

800 900 1000 1100 1200 1300Temperature (°C)

EFC

l

5% K and 2% Cl0.8% K and 2% Cl0.8% K and 0.2% Cl

Figure 4.5 - Effect of Higher Cl and K Contents on Cl Enrichment Factor

Combustion Air = 30%, Composition: S/Na2 = 0.35

Potassium

0.0

1.0

2.0

3.0

800 900 1000 1100 1200 1300Temperature (°C)

EFK

5% K and 2% Cl5% K and 0.2% Cl

0.8% K and 0.2% Cl

Figure 4.6 - Effect of Higher Cl and K Contents on K Enrichment Factor

Combustion Air = 30%, Composition: S/Na2 = 0.35

56

4.2.4 Effect of Sulfur Content in Black Liquor

As discussed earlier, the effect of SO2 in the flue gas on Cl and K enrichment factors is related to

the “sulfation” of the ash in the upper region of the recovery boiler. The amount of SO2 in the

flue gas is greatly affected by the sulfur content in the liquor and by the bed temperature. To

evaluate the impact of SO2, two different black liquor compositions were considered: one has a

sulfur-to-sodium ratio (S/Na2) equal to 0.35 (typical for most kraft mills) and the other 0.5 (high

sulfur content). The lower bed temperature was varied from 800 to 1300°C.

Table 4.5 - Black Liquors with Different Sulfur Content

Elemental Analysis Composition (wt %) Na 20.4 21.4 S 5.0 7.4 K 0.8 0.8 Cl 0.2 0.2

Figure 4.7 shows the influence of char bed temperature and sulfur content on Cl and K

enrichment factors. At low temperatures, Cl and K enrichment factors are not affected by the

sulfur content. Although previous works indicated that at low bed temperatures the SO2 present

in the flue gas would lead to the sulfation of the ash reducing Cl enrichment factors, the results in

this work do not show that [43-44]. This happened because the amount of HCl formed by the

sulfation reaction is small compared to the amount of Cl remaining in the ash. Other simulations

were then conducted using ashes with higher Cl content and a similar behavior to the studies

mentioned was observed. In the case of K, the sulfation reaction does not affect its final content

in the ash.

At high temperatures, Cl and K enrichment factors are higher for the liquor with high sulfur

content. This happens because the equilibrium condition in the lower bed changes and the sulfur

salts will remain mostly in the bed [48]. As a result, the liquor with high sulfur content will

produce an ash with higher Cl and K contents, resulting in higher enrichment factors at high

temperatures.

57

Chloride

0.0

1.0

2.0

3.0

4.0

800 900 1000 1100 1200 1300

Temperature (°C)

EFC

l

S/Na2 = 0.5

S/Na2 = 0.35

Potassium

0.0

0.5

1.0

1.5

2.0

800 900 1000 1100 1200 1300Temperature (°C)

EFK

S/Na2 = 0.5

S/Na2 = 0.35

Figure 4.7 - Effect of Sulfur Content on Cl and K Enrichment Factors

Combustion Air = 30%, Composition: Cl=0.2%, K=0.8%

58

4.2.5 Effect of Inorganic Release

In this work, part of the black liquor sprayed into the boiler is assumed to be released and enters

the oxidizing zone (Stream 5) of the boiler. In order to accurately predict the enrichment factors,

this portion released must be considered. More specifically, the Na release affects not only the

enrichment factors but also the amount of fume generated. Sulfur release is also important, but it

affects mostly the carbonate content in the ash.

While the portion of the liquor released during the processes of drying, devolatilization and

pyrolysis is important, there is no consensus about their amounts. Frederick et al [52] showed

that sodium release was between 23 and 33% from the input value, while Verrill et al [57]

suggested a lower Na loss, from 5 to 20%. Another study showed the release value was in the

range of 3 to 15% [55]. Studies of Sricharoenchaikul et al. [56] indicate S releases between 25

and 50%.

The uncertainty over the amount of Na and S release is due to the fact that these elements can be

in the form of different compounds in the black liquor. Organically bound Na and S are expected

to be released readily when thermal decomposition starts. On the other hand, the release of

inorganic Na and S will be dependent on which compounds they are. For example, residual

NaOH would be relatively easy to vaporize, while Na2S or Na2SO4 may not be released readily.

In order to confirm the importance of the release during combustion, one test was run with the

values for Na release varying over the range found for one black liquor sample. The result is in

Figure 4.8.

The results indicate that the K enrichment factor is not affected significantly over the range of

sodium release used. This is expected since the behavior of sodium and potassium is similar and

even the values of release factor measured are in the same range. On the other hand the Cl

enrichment factor decreases as the sodium release increases. This is expected, since more sodium

release result in more sodium salts which would “dilute” the Cl salts in the ash.

59

0

0.5

1

1.5

2

2.5

3

3.5

5 6 7 8 9 10Na Release Amount (wt %)

Enric

hmen

t Fac

tor

Cl

K

Figure 4.8 - Effect of Inorganic Release on Cl and K Enrichment Factors

Temperature = 1000°C, Combustion Air = 30%, Composition: Cl=0.2%, K=0.8%, S/Na2 = 0.35

4.3 Entrained Flow Reactor Tests

Once the impact of the operating conditions on the enrichment factors were studied, the next step

was to validate the Cl and K enrichment factor model. This was done through a combustion test

using the entrained flow reactor existing in the department described in Figure 3.7. The 5 black

liquor samples, whose release factors were previously obtained, were used for this test.

The samples are fed to the reactor and the fume generated from the black liquor combustion is

extracted from a sampling device placed at the bottom of the reactor. The fume samples are

analyzed and the composition determined. With this information the enrichment factor is

calculated for all 5 samples.

The next step is to provide the model with the inputs of the experimental setup which includes

the black liquor composition, the reactor temperature, the release factor obtained experimentally

and the amount of air to complete the combustion of the black liquor. The model then calculates

60

the Cl and K enrichment factors for the experiments performed. The comparison between the

experimental and calculated results is shown in Table 4.6.

The results indicate that the model is capable of predicting the enrichment factor with accuracy;

however it is important to highlight that the results are sensitive to the release of the inorganic

chemicals determined experimentally.

Table 4.6 – Comparison of Enrichment Factors Determined by EFR Tests and the Model

EFCl EFK

Experimental Calculated Experimental Calculated

Sample 1 2.11 ± 0.02 2.20 1.58 ± 0.04 1.51

Sample 2 2.89 ± 0.07 2.87 1.10 ± 0.05 1.19

Sample 3 3.71 ± 0.09 3.70 2.03 ± 0.08 2.20

Sample 4 2.54 ± 0.05 2.39 1.60 ± 0.05 1.50

Sample 5 2.32 ± 0.04 2.45 1.87 ± 0.03 1.80

4.4 Summary

There are three important findings which should be highlighted:

1) Cl enrichment factor is more sensitive to changes in the variables studied than the K

enrichment factor. The values calculated for Cl enrichment factor can reach values

exceeding 3.5. On the other hand, K enrichment factor are all in the interval between 1

and 2 for the set of conditions used.

2) Among all parameters studied, the char bed temperature is the most important factor

affecting the Cl and K enrichment factor.

3) The changes in the Cl and K content, in the as-fired black liquor, does not affect the Cl

and K enrichment factors significantly. Therefore, the presence of an ash treatment

system to purge Cl and K from the recovery process would allow the levels of these

elements to be reduced in the process liquors without affecting the enrichment observed

in the precipitator ash.

61

5. Model Validation for a Kraft Mill

This chapter concerns the validation of the model used in this study. The major objective is to

compare the simulated data with the actual operating data from a bleached kraft pulp mill. In

order to accomplish this task, a set of data collected from a Brazilian kraft pulp mill was

collected and treated as described in the methodology section.

The validation process was done in three steps: first the different areas of the mill were simulated

individually in order to test the model capability to simulate the process and to identify possible

errors. In the second step, all individual sub-models were put together to perform a full cycle

simulation and verify the model calculations for a closed liquor loop. The final step was

dedicated to calculate the white liquor sulfidity produced by the chemical recovery process

during specific periods of time where changes in sulfidity where identified in the mill data.

5.1 Validation of the Neural Network

Using the neural network in CADSIM is a threefold process. First, the neural network parameters

need to be specified. The first parameter is the topology of the network which is the number of

cells or neurons in the hidden layers of the network, and the non-linear mapping function used

(sigmoid or hyperbolic tangent). In this work it was set a number of 2 hidden layers (A and B)

containing 100 cells in each layer. The function used was sigmoid.

The second parameter is the learning steps, which involve the learning rate which indicates the

magnitude of change in the “weights” used by the neural network, and the momentum which

keeps track of the previous “weight” changes. For this neural network the learning steps used

were 0.5 (default for the block) and the momentum was 0.1 (also default for the block).

The last parameter is the stopping criterion which is chosen considering a number of iterations or

learning until an average error on the training patterns is met. For the neural network trained, it

was chosen that an average error not bigger than 0.3% was acceptable for the training patterns.

The training patterns submitted to the network for the training process to begin consisted of a

total of 17000 data points involving different ash treatment input conditions such as ash

composition, ash-to-water ratio and temperature. With these data, the network calculates the ash

62

solubility, the liquid and solid fraction and the solid composition. The values were then

compared to the ones obtained from OLI and an average error on the training pattern was

calculated. A schematic diagram of the neural network block used to represent the ash treatment

system is shown in Figure 5.1.

Figure 5.1 - Schematic Diagram of the Ash Treatment System Block

Once the network was able to perform the calculations within the specified error limits, the third

step or the validation process was initiated. For this step, other 4000 data points were used to

validate the neural network. The purpose of the validation is to confirm the capability of the

network to generalize within the domain of data used; in other words, its capacity to interpolate

between the limits of operation for the process simulated.

The process consisted of a comparison between the data calculated by the neural network and the

OLI software as shown in Figure 5.2 for ash solubility. Most of the results, including liquid and

solid phase composition, are very close to OLI results; suggesting that the block was valid. The

block was then inserted in the model of the whole chemical recovery process and used to

calculate, Na, S, Cl and K balances for a whole mill simulation.

63

R2 = 0.9991

300

400

500

600

300 400 500 600

Ash Solubility (g/L) - OLI

Ash

Sol

ubili

ty (g

/L) -

Neu

ral N

etw

ork

n = 4000

Figure 5.2 - Comparison of Ash Solubilities Given by OLI and the Neural Network (NN)

5.2 Steady State Simulation of Mill Areas

In order to evaluate the CADSIM-based model developed in this study, a set of data covering an

entire recovery area was collected and used for comparison. This mill has three fiber lines and a

recovery system to process the black liquor as shown schematically in Figure 5.3. The mill is

equipped with an advanced data collection system, and has kept good historical records of

operating data, liquor analysis and precipitator ash analysis over a wide range of liquor

sulfidities. The mill is also equipped with an ash treatment system for controlling Cl and K.

These particular features make the mill a good case study to evaluate the effect of makeup

strategies on Na and S balance, as well as on Cl and K accumulation.

Operating data of the evaporation plants, recovery boilers, causticizing plants and lime kilns

were first analyzed to remove errors. The data was grouped into specific areas of the mill and

simulations were performed on each individual area. The results are presented in the following

order: 5 evaporation plants, 3 recovery boilers and 3 causticizing plants.

64

Figure 5.3 - Schematic Diagram of the Mill Case Study

5.2.1 Evaporation Plants of Case Study Mill

There were 5 evaporation plants at this mill site. Four plants had 6 effects and 10 evaporator

bodies, and one plant had 7 effects and 11 evaporator bodies. The effects were numbered as 1A,

1B, 1C, 1D, 2A, 2B, 3, 4, 5, 6 and 7 respectively. In all plants, steam is supplied to evaporators

1A, 1B, 1C and 1D in parallel. In 4 plants, the weak black liquor is fed in the last effect and in

one plant it is fed in the 4th effect and then sent to the last effect. The flow of black liquor and

steam are counter-current and the vapor from the 1st effect is used to heat the liquor in the 2nd

effect and so on. After the 2nd effect the liquor is sent to a medium concentration storage tank

and then to the first effect (1A, 1B, 1C and 1D). The clean condensate from the 1st effect is

collected and returned to the boiler while the condensed vapor from the other effects is collected

and either reused or treated as effluent. A schematic diagram for one of the plants is shown in

Figure 5.4.

Figure 5.4 - Schematic Diagram of the Simulated Evaporation Plant

The data used in the simulation of the Evaporation Plant 1 is shown in Table 5.1A. This data is

an average value obtained for a three month period of stable operating condition of the plant

simulated. It is important to mention that some plants have more data available for comparison,

65

due to more advanced monitoring technologies used on its installation while the older plants

have less data for comparison. As described in the methodology, a material balance calculation

was also performed to check the validity of the data and help in finding suitable datasets to be

used in CADSIM. The calculation procedure is shown in Appendix D.

Table 5.1A – Input Data Used for Evaporation Plant 1 Simulation

Units Plant Data Weak Black Liquor Input Flow m3/h 439.7 Weak Black Liquor Solids % 14.0 Weak Black Liquor Temperature ºC 83.5 Surface Condenser Water Input Temperature ºC 32.9 6th Effect Pressure kPa -83.6 5th Effect Pressure kPa -70.5 3rd Effect Pressure kPa -24.3 1D Effect Pressure kPa 186.7 1C Effect Pressure kPa 200.0 1B Effect Pressure kPa 231.3 1A Effect Pressure kPa 232.4 Live Steam Temperature ºC 152.9 Strong Black Liquor Solids % 62.8

Once the plant model in Figure 5.4 reached the steady state, the values available in the mill data

were compared to the model findings. The result is seen in Table 5.1B. As it can be seen, the

result is in good agreement with the data available for evaporation plant 1. A similar procedure

was then adopted for the remaining 4 evaporation plants. The input data for each plant is shown

in the respective table followed by the table with the comparison between the simulation result

and the plant data.

Table 5.1B – Comparison of Steady State Data for Evaporation Plant 1

Units Plant Data Simulation Black Liquor Flow from Intermediate Tank m3/h 208.6 200.6 Steam Economy m3/t 5.5 5.2 Condensate Flow m3/h 47.8 62.2 Surface Condenser Water Flow m3/h 2856.3 2854.8 Surface Condenser Water Output Temperature ºC 50.2 45.6 Total Steam Flow t/h 65.8 69.0 Steam Flow to 1D Effect t/h 32.4 35.9 Steam Flow to 1C Effect t/h 13.7 14.1 Steam Flow to 1B Effect t/h 10.2 10.0 Steam Flow to 1A Effect t/h 9.2 9.1 Strong Black Liquor Flow to Tank m3/h 90.4 85.6 Strong Black Liquor Temperature to Tank ºC 120.1 119.6

66

Table 5.2A - Input Data Used for Evaporation Plant 2 Simulation

Units Plant Data Weak Black Liquor Input Flow m3/h 597.3 Weak Black Liquor Solids % 17.0 Weak Black Liquor Temperature ºC 86.3 Surface Condenser Water Input Temperature ºC 31.8 6th Effect Pressure kPa -80.2 5th Effect Pressure kPa -73.5 4th Effect Pressure kPa -64.7 3rd Effect Pressure kPa -50.2 2B Effect Pressure kPa -13.6 2A Effect Pressure kPa 73.4 1D Effect Pressure kPa 201.6 1C Effect Pressure kPa 201.8 1B Effect Pressure kPa 211.6 1A Effect Pressure kPa 211.8 Live Steam Temperature ºC 142.1 Strong Black Liquor Solids % 63.7

Table 5.2B – Comparison of Steady State Data for Evaporation Plant 2

Units Plant Data Simulation Black Liquor Flow from Intermediate Tank m3/h 254.1 213.6 Steam Economy m3/t 5.8 5.4 Surface Condenser Water Flow m3/h 3659.9 3644.6 Surface Condenser Water Output Temperature ºC 49.2 44.5 Total Steam Flow t/h 86.5 86.8 Steam Flow to 1D Effect t/h 35.1 37.3 Steam Flow to 1C Effect t/h 22.6 21.4 Steam Flow to 1B Effect t/h 15.0 14.9 Steam Flow to 1A Effect t/h 13.8 13.3 Strong Black Liquor Flow to Tank m3/h 135.2 144.7 Strong Black Liquor Temperature to Tank ºC 115.5 128.0

Table 5.3A - Input Data Used for Evaporation Plant 3 Simulation

Units Plant Data Weak Black Liquor Input Flow m3/h 400.2 Weak Black Liquor Solids % 16.0 Weak Black Liquor Temperature ºC 87.0 6th Effect Pressure kPa 77.7 5th Effect Pressure kPa 64.0 4th Effect Pressure kPa 45.1 3rd Effect Pressure kPa 6.6 Surface Condenser Water Input Temperature ºC 33.9 Black Liquor Solids Effect 1D % 66.3 Black Liquor Solids Effect 1C % 67.6 Black Liquor Solids Effect 1B % 71.9 Black Liquor Solids Effect 1A % 70.5 Strong Black Liquor Solids % 75.8

67

Table 5.3B – Comparison of Steady State Data for Evaporation Plant 3

Units Plant Data Simulation Black Liquor Flow to Intermediate Tank m3/h 97.0 105.4 Black Liquor From Evaporation A+B m3/h 240.4 277.7 Black Liquor Solids to 1D Effect % 62.6 62.7 Steam Economy m3/t 4.8 4.5 Surface Condenser Water Flow m3/h 3816.3 3823.2 Surface Condenser Water Output Temperature ºC 48.1 42.9 Steam Flow to 1D Effect t/h 21.4 18.2 Steam Flow to 1C Effect t/h 22.0 13.5 Steam Flow to 1B Effect t/h 21.3 13.9 Steam Flow to 1A Effect t/h 21.5 19.4 Strong Black Liquor Flow to Tank m3/h 218.3 217.8 Strong Black Liquor Temperature to Tank ºC 129.1 126.6

Table 5.4A - Input Data Used for Evaporation Plant 4 Simulation

Units Plant Data Weak Black Liquor Input Flow m3/h 812.5 Weak Black Liquor Solids % 16.1 Weak Black Liquor Temperature ºC 88.6 7th Effect Pressure kPa -81.6 6th Effect Pressure kPa -64.2 5th Effect Pressure kPa -52.3 4th Effect Pressure kPa -27.8 3rd Effect Pressure kPa 8.0 2B Effect Pressure kPa 65.3 2A Effect Pressure kPa 68.1 1D Effect Pressure kPa 239.0 1C Effect Pressure kPa 242.1 1B Effect Pressure kPa 240.4 1A Effect Pressure kPa 240.8 Surface Condenser Water Input Temperature ºC 32.0 Strong Black Liquor Solids % 78.0

68

Table 5.4B – Comparison of Steady State Data for Evaporation Plant 4

Units Plant Data Simulation Black Liquor Solids Effect 1A % 72.3 76.5 Black Liquor Solids Effect 1B % 63.7 67 Black Liquor Solids Effect 1C % 61.6 62 Black Liquor Solids Effect 1D % 54.1 55 Black Liquor Solids Effect 2A % 46.6 49.5 Black Liquor Solids Effect 2B % 43.4 36.9 Black Liquor Solids Effect 3 % 28.1 29.6 Black Liquor Solids Effect 4 % 24.0 25.2 Black Liquor Solids Effect 5 % 21.2 22.2 Black Liquor Solids Effect 6 % 20.3 19.9 Black Liquor Solids Effect 7 % 19.4 18.3 Vapor Temperature Exit Effect 2A ºC 114.6 120.8 Vapor Temperature Exit Effect 2B ºC 114.1 112.5 Vapor Temperature Exit Effect 3 ºC 102.2 106.9 Vapor Temperature Exit Effect 4 ºC 91.2 101.1 Vapor Temperature Exit Effect 5 ºC 80.7 90.4 Vapor Temperature Exit Effect 6 ºC 73.5 80.3 Vapor Temperature Exit Effect 7 ºC 54.0 56.8 Black Liquor Temperature Exit Effect 1A ºC 129.1 142 Black Liquor Temperature Exit Effect 1B ºC 128.8 141 Black Liquor Temperature Exit Effect 1C ºC 128.8 139 Black Liquor Temperature Exit Effect 1D ºC 129.9 135 Black Liquor Temperature Exit Effect 2A ºC 120.2 125.4 Black Liquor Temperature Exit Effect 2B ºC 111.6 111.7 Black Liquor Temperature Exit Effect 3 ºC 98.1 104.9 Black Liquor Temperature Exit Effect 4 ºC 86.4 93.3 Black Liquor Temperature Exit Effect 5 ºC 76.6 82.5 Black Liquor Temperature Exit Effect 6 ºC 67.1 75.1 Black Liquor Temperature Exit Effect 7 ºC 56.6 60.9 Temperature of Condensate at Exit Effect 7 ºC 58.8 62.5 Surface Condenser Water Output Temperature ºC 45.7 45.8 Surface Condenser Water Flow t/h 5440.3 5440.3 Total Steam Flow t/h 126.4 112.2 Strong Black Liquor Flow to Tank m3/h 134.7 140.3 Strong Black Liquor Temperature to Tank ºC 134.9 134.7

Table 5.5A – Input Data Used for Evaporation Plant 5 Simulation

Units Plant Data Weak Black Liquor Input Flow m3/h 474.5 Weak Black Liquor Solids % 15.6 Weak Black Liquor Temperature ºC 85.9 6th Effect Pressure kPa -72.9 5th Effect Pressure kPa -62.2 4th Effect Pressure kPa -40.7 3rd Effect Pressure kPa -0.2 1D Effect Pressure kPa 198.6 1C Effect Pressure kPa 205.1 1B Effect Pressure kPa 244.4 1A Effect Pressure kPa 254.8 Surface Condenser Water Input Temperature ºC 28.6

69

Table 5.5B – Comparison of Steady State Data for Evaporation Plant 5

Units Plant Data Simulation Steam Economy m3/t 4.2 5.5 Surface Condenser Water Output Temperature ºC 45.6 47.1 Total Steam Flow t/h 89.5 72.9 Steam Flow Effect 1D t/h 25.6 26.1 Steam Flow Effect 1C t/h 23.7 22.5 Steam Flow Effect 1B t/h 21.3 14.1 Steam Flow Effect 1A t/h 19.6 10.2 Flow of Condensate Exit Effect 7 m3/h 333.7 348.6 Vapor Temperature Exit Effect 2A ºC 114.6 108.4 Vapor Temperature Exit Effect 3 ºC 100.1 99.9 Vapor Temperature Exit Effect 4 ºC 86.0 86.2 Vapor Temperature Exit Effect 5 ºC 74.7 75.4 Vapor Temperature Exit Effect 6 ºC 66.9 64.9 Black Liquor Temperature Exit Effect 1A ºC 131.1 142.0 Black Liquor Temperature Exit Effect 1B ºC 128.2 138.3 Black Liquor Temperature Exit Effect 1C ºC 124.7 134.7 Black Liquor Temperature Exit Effect 1D ºC 122.4 129.1 Black Liquor Temperature Exit Effect 2A ºC 106.2 114.4 Black Liquor Temperature Exit Effect 2B ºC 107.7 107.0 Black Liquor Temperature Exit Effect 3 ºC 91.2 87.9 Black Liquor Temperature Exit Effect 4 ºC 79.1 76.7 Black Liquor Temperature Exit Effect 5 ºC 68.5 69.0 Boiling Point Rise Effect 1A ºC 17.7 20.8 Boiling Point Rise Effect 1B ºC 14.3 16.6 Boiling Point Rise Effect 1C ºC 10.4 13.7 Boiling Point Rise Effect 1D ºC 8.3 9.1 Boiling Point Rise Effect 2A ºC 6.8 6.0 Boiling Point Rise Effect 2B ºC 6.1 3.4 Boiling Point Rise Effect 3 ºC 5.3 2.3 Boiling Point Rise Effect 4 ºC 4.6 1.7 Boiling Point Rise Effect 5 ºC 2.1 1.3 Boiling Point Rise Effect 6 ºC 1.8 1.1 Strong Black Liquor Flow to Tank m3/h 78.4 82.7 Strong Black Liquor Temperature to Tank ºC 128.3 127.1

In order to help an evaluation of all these data, Figure 5.5 plots the ratio between the mill data

and the respective simulated values for each output variable. All the data of flow rates, pressures,

temperatures and concentrations are shown in this graph. Ratio values close to 1 means that the

simulated values agree well with the actual mill data. As it is seen in Figure 5.5 most data is in

good agreement, except for two groups where the ratio exceeds 1.5 and other 2.0. The first group

represents the steam flow to the evaporators in plant 5 and the other the calculated BPR for some

effects of plant 5. The BPR discrepancies are connected to heat transfer coefficients assumed in

the calculations used by mill personal, while the steam consumption is connected to a fouling

episode. The results suggest that CADSIM is capable of simulating the evaporation plants and

that it ought to be able to simulate the remaining parts of the recovery area.

70

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Figure 5.5 - Comparison between Simulated and Mill Values for Evaporation Plants

5.2.2 Recovery Boiler of Case Study Mill

The second set of results was obtained from simulations performed using CADSIM with data

gathered from the three recovery boilers at this mill. Figure 5.6 shows a schematic diagram of the

recovery boiler model built with CADSIM to simulate the boilers. As described in the

methodology, a material balance calculation was also performed to check the validity of the data

and help in finding suitable datasets to be used in CADSIM. An example of the calculations

performed for the recovery boiler simulation is shown in the Appendix E.

Figure 5.6 - Schematic Diagram of the Simulated Recovery Boiler

71

Recovery boiler A is a bi-drum boiler with a design firing capacity of 3440 tds/d of black liquor

at 77% dry solids. Air for the liquor combustion is provided in three levels. Under normal

operating conditions, the boiler would be able to achieve 95% reduction efficiency and generate

524 t/h of steam at 6.4 MPa and 450°C.

Similarly to the evaporation plants test, the data employed are average values obtained for a three

month period of stable operating condition of the boiler simulated. The input data is presented in

Table 5.6A followed by the comparison between simulation result and boiler data in Table 5.6B.

Recovery boiler B is a single drum boiler that has a capacity of firing 3600 tds/d black liquor at

77% dry solids. The air distribution system is also divided into three levels. The average steam

generation is about 510 t/h of superheated steam at 6.4MPa and 450°C. The input data used in

boiler B simulation is given in Table 5.7A and the results of the comparison in Table 5.7B.

Table 5.6A – Input Data for Recovery Boiler A Simulation

Streams Items Units Boiler Data Black Liquor C wt% 35 H wt% 3.3 O wt% 34.1 Na wt% 18.6 K wt% 2.2 S wt% 5.5 Cl wt% 1.2 Inerts wt% 0.8 Solids Concentration wt% 76.6 Flow t/d 2980 Temperature ºC 135 Higher Heat Value kJ/kg BLS 13650 Ambient temperature ºC 25 Air FD air preheat temp. ºC 117 Excess Air % Stoichiometric 10 Smelt reduction efficiency % 90 Smelt Temperature ºC 860 Exit gas temperature ºC 182 Stack gas TRS as H2S ppm 0.2 SO2 ppm 40 CO ppm 72 Steam Blowdown flow of BLS 3% Sootblowing steam of BLS 9% Feedwater temperature ºC 110 Input pressure MPa 8.9 Water Feedwater Temperature ºC 130 Ash Dust recycle % of BLS 8 Cl Enrichment Factor 2.7 K Enrichment Factor 1.6 SO4/CO3 Ratio 25

72

Table 5.6B – Comparison of Steady State Data for Recovery Boiler A

Units Plant Data Simulation Feedwater Flow t/h 387.1 381.2 Superheater Steam Flow t/h 369.4 369.5 Boiler Drum Pressure MPa 7.1 7.1 Superheater Steam Temperature ºC 444.8 445.0 Superheater Steam Pressure MPa 6.2 6.2 Sootblower Steam Temperature ºC 307.1 296.1 Black Liquor Organic Fraction % 60.8 61.0 Primary Air Flow t/h 130.9 132.6 Secondary Air Flow t/h 309.5 312.3 Tertiary Air Flow t/h 83.5 85.7 Sodium in Ash % 28.7 29.1 Potassium in Ash % 5.5 5.8 Chloride in Ash % 5.6 5.5 Carbonate in Ash % 1.0 1.5 Sulfate in Ash % 58.7 58.1

Table 5.7A – Input Data for Recovery Boiler B Simulation

Streams Items Units Boiler Data Black Liquor C wt% 35 H wt% 3.3 O wt% 33.6 Na wt% 18.2 K wt% 2.5 S wt% 5.1 Cl wt% 2.2 Inerts wt% 0.8 Solids Concentration wt% 75.7 Flow t/d 2830 Temperature ºC 133 Higher Heat Value kJ/kg BLS 13980 Ambient temperature ºC 25 Air FD air preheat temp. ºC 127 Excess Air % Stoichiometric 10 Smelt reduction efficiency % 90 Smelt Temperature ºC 860 Exit gas temperature ºC 170 Stack gas TRS as H2S ppm 0.5 SO2 ppm 74 CO ppm 142 Steam Blowdown flow of BLS 4% Sootblowing steam of BLS 10% Feedwater temperature ºC 132 Input pressure MPa 8.8 Water Feedwater Temperature ºC 132 Ash Dust recycle % of BLS 8 Cl Enrichment Factor 3.0 K Enrichment Factor 1.6 SO4/CO3 Ratio 30

73

Table 5.7B – Comparison of Steady State Data for Recovery Boiler B

Units Plant Data Simulation Feedwater Flow t/h 378.5 380.6 Superheater Steam Flow t/h 351.5 351.3 Boiler Drum Pressure MPa 6.7 6.8 Superheater Steam Temperature ºC 429.8 429.4 Superheater Steam Pressure MPa 5.9 5.9 Sootblower Steam Temperature ºC 279.6 284.2 Black Liquor Organic Fraction % 60.8 61.5 Primary Air Flow t/h 167.8 170.6 Secondary Air Flow t/h 233.9 237.5 Tertiary Air Flow t/h 94.3 97.4 Sodium in Ash % 25.8 29.3 Potassium in Ash % 6.9 6.7 Chloride in Ash % 13.9 13.2 Carbonate in Ash % 0.8 1.0 Sulfate in Ash % 52.5 49.8

Recovery Boiler C is a single drum boiler that processes 2900 tds/d at 77% concentration. The

boiler reduction efficiency is around 94% and generates 433 t/h of steam at 6.4MPa and 455°C.

Table 5.8A – Input Data for Recovery Boiler C Simulation

Streams Items Units Boiler Data Black Liquor C wt% 35 H wt% 3.3 O wt% 32.8 Na wt% 19 K wt% 2.6 S wt% 5.1 Cl wt% 2.1 Inerts wt% 0.8 Solids Concentration wt% 76.1 Flow t/d 2480 Temperature ºC 132 Higher Heat Value kJ/kg BLS 13600 Ambient temperature ºC 25 Air FD air preheat temp. ºC 127 Excess Air % Stoichiometric 10 Smelt reduction efficiency % 92 Smelt Temperature ºC 860 Exit gas temperature ºC 185 Stack gas TRS as H2S ppm 0.7 SO2 ppm 4.2 CO ppm 209 Steam Blowdown flow of BLS 4% Sootblowing steam of BLS 8% Feedwater temperature ºC 131 Input pressure MPa 9 Water Feedwater Temperature ºC 131 Ash Dust recycle % of BLS 8 Cl Enrichment Factor 3.0 K Enrichment Factor 1.5 SO4/CO3 Ratio 4

74

Table 5.8B – Comparison of Steady State Data for Recovery Boiler C

Units Plant Data Simulation Feedwater Flow t/h 328.7 331.2 Blowdown Flow t/h 0.5 0.52 Boiler Drum Pressure MPa 7.1 7.07 Superheater Steam Temperature ºC 435.1 436.1 Superheater Steam Pressure MPa 6.1 6.19 Sootblower Steam Flow t/h 7.8 7.76 Black Liquor Organic Fraction % 60.8 60.0 Primary Air Flow t/h 126.3 138.4 Secondary Air Flow t/h 202.7 215.6 Tertiary Air Flow t/h 58.6 69.8 Sodium in Ash % 27.9 30.4 Potassium in Ash % 6.3 6.5 Chloride in Ash % 7.8 6.9 Carbonate in Ash % 12.3 12.3 Sulfate in Ash % 45.8 43.9

Once all the simulations were done, the final collection of data was plotted in Figure 5.7. As seen

in the graph, the agreement between simulated and mill values is excellent. The average error for

all the boilers simulated is around 4% with few variables with an error higher than 10% from the

calculated values.

0.0

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1.0

1.2

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43

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Rat

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Valu

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Figure 5.7 - Comparison between Simulated and Mill Values for Recovery Boilers

75

5.2.3 Causticizing Plants of Case Study Mill

The third set of simulation results was obtained for the three causticizing plants at the mill. Each

causticizing plant consists of green liquor clarification, dregs washers, slaking, causticizing,

white liquor clarification, lime mud washing, lime mud filters and lime kilns. The main

differences between these plants are their production capacity and the process used to clarify the

green liquor. Despite the complexity of the actual operation, these three causticizing plants can

still be lumped into one single system to make it easier to simulate. A schematic diagram of the

model used to simulate the causticizing plants and lime kilns is shown in Figure 5.8. As

described in the methodology, a material balance calculation was also performed to check the

validity of the data and help in finding suitable datasets to be used in CADSIM. The calculation

routine used is presented in Appendix F.

Figure 5.8 - Schematic Diagram of the Simulated Causticizing Plant

Recausticizing plant 1 has a project capacity of 5600 m3/d of white liquor with active alkali at

about 100g/L Na2O and 30% sulfidity. Under steady state operating condition the plant should

reach a causticizing efficiency of 82%. The input data for recausticizing plant 1 is shown in

Table 5.9A. The comparison between plant data and simulation results for each of the three areas

is shown in Table 5.9B.

76

Table 5.9A – Input Data Used for Causticizing Plant 1 Simulation

Units Plant Data Weak Wash Flow to Dissolving Tank m3/h 192.2 Raw Green Liquor Density at Tank kg/m3 1139.0 Total Alkali Raw Green Liquor g(NaOH)/L 159.9 Carbonate in Raw Green Liquor g(NaOH)/L 106.4 NaOH in Raw Green Liquor g(NaOH)/L 3.6 Sodium Sulfide in Raw Green Liquor g(Na2S)/L 49.0 Sulfidity in Raw Green Liquor % 31.6 Chloride in Smelt % 4.9 Dregs in Raw Green Liquor ml/L 14.4 Lime to Slaker t/h 13.7 Slaker Temperature ºC 99.9 Condensate Flow to Lime Mud Washer m3/h 27.8 Input Flow to Causticizers m3/h 192.7 Lime Mud Flow to Filter m3/h 68.5 Lime Mud Solids to Kiln % 76.9 Total Fuel to Kiln m3/h 2645.3

Table 5.9B – Comparison of Steady State Data for Causticizing Plant 1

Units Plant Data Simulation Total flow of Filtered Liquor to Slaker m3/h 191.0 191.4 Green Liquor Density to Slaker g/cm3 1.2 1.2 Filtered Green Liquor Temperature ºC 84.3 83.4 Green Liquor Dregs to Slaker m3/h 5.0 4.9 Total Alkali at Causticizers g/L 164.7 150.7 Active Alkali at Causticizers g/L 140.7 130.2 Effective Alkali at Causticizers g/L 117.8 108.2 Sulfidity at Causticizers % 31.6 30.8 Density of Liquor in Causticizer g/cm3 1.2 1.2 Lime Consumption kg/m3 54.2 63.7 White Liquor Total Flow to Storage m3/h 192.3 228.3 White Liquor Total Alkali g(NaOH)/kg 156.6 156.5 White Liquor Active Alkali g(NaOH)/L 134.6 140.6 White Liquor Effective Alkali g(NaOH)/L 110.0 113.0 White Liquor Sulfidity % 36.5 36.5 Causticizing Degree % 80.0 83.0 Chloride in White Liquor g(NaCl)/L 10.0 9.3 Lime Mud in White Liquor ml/L 0.3 0.1 Potassium in White Liquor g(K)/L 9.6 9.4 Carbonate in White Liquor g(NaOH)/L 21.6 15.8 Sulfide in White Liquor g(NaOH)/L 50.5 55.3 NaOH in White Liquor g(NaOH)/L 85.4 85.3 Residual Carbonate % 3.0 2.7 Lime Availability g(CaO)/kg 937.1 940.5 Kiln Production t/d 306.4 302.4 O2 Content in Kiln Flue Gas % 2.5 2.5 Kiln Flue Gas Temperature ºC 260.9 260.0

77

Recausticizing plant 2 can produce 8100 m3/d of white liquor with active alkali at about 100g/L

Na2O and 30% sulfidity. The expected causticizing efficiency under steady state operating

conditions would be 82%.

Table 5.10A – Input Data Used for Causticizing Plant 2 Simulation

Units Plant Data Weak Wash Flow to Dissolving Tank m3/h 321.2 Raw Green Liquor Density at Tank kg/m3 1124.1 Total Alkali Raw Green Liquor g(NaOH)/L 162.3 Carbonate in Raw Green Liquor g(NaOH)/L 106.3 NaOH in Raw Green Liquor g(NaOH)/L 4.3 Sulfide in Raw Green Liquor g(Na2S)/L 49.3 Sulfidity in Raw Green Liquor % 29.7 Chloride in Smelt % 5.3 Lime to Slaker t/h 15.0 Slaker Temperature ºC 103.0 Input Flow to Causticizers m3/h 317.0 Lime Mud Flow to Filter m3/h 68.5 Lime Mud Solids to Kiln % 76.9 Total Fuel to Kiln m3/h 2645.3

Table 5.10B – Comparison of Steady State Data for Causticizing Plant 2

Units Plant Data Simulation Total flow of Filtered Liquor to Slaker m3/h 319.9 326.2 Green Liquor Density to Slaker kg/m3 1149.0 1136.3 Raw Green Liquor Temperature ºC 94.3 93.0 Filtered Green Liquor Temperature ºC 85.0 89.0 Green Liquor Dregs to Slaker m3/h 1.0 0.9 Total Alkali Filtered Green Liquor g(NaOH)/L 161.9 159.6 Total Alkali at Causticizers g/L 168.6 132.8 Active Alkali at Causticizers g/L 136.9 111.8 Effective Alkali at Causticizers g/L 117.2 92.6 Sulfidity at Causticizers % 31.6 30.8 Density of Liquor in Causticizer kg/m3 1186.2 1212.6 Lime Consumption kg/m3 54.2 63.7 White Liquor Total Flow to Storage m3/h 295.6 323.0 White Liquor Total Alkali g(NaOH)/kg 156.3 151.9 White Liquor Active Alkali g(NaOH)/L 133.5 133.8 White Liquor Effective Alkali g(NaOH)/L 109.3 107.1 White Liquor Sulfidity % 36.7 36.5 Causticizing Degree % 79.3 79.9 Chloride in White Liquor g(NaCl)/L 11.0 10.0 Lime Mud in White Liquor ml/L 0.03 0.09 Potassium in White Liquor g(K)/L 9.8 6.7 Carbonate in White Liquor g(NaOH)/L 22.4 18.1 Sulfide in White Liquor g(NaOH)/L 49.7 53.3 NaOH in White Liquor g(NaOH)/L 83.8 80.5 Residual Carbonate % 3 2.7 Lime Availability g(CaO)/kg 937.1 940.5 Kiln Production t/d 506.4 504.2 O2 Content in Kiln Flue Gas % 2.5 2.5 Kiln Flue Gas Temperature ºC 260.9 260

78

The recausticizing plant 3 is designed for 9350 m3/d of white liquor with a higher active alkali at

105 g/L Na2O and 25% sulfidity. The causticizing efficiency under steady state operating

condition is 83%. The input data for the causticizing plant 3 is shown in Table 5.11A. The

comparison between plant data and simulation results for each of the three areas is shown in

Table 5.11B.

Table 5.11A – Input Data Used for Causticizing Plant 3 Simulation

Units Plant Data Weak Wash Flow to Dissolving Tank m3/h 367.5 Raw Green Liquor Density at Tank kg/m3 1164.0 Total Alkali Raw Green Liquor g(NaOH)/L 159.7 Carbonate in Raw Green Liquor g(NaOH)/L 97.2 NaOH in Raw Green Liquor g(NaOH)/L 3.4 Sulfide in Raw Green Liquor g(Na2S)/L 50.7 Raw Green Liquor Temperature ºC 97.6 Chloride in Smelt % 5.3 Flow Raw Green Liquor to X-Filters m3/h 364.5 Flow of Dregs from Filters m3/h 11.9 Lime to Slaker t/h 23.1 Slaker Temperature ºC 102.6 Input Flow to Causticizers m3/h 347.1 Lime Mud Flow to Filter m3/h 132.4 Lime Mud Solids to Kiln % 73.4 Total Fuel to Kiln m3/h 4181.6

Once all causticizing plant simulations were performed the results were plotted in Figure 5.9. All

parameters of flow, pressure, temperature and concentrations for green and white liquors

measured were compared to the simulated values. The results show only three parameters

presented a ratio over 1.5. The parameter in this case is the concentration of lime mud in the

white liquor. Since this value is usually low, the error may be due to experimental errors in mill

analysis. Also, the standard deviation of the value for this parameter indicates that the simulated

values would not be out of the range seen during normal operating condition. The average error

for all the plants simulated is around 9% with few variables with an error higher than 20% from

the calculated values. Similar to the previous cases, the simulation was also run using average

values over a period of normal mill operation. The agreement is also good in most of the cases.

79

Table 5.11B – Comparison of Steady State Data for Causticizing Plant 3

Units Plant Data Simulation Total flow of Filtered Liquor to Slaker m3/h 338.1 368.0 Green Liquor Density to Slaker kg/m3 1145.7 1122.8 Filtered Green Liquor Temperature ºC 82.1 93.6 Green Liquor Dregs to Slaker m3/h 5 4.9 Dregs in Raw Green Liquor ml/L 14.4 14 Total Alkali Filtered Green Liquor g(NaOH)/L 157.6 143.2 Active Alkali Filtered Green Liquor g(NaOH)/L 53.9 47.4 Carbonate in Filtered Green Liquor g(NaCO3)/L 101.6 95.8 Effective Alkali in Filtered Green Liquor g(NaOH)/L 28.5 28.5 NaOH in Filtered Green Liquor g(NaOH)/L 2.9 9.6 Sulfide in Filtered Green Liquor g(Na2S)/L 49.0 37.8 Sulfidity in Filtered Green Liquor % 32.0 32.0 Total Alkali at Causticizer 1 g/l 161.5 128.8 Active Alkali at Causticizer 1 g/l 135.8 108.7 Effective Alkali at Causticizer 1 g/l 108.0 88.7 Carbonate in Causticizer 1 g(Na2CO3)/L 25.3 21.1 NaOH in Causticizer 1 g/cm3 1.2 1.2 Na2S at Causticizer 1 kg/m3 54.2 63.7 Temperature at Causticizer 1 ºC 102.9 103.0 White Liquor Total Flow to Storage M3/h 258.4 320.4 White Liquor Total Alkali g(NaOH)/kg 155.0 152.2 White Liquor Active Alkali g(NaOH)/L 135.2 134.3 White Liquor Effective Alkali g(NaOH)/L 109.6 106.1 White Liquor Sulfidity % 38.5 38.5 Causticizing Degree % 80.4 78.7 Chloride in White Liquor g(NaCl)/L 8.8 4.6 Lime Mud in White Liquor ml/L 0.0 0.1 Potassium in White Liquor G(K)/L 9.6 6.0 Carbonate in White Liquor g(NaOH)/L 20.5 17.9 Sulfide in White Liquor g(NaOH)/L 49.7 56.5 NaOH in White Liquor g(NaOH)/L 79.8 77.8 Residual Carbonate % 3.1 3.3 Lime Availability g(CaO)/kg 920.4 941.8 Kiln Production t/d 619.1 616.6 O2 Content in Kiln Flue Gas % 5.3 5.3 Kiln Flue Gas Temperature ºC 272.3 275.0

The results obtained for the simulation of the different parts of the kraft chemical recovery

indicate that CADSIM Plus can be used to calculate Na and S balances for actual mills. The final

step for the validation is now to integrate all collected mill data in a single model that would

simulate the whole process. The chemical balance model will then be used to simulate liquor

compositions and to test the chemical makeup strategies.

80

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Figure 5.9 - Comparison between Simulated and Mill Values for Causticizing Plants

5.3 Full Mill Steady State Simulation

Once results of simulations for each individual area have been validated against actual operating

data, a model that connects all individual processes and equipment together was built to simulate

the entire liquor cycle. The mill data was reorganized and the individual plants were combined in

order to produce a simplified model as done by Treiber et al [87]. The results were then plotted

and are shown in Figure 5.10. The average error for all the plants simulated is around 10% with

few variables with an error higher than 20% from the calculated values. The graph shows only

two cases were the ratio between the mill and simulated value are over 1.5. They correspond to

the BPR calculated for the evaporation plant. The other simulated values agree well with the

actual mill data, suggesting that CADSIM was capable of simulating the entire recovery area for

the steady state condition.

The result gives confidence that the model would be able to handle the simulation of the Na and

S balance for the mill. In order to evaluate the changes in this balance the sulfidity of the white

liquor will be monitored as the response variable and all other information gathered will be used

as input for the simulation.

81

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Figure 5.10 - Comparison between Simulated Values and Mill Value for Full Mill Simulation

5.4 - Dynamic Simulation of Case Study Mill

The model was first used to predict the white liquor sulfidity. Once all other necessary variables

have been entered to the program, the model calculates the change in sulfidity over a specified

period of time. In this case, the calculated values were daily averages of the white liquor

sulfidity. They were then compared to mill values.

The first test involves the simulation of a 10 day period where the daily average values of most

data around the recovery cycle were very stable. This allowed for the system to present little

variation for the calculations and provided the result shown in Figure 5.11, where the thick black

line is the calculated values and the light line is mill data. The result was in good agreement with

the actual mill data. Another period of 10 days was then tested; however the data used had

variations due to changes in different areas of the mill including the makeup amounts. As the

result shows in Figure 5.12, the mill values differ from the calculated values. Other tests

conducted using data containing unstable periods of time also showed poor results. The source of

the problem is likely due to the abrupt change in production rate, leading to fluctuations in the

amounts of chemicals needed. The use of different control strategies for the makeup streams was

82

not extensively tested in this work, but it may correct the problem allowing simulation of

unstable periods of operation.

35

36

37

38

39

40

1 2 3 4 5 6 7 8 9 10Time (days)

Sulfi

dity

(%)

Mill Value

Calculated Value

Figure 5.11 – First Simulation Test of White Liquor Mill Sulfidity

35

36

37

38

39

40

1 2 3 4 5 6 7 8 9 10Time (days)

Sulfi

dity

(%)

Mill Values

Calculated Values

Figure 5.12 – Second Simulation Test of White Liquor Mill Sulfidity

The previous result indicated that the model should be tested further. The major focus would be

periods where the sulfidity was significantly altered due to changes in the use of saltcake from

the ClO2 plant. Since the saltcake is composed mostly of sodium sulfate (Na2SO4) and sodium

acidic sulfate (NaHSO4), the mill recycled the effluent to be used as sulfur makeup in the

recovery area to reduce cost and discharge volume.

83

On this test, a maintenance shutdown for a short period of time decreased the recirculation of

makeup from around 80 to 25 kg Na2SO4/ ton of pulp. As a result the sulfidity of the white liquor

to the digester is decreased and then returns to the previous condition once the situation was back

to normal. The result of this simulation with the mill data is presented in Figure 5.13.

20

25

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0 5 10 15Time (days)

Sulfi

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(%A

A)

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80

120

160

Saltc

ake

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vapo

ratio

n (k

g N

a 2SO

4/adt

)

Mill ValuesSimulated ValuesSaltcake Effluent

Figure 5.13 – Sulfidity Change Test due to a Short Period Shutdown of ClO2 Plant

It is interesting to notice that the sulfidity levels reduced from around 35% AA to around 30%

AA for the reduction considered, and that the sulfidity started to reduce 3 days after the saltcake

recirculation was reduced. Similarly the duration of the event was three days for the recirculation

and for the sulfidity as well.

5.5 Limitations and Model Specifications

Although the CADSIM model provided very good results in this study, the model works if the

user has in mind a set of limitations that exist in the individual blocks and/or operations. Some

specifications are also necessary for the model to run properly. These limitations and

specifications are discussed below.

84

Fiber Line

The fiber line model included in the Kraft Mill model was developed by Aurel Systems.

The author did not concentrate efforts in validating this part of the model, since a study

conducted by Oliveira and Cardoso [106] showed that the fiber line model in CADSIM is able to

simulate the process in a mill study.

Evaporation Plant

The evaporation plant in the Kraft Mill model was tested with data from the Aracruz mill

as well as data from other mills. This model, while very versatile, does not account for fouling of

the evaporators. If the fouling process has to be considered, a correlation would be needed to

estimate changes in the available heat exchange area over time. Tests conducted by Hajiha [107]

indicated that the model was sensitive to identify important variables in the evaporation process,

given that the data provided was originated from real mill data. However, tests conducted using

random values did not allow the model to reach convergence even after long periods of time.

Recovery Boiler

The recovery boiler in the Kraft Mill model requires a number of specifications due to the

complexity of the process and reactions happening. The specifications include:

Flue Gas CO - Due to incomplete mixing and cool spots in the Recovery Furnace, a small

fraction of the combustion products will be in the form of CO. This is difficult to predict

directly and need to be provided to the model.

Flue Gas Sulfur Fraction - This is the mole fraction of sulfur in the flue gases and need to

be specified.

Fly Ash Loss - This is a weight percent based on total ash mass flow and should be

specified, although losses in the precipitator are small.

Smelt Carbon Fraction - Carbon in the smelt is lumped into the inert component flow.

Generally speaking, this carbon would represent a dead load on the system and it would

not be available to the recovery system when the liquor solids are recirculated to the

boiler.

85

Chloride Enrichment Factor - This value has to be calculated using the EPAC model.

This value indicates the split of NaCl between the liquor and the ash streams. However,

the user has the option to enter the value manually in the Kraft Mill model.

Potassium Enrichment Factor - This value has also to be calculated using the EPAC

model. However, the user can enter the value manually in the model. This value also

indicates the split of potassium between the liquor and the ash streams.

Ratio of Carbone to Sulfate in the Ash - This is the weight ratio of Na2CO3 to Na2SO4 in

the fly ash. This value should be specified in the recovery boiler model.

Incomplete combustion – This is flagged in the case where the moles of all the main

elements (i.e. C, H, O, N, Na, K and Cl) are balanced and the moles of O become

negative. In this situation, the moles of carbon and hydrogen are converted to inerts in the

smelt in order to bring the moles of oxygen to 0. When the system is balanced, there will

be a slight excess of oxygen in the flue gases. It is assumed that carbon and hydrogen,

which create the largest demand for oxygen, will become unavailable and end up in the

smelt.

Negative sodium carbonate in the smelt stream: In balancing sulfur and sodium, where

the sulfur content is too high or the overall inorganic solids are out of the correct range,

the residual sodium may be negative. In this situation, the Na2CO3 in the smelt will be

negative and an error message will be issued by the program. This should not occur if the

sulfur moles are not excessive relative to the sodium. This situation can be corrected by

increasing the NaOH flow or by entering Na2CO3 flow or reducing the sulfur flowing in

as SO2.

Causticizing Plant

The causticizing plant model is very sensitive to the lime makeup stream. It is advised

that the changes in the plant should be made in small increments to avoid upsets in the lime mud

filter and causticizers.

86

5.6 Summary

Four important findings are worth mentioning in this chapter:

1) The neural network block was able to calculate the solubilities of different precipitator ash as

done by the OLI software.

2) CADSIM was able to simulate all individual parts of the chemical recovery process as

indicated by the validation results. The discrepancies from the mill values were on average

lower than 10%, but some variables presented differences higher than 20%.

3) The steady state simulation of the full liquor cycle was also successful as expected, once all

individual parts were put together.

4) The dynamic simulation of the full mill was also successful for the two cases presented.

However it is important to mention that only the sulfidity was simulated as a response

parameter over periods of time under very stable operating conditions. Other parameters

were not simulated and unstable operating conditions resulted in poor results.

87

6. Sensitivity Analysis of CADSIM Model

This chapter discusses the findings of a sensitivity analysis done with the model developed for

the entire kraft mill and validated against real data as shown in the previous chapter. Two

important balances were studied: one is the accumulation of Cl and K in the recovery boiler

precipitator ash; the other is the Na and S concentration in the white liquor. As already

mentioned, the Cl and K levels are associated with corrosion and plugging of chemical recovery

equipment, while Na and S concentrations are important parameters in the pulping process.

The effect of three different variables on the Cl and K accumulation were studied: the input of Cl

and K with wood, the mill soda inventory and the use of ash purging/ash treatment. With respect

to the Na and S balance, the study evaluates the impact of ash purging versus ash treatment and

use of different chlorine dioxide effluents.

6.1 Accumulation of Cl and K in the Precipitator Ash

Cl and K enter the liquor cycle with wood and with makeup chemicals. Typically there were no

intentional purges for alkali soluble elements in a kraft mill operation. Most of the alkali soluble

ions leave the liquor cycle with the pulp and with liquor spills or other unintentional losses. A

small portion of Cl and K is purged with the boiler flue gas, grits, dregs, lime, etc. Thus to study

the accumulation of Cl and K in the precipitator ash it was used the model developed in

CADSIM.

As in all material balances, the parameters that would affect the final steady state concentration

and the time to reach it are the input stream, the output stream and the size of the system studied.

Thus for this accumulation calculation the variables studied involve the Cl and K input, ash

purging / treatment amount and the amount of chemicals in the recovery cycle (soda inventory).

The tests designed consider a hypothetical mill producing 1000 tons of bleached kraft pulp per

day with the initial concentrations of Cl and K set to be zero throughout the process. Other

assumptions about the process are: the amount of precipitator ash generated is around 8% of the

fired solids, the enrichment factor is fixed at 2.5 for Cl and 1.5 for K; the sulfidity is kept fixed at

29.5% AA. The makeup chemicals consisted of NaOH and effluent from a R8 ClO2 generator.

88

6.1.1 Steady State Definition

At time zero, the input of Cl and K with wood is set to 1 kg/ ton of pulp produced. The levels of

Cl and K are then monitored until steady state condition is reached. Considering that the kraft

chemical recovery is a recycling process and estimating that process transients follow a well-

stirred mixed tank, the changes in Cl and K concentrations will asymptotically approach the

steady state end point and thus take a long time.

Therefore, in order to compare results of simulations with different input variables, a steady state

end point should be defined. Thus, the steady state condition was assumed to be reached when

the percentage change in concentration of Cl and K in the precipitator ash was 1% from the

previous day. At this point the changes in the Cl and K concentrations would hardly be detected

by the analysis employed to measure them. Figure 6.1 shows one example from a Cl and K

accumulation test where the steady state is reached after 65 days.

0

20

40

60

80

100

0 30 60 90Time (days)

% C

hang

e fro

m P

revi

ous

Day

1%

Cl K

Figure 6.1 – Determination of Steady State Condition for Cl and K Ash Concentration

89

6.1.2 Effect of Cl and K Input

In this test two input amounts of Cl and K in the wood are studied in order to evaluate the effect

of input amount in the steady state concentration seen in the ash. At the beginning the Cl and K

concentrations are set to zero throughout the process. For the first test, at time zero the inputs of

Cl and K entering with wood are set to 1 kg/ ton of pulp produced. The levels of Cl and K are

then monitored until the Cl and K in the precipitator ash reaches steady state as defined earlier.

In the second test all similar conditions are maintained except for the inputs of Cl and K that now

are set to 2 kg/ ton of pulp produced. The results are shown in Figures 6.2 and 6.3 respectively

for Cl and K. As expected the effect of input in the steady state concentration affects the final

value obtained at steady state. For the example shown the concentration doubles as the input

doubles. However the time needed to reach the steady state concentration in the system is the

same around 65 days as indicated in Figures 6.2 and 6.3. Other tests conducted for an input of

3kg and 0.5kg per ton of pulp resulted in similar behavior, what lead us to conclude that the final

steady state concentration is directly proportional to the Cl and K input given that the remaining

conditions throughout the process are kept constant.

0

1

2

3

4

5

0 30 60 90 120 150Time (days)

Cl o

r K/(N

a+K

), m

ol%

in a

sh

Cl

K

Time to reach steady state

Figure 6.2 - Effect of Cl and K Input on Steady State Concentration for Cl=K=1kg/ton pulp

90

0

2

4

6

8

10

0 30 60 90 120 150Time (days)

Cl o

r K/(N

a+K

), m

ol%

in a

shCl

K

Time to reach steady state

Figure 6.3 - Effect of Cl and K Input on Steady State Concentration for Cl=K=2kg/ton pulp

6.1.3 Effect of Soda Inventory

The soda inventory is defined as the mass ratio between the total amount of Na in the liquor in

storage tanks, equipment, pipelines, etc. in the recovery cycle, and the amount of Na that is

needed for the process. This ratio, which is typically about 2.5, gives an estimate of the amount

of chemicals present in the recovery cycle. To study the effect of the soda inventory in the

accumulation of Cl and K in the precipitator ash, three soda inventory levels were studied using

the CADSIM model: typical (2.5), small (1.5) and large (3.5). These values were obtained by

increasing or reducing the amount present in the cycle maintaining the amount needed for the

process constant.

Similar to the previous test, the initial concentrations of Cl and K are first set to be zero. At time

zero the input of Cl and K are set to 1 kg/ton of pulp. The Cl and K concentrations in the ash are

monitored until the system reaches steady state. The results are shown in Figures 6.4 and 6.5

respectively for Cl and K.

91

0

1

2

3

4

5

0 30 60 90 120

Time (days)

Cl/(

Na+

K),

mol

% in

ash

Large Inventory (3.5)

Small Inventory (1.5)

Typical Inventory (2.5)

Figure 6.4 - Cl Accumulation in Precipitator Ash – Effect of Inventory

0

1

2

3

0 30 60 90 120

Time (days)

K/(N

a+K

), m

ol%

in a

sh

Large Inventory (3.5)

Small Inventory (1.5)

Typical Inventory (2.5)

Figure 6.5 - K Accumulation in Precipitator Ash – Effect of Inventory

As expected, an increase in mill soda inventory did not affect the final steady state concentration

of Cl and K, but it requires more time for the system to reach this value. This is understandable

because there are more chemicals stored around the process and thus it takes longer for the

92

concentration to change and to reach a final steady state. The result shown in Figure 6.6 indicates

that the time to reach steady state increases from 32 days for a hypothetical mill with soda

inventory of 1 to 43 days for a small soda inventory mill (1.5), to 65 days for a typical soda

inventory mill and 88 days for a large soda inventory mill. This linear relationship between the

time to reach steady state and the soda volume will be discussed in section 7.1.

R2 = 0.9999

0

20

40

60

80

100

1 2 3 4Soda Inventory Size

Tim

e to

Rea

ch S

tead

y St

ate

(day

s)

Figure 6.6 - Time for Cl and K Concentration to Reach Steady State for Different Inventory

6.1.4 Effect of Ash Purging / Ash Treatment

The effect of ash purging and ash treatment were also studied using the same method. At time

zero, the input of Cl and K with wood in the model is set to 1 kg/ ton of pulp produced. The

levels of Cl and K are then monitored as they increase and reach steady state. After this period

two scenarios were considered: (i) the ash is purged to reduce Cl and K levels, (ii) an ash

treatment system is installed.

Different amounts of ash were purged or treated in order to see the effects on the Cl and K

content in the ash. The amounts chosen were 5%, 10%, 25%, 50% and 100% of the ash. For the

ash treatment system an ash to water ratio equal to 1 kg/L and temperature of 80°C was

established as standard operating condition.

93

As an example of the calculations, the data for 5, 10 and 25% of ash purging and ash treatment

are shown in Figures 6.7 and 6.8. As is seen, the levels of Cl and K reduce as the purging or

portion treated is increased. For Cl, a 5% purging resulted in an ash with 3.66 mol% at steady

state while 10% resulted in 3.29 mol% and 25% purging 2.39 mol%. In the case of K, the

concentrations at steady state for 5% purging is 1.95 mol%, 10% purging resulted in 1.83 mol%

and 25% purging is 1.49 mol%. It is important to notice that the levels of Cl and K at steady state

are close if compared ash purging and ash treatment. This happens because the ash treatment

system efficiency is close to 100%, which is equivalent of the removal by ash purging.

Once the portion being treated or purged is sent back to the process, the levels of Cl and K return

to their original concentrations as expected. Also, as indicated by the dashed lines the time to

reach the steady state condition remains the same.

Figure 6.7 - Accumulation of Cl and K in the Precipitator Ash – 5, 10 and 20% Ash Purging

94

Figure 6.8 - Accumulation of Cl and K in the Precipitator Ash – 5, 10 and 20% Ash Treated

In order to observe the effects of ash purging and treatment, a graph showing the steady state

levels of Cl and K in the precipitator ash for different portions treated is shown in Figure 6.9.

0

1

2

3

4

5

0 20 40 60 80 100

Portion of Ash Purged or Treated (%)

Cl o

r K/(N

a+K

), m

ol%

in a

sh Ash PurgedAsh Treated

K

Cl

Figure 6.9 - Cl and K Levels in the Precipitator Ash for Different Portion of Ash Treated

95

The result suggests that above 40% ash purged or treated, the Cl and K contents at steady state

are not significantly reduced. This result is expected since the content of Cl and K in the system

is lower, which makes its removal more difficult. Also the Cl and K inputs to the system are

maintained constant, which prevents the depletion of both ions.

6.2 Balance of Na and S in the Recovery Cycle

The effect of three variables on the Na and S balance in the recovery cycle were studied: ash

purging versus ash treatment, the makeup procedure and the ClO2 effluent used. The study was

conducted using the CADSIM model as described in the Cl and K accumulation study, but in this

case the objective was to obtain the amount of makeup needed to maintain the alkali stock and

the sulfidity of the white liquor. The values controlled for this hypothetical mill example are

NaOH concentration (70 g/l as Na2O) and sulfidity (29.5% AA basis).

6.2.1 Effect of Ash Purging versus Ash Treatment

Once the Cl and K balance study was performed, the impact of ash purging and ash treatment on

the Na and S balance was also studied. More specifically, it was simulated by the CADSIM

model how much Na and S makeup is necessary to keep a targeted sulfidity of 29.5% AA in the

white liquor. The test is similar to the one performed for the Cl and K accumulation, but now the

Na and S makeup is monitored to identify the amounts at steady state needed to provide a white

liquor with proper Na and S balance.

Thus, the first 150 days of the graph shows the amount needed to sustain the needed Na and S

balance. Then, similarly to the Cl and K study, two scenarios are considered: in one the ash is

purged and in the other the ash is treated. Once the purge or treatment starts, the model calculates

the makeup amount needed for this new steady state condition. For the calculations, three

makeup streams were available to be supplied into the chemical recovery: caustic (NaOH),

saltcake (Na2SO4) and effluent from the chlorine dioxide generators. The result for the test

conducted with 33% ash purging is shown in Figure 6.10.

96

0

10

20

30

40

0 30 60 90 120 150 180 210Time (days)

Mak

eup

Amou

nt (t

/d)

NaOH

ClO2 Effluent

Na2SO4 Makeup

0% Ash Purged 33% Ash Purged

Figure 6.10 - Na and S Makeup Requirement to Maintain 29.5% Sulfidity (AA) – 33% Ash

Purging

The results for 33% ash treated are shown in Figure 6.11. As is shown, there is a significant

difference between the makeup amounts required in each case. For the purging test, there is a

need to add saltcake makeup. On the other hand, the makeup requirement for the test where the

ash treatment was used, show that the sulfidity could be maintained without saltcake makeup.

97

0

10

20

30

40

0 30 60 90 120 150 180 210

Time (days)

Mak

eup

Am

ount

(t/d

)0% Ash Treated 33% Ash Treated

NaOH

ClO2 Effluent

Figure 6.11 - Na and S Makeup Requirement to Maintain 29.5% Sulfidity (AA) – 33% Ash

Treated

As Figures 6.12 and 6.13 indicate, the advantage of ash treatment systems in this case study lies

in the reduced makeup requirement needed to maintain a specified sulfidity. In Figure 6.13, is

seen that all ClO2 effluent available is not enough to sustain the sulfidity as purging increases

above 50%.

98

0

20

40

60

0 20 40 60 80 100

Fraction of Ash Purged (%)

Mak

eup

Am

ount

(t/d

)

NaOH ClO2 Effluent

Na2SO4 Makeup

Figure 6.12 - Total Makeup Requirement for Different Fractions of Ash Purged

0

20

40

60

0 20 40 60 80 100

Fraction of Ash Treated (%)

Mak

eup

Am

ount

(t/d

)

NaOH

ClO2 Effluent

Figure 6.13 - Total Makeup Requirement for Different Fractions of Ash Treated

99

6.2.2 – Effect of Chlorine Dioxide Effluent

The model is then used to evaluate the impact of the change in the process used in the ClO2 plant.

Two technologies are considered: the R8/SVP-Lite process and the R10/SVP-SCW process,

which are the most common ClO2 technologies used by bleached kraft mills today. The inputs

(purchased chemicals), losses and performances used in the model for both processes are set

based on values found in the literature. The amount of saltcake that needs to be sent from the

ClO2 generator to the chemical recovery area is then calculated in order to keep a targeted

sulfidity value.

Since the two processes are different, the amount of sodium recovered, the amount of sulfur

recovered, and the amount of effluent sewered are different. Table 6.1 summarizes the

calculations performed for a mill producing 25 tonnes/day of ClO2.

R10/SVP-SC is different from R8/SVP Lite in the following parameters:

no need to neutralize the saltcake

better sodium and sulfur recovery

less effluent is sewered.

Table 6.1 - Comparison of Two ClO2 Processes

ClO2 Plant Process R8 / SVP Lite R10 / SVP-SC Type of Saltcake Produced Na3H(SO4)2 Na2SO4 Sulfur Recovered from ClO2 Plant 5.7 t/d 5.8 t/d Sodium Recovered from ClO2 Plant 5.6 t/d 8.4 t/d Effluent Sewered 25% 1%

The results show that the model is able to simulate different ClO2 processes and could be used to

study the impact of by-product streams used as makeup.

6.3 – Summary Regarding the Cl and K balance the following findings summarize the results presented:

1) The input of Cl and K to the mill is directly proportional to the steady state concentration

of these ions in the precipitator ash.

100

2) The soda inventory size does not affect the steady state concentration of Cl and K in the

precipitator ash, but the time for the steady state concentration to be reached. The

increase in soda inventory showed a linear correlation with the time to reach steady state.

3) The ash purging and ash treatment effects on the levels of Cl and K in the precipitator ash

are more pronounced for the first 40% purged or treated, as the fraction of purging or

treatment increase beyond this amount the reduction is not as significant.

With respect to the Na and S balance the findings of the simulations are:

1) For the system studied, the use of ash treatment reduced the Na and S requirement from

128 t/d for 100% ash purging to 52 t/d for 100% ash treatment. The savings with the ash

treatment were able to maintain the recovery cycle at desired sulfidity and alkali stock

without the need of extra makeup stream.

2) The use of R10 ClO2 generator technology allowed a reduction in 24% in effluent

sewered.

101

7. Simple CSTR Material Balance Model

Once the model has been validated and a sensitivity study conducted by the CADSIM model two

questions arises: can the results obtained in the sensitivity study provide a general insight in the

behavior of the Cl, K, Na and S balances? How are the results in these two sections connected?

The point in exploring these questions is to find out if some estimates could be made about the

balances of the elements considered in this study without the need for a complete simulation of

the recovery cycle, which takes a significant amount of time and resources.

In order to answer these two questions, it will be developed in this chapter a mathematical

approach to the problem studied. The development of this simplified mathematical modeling and

the assumptions made are shown in the first section. The implications on how the results are

connected to the findings in the previous chapter are discussed in the second section.

7.1 Mathematical Modeling of the Kraft Process As mentioned earlier K and Cl are the most important non-process elements in the kraft cycle.

They are soluble in the liquor cycle and have the wood chips as their major source. These

elements usually leave the cycle in the form of liquor spills, in the cellulose pulp or as part of

purging processes such as the one done with the electrostatic precipitator ash. Although these

input and output streams are important in determining the final levels of Cl and K, their actual

flow rate is small if compared to the chemical inventory circulating in the recovery cycle.

Therefore considering the recovery cycle shown in Figure 7.1 (a), the input amount q is much

smaller than the amount circulating in the process Q. The recycle system can them be expected

to behave similarly to an agitated vessel with a high level of internal recirculation or, more

specifically, to a continuous stirred tank reactor model as indicated in Figure 7.1 (b).

102

Figure 7.1 – Schematic Representation of Chemical Recovery Cycle as a CSTR Model

Now considering that the tank in Figure 7.1(b) at time zero contains an initial amount Qo of Cl

dissolved in a total volume V. Assume that at time zero the tank will start to receive an input

stream at the rate of f containing Cl at concentration Cin and the well-stirred mixture is draining

from the tank at the same rate as indicated in Figure 7.2. The model should then allow the

calculation of the amount of Cl at any time in the tank as well as the concentration that would be

reached at steady state.

Figure 7.2 – Schematic Diagram of CSTR

103

To find the general equation that describes this process, a material balance needs to be performed

around the tank. Thus, the rate of change of Cl in the tank, dQ/dt, is equal to the rate at which Cl

is flowing in minus the rate at which Cl is flowing out. The input rate is given by the input

concentration Cin multiplied by the rate f given. The output rate similarly is given by the output

concentration multiplied by the rate f.

Since the output concentration is not given it must be calculated. Considering that the mixture is

“well-stirred”, the concentration throughout the tank is the same and equal to the output

concentration or more specifically Q(t)/V. Thus the equation governing this process is:

VQffC

dtdQ

in (7.1)

where Q is the amount of Cl (for this example is considered kg), t is the unit of time (seconds),

Cin is the input concentration (kg/m3), f is the flow (m3/s) and V is the volume of the tank (m3).

Rearranging this equation it is obtained:

inVCQVf

dtdQ

(7.2)

Separating the variables the equation 7.2 becomes:

dtVf

VCQdQ

in

(7.3)

Integrating the left side of equation 7.3 with respect to Q and the right side with respect to t:

KVftVCQln in (7.4)

where K is a constant of integration. Now taking the exponential of both sides in equation 7.4:

)exp()/exp( KVftVCQ in (7.5)

Equation 7.5 can be rearranged to give:

)/exp( VftkVCQ in (7.6)

104

It is given that at time zero Q(0), an initial amount Qo is present in the tank, then:

in0in0 VCQk0kVCQ )exp( (7.7)

Using the value found for the integration constant k, equation 7.6 becomes:

)/exp(/exp VftQVft1VCQ 0in (7.8)

Now considering that the concentration at any time is given by C = Q/V and at the beginning by

C0 = Q0/V, equation 7.8 becomes:

Vft0

Vftin eCe1CC // (7.9)

Thus it is shown that the concentration at any time inside the tank would be influenced by two

portions: the first portion described by the first term in equation 7.9 is the concentration coming

from the input stream due to the flow process and the second portion described by the second

term is the concentration of the original material that remains inside the tank. A graph

representing the equation 7.9 in the case where the initial concentration is set to zero at time zero

(C0 = 0), Cin = 1kg/m3 and f = 3 m3/day is shown in Figure 7.3.

0

0.5

1

1.5

0 20 40 60 80 100 120Time (days)

Con

cent

ratio

n (k

g/m

3 )

Figure 7.3 – Graphic Representation of Equation 7.9 with C0 = 0, Cin = 1kg/m3 and f = 3m3/day

105

As it is shown in Figure 7.3, the curve resembles the values calculated for Cl and K

accumulation in the precipitator ash as seen in the previous chapter. Making the proper unit

conversion and setting the data based on values from Chapter 6, the data obtained from CADSIM

simulations and the data obtained from equation 7.9 have an excellent agreement with

differences not bigger than 1% as indicates Figure 7.4. The time for the system to reach the

steady state concentration as defined in chapter 6 remained at 65 days. Considering that a CSTR

model is being used, it was also calculated the time constant of the process which is

approximately 14 days. This is the time for the concentration to reach 63.2% of its final value

(characteristic of a first order process for t = or 1-e-1).

0

1

2

3

4

5

0 30 60 90 120 150Time (days)

Cl/(

Na+

K),

mol

% in

ash

Mathematical Formula

Kraft Mill Model

Time Constant

Steady StateConcentration

Figure 7.4 – Comparison of CADSIM data for Cl Accumulation Test and Equation 7.9 assuming

C0 = 0, Cin = 4.2kg/m3 and f = 3.5m3/day

It is important to mention that this behavior is verified because the input and output flow rate “f”

are equal and constant and the input concentration is also constant. However, on a real kraft mill

these values would change over time and an analytical solution such as the one obtained here

would not be possible. In that case, a numerical solution to the concentration problem would be

necessary, which in the case of a kraft mill would be provided by the CADSIM model developed

in this work.

106

7.2 Analysis of the Model Variables Now that the general equation describing the concentrations in the system at any time is known a

set of studies can be done to understand the impact of the variables present in the equation on the

concentrations of the substances considered in a system being evaluated. The first question asked

is what would happen over long periods of time with the concentration of a chemical under

study. In order to answer the question, consider that the time is increased (t→) in equation 7.9:

expCexp1CC 0in (7.10)

Since exp(-) = 0 equation 7.10 reduces to:

inCC (7.11)

This means that when t → , the value of C(t) or C() → Cin which physically means that if

enough time is given the mixture originally in the tank will be replaced by the mixture flowing

in, whose concentration is Cin. Furthermore, the concentration at steady state will be directly

proportional to the input concentration provided as the result obtained in section 6.1.2 and shown

in Figure 7.5.

0

2

4

6

8

10

0 30 60 90 120 150Time (days)

Cl/(

Na+

K),

mol

% in

ash

Cl = 2kg/ton pulp

Cl = 1kg/ton pulp

Time to reach steady state

Figure 7.5 – Effect of Input on Cl Accumulation in Precipitator Ash

107

At this point it is interesting to provide some insight into the other three parameters present in

equation 7.9, the rate of flow f, the volume of the tank (or system chemical inventory) V and the

initial concentration C0. Similarly to the previous case, if the value of f is increased, the term

exp(–ft/V) will tend to zero at a faster pace than originally, which makes the concentration at

steady state C() one that can be reached faster. Physically this indicates that the original

mixture is replaced faster by the one flowing in.

With respect to changes in V, if an increase in the volume of the tank is made, the term exp(-

ft/V) will tend to zero at a slower pace and therefore it would take more time to reach the steady

state concentration. It is important to consider that in both cases when f or V is changed the

concentration at steady state remains the same equal to Cin and that f and V are linearly

correlated to time as shown in Figures 6.4 to 6.6 in section 6.1.3.

Finally with regard to C0 three situations may occur according to its value being equal, smaller or

larger than Cin. Going back to equation 7.9 and rearranging it:

VftCCCC in0in /exp (7.12)

If C0 = Cin the concentration in the tank is invariant, since the solution being replaced has the

same concentration and equation 7.12 reduces to:

inin CCVft0CC /exp* (7.13)

If C0 < Cin, then |C0 – Cin| = – (C0 – Cin) = (Cin – C0) therefore:

VftCCCC 0inin /exp (7.14)

Thus, the concentration increases and approaches asymptotically the steady state concentration.

If C0 > Cin, then |C0 – Cin| = C0 – Cin therefore:

VftCCCC in0in /exp (7.15)

The concentration in the tank would decrease and approach asymptotically the steady state

concentration. In order to show the effect of initial concentration, an example is shown below for

a tank of 50 m3 containing an initial concentration of salt C0. This tank starts receiving a solution

108

containing 4kg/m3 of salt at time zero. The solution enters and leaves the tank at the rate of 3

m3/day. The impact of initial concentration in the tank is then evaluated by assuming different

initial values (0, 2, 4, 6 and 8 kg/m3). Figure 7.6 shows the results of the calculations.

0

2

4

6

8

0 20 40 60 80 100 120Time (days)

Con

cent

ratio

n (k

g/m

3 )

Figure 7.6 – Effect of Initial Concentration on CSTR Model

7.3 Summary The major findings shown in this chapter were:

1) The data calculated by the CSTR model presented excellent agreement with the data

obtained by CADSIM given all the simplifications in both cases are applicable.

2) The analysis of the variables confirmed the findings of Chapter 6 and gave an insight

in the behavior of the variables studied. As seen, the steady state concentration is

directly influenced by the input concentration as also obtained by CADSIM. This

result suggests that setting the makeup as close as possible to the desired Na2/S ratio

could be helpful to obtain better control over this parameter. The flow rate and the

soda inventory affect the time to reach steady state and as shown these variables are

linearly correlated to the time to reach steady state as expected from the mathematical

model calculations.

109

8. Conclusions This work focused on the balances of Cl, K, Na and S in the chemical recovery process using a

CADSIM model. Based on the objectives set at the beginning of this document it can be said that

1) The CADSIM model developed was able to simulate processes from a real kraft pulp mill

as demonstrated by the validation process. Furthermore the ash treatment block

developed for the model was able to simulate a real treatment process present in the case

mill used in the validation. The dynamic simulations presented good agreement for the

two cases studied; however tests performed with data from unstable periods of operation

resulted in poor results with the model crashing in most situations.

2) The Na and S balance in the system studied showed that the use of ash treatment reduced

the Na and S requirement from 128 t/d for 100% ash purging to 52 t/d for 100% ash

treatment. The savings with the ash treatment were able to maintain the recovery cycle at

desired sulfidity and alkali stock without the need of extra makeup stream. The use of

R10 ClO2 generator technology allowed a reduction in 24% in effluent sewered.

Furthermore this study also allows the following conclusions:

3) The study on the parameters affecting the Cl and K enrichment factors confirmed that

they are not constant as previously suspected, but the changes are usually small if the

lower bed temperature is not altered. The changes in content of Cl and K in the as-fired

black liquor would not impact extensively the enrichment factors.

4) Regarding the Cl and K balance, the theoretical considerations developed for their

accumulation indicate that the steady state concentration of these ions in the precipitator

ash is directly proportional to the Cl and K input to the mill. The soda inventory affects

the time for the steady state concentration to be reached. The increase in soda inventory

showed a linear correlation with the time to reach steady state. The ash purging and ash

treatment effects on the levels of Cl and K in the precipitator ash are more pronounced

for the first 40% purged or treated; beyond this portion the reductions are small.

110

9. Recommendations

Although the CADSIM program presented very good agreement with the data from the real kraft

mill as shown previously, the result was only true for very stable periods of operation where the

mill was at steady operating condition. Dynamic tests performed under various different

conditions presented in many cases problems with convergence and accuracy. In a few cases the

model crashed due to major discrepancies.

Therefore more studies need to be conducted in order to refine the model capabilities and

provide a more robust program that would be able to simulate a larger number of situations. The

first step should focus on improving the control strategy for the makeup chemicals. Two aspects

should be considered: different input points and different setup controls for the makeup streams.

It is important to highlight that these changes must be performed at slow pace to allow the model

to accommodate the new settings and start converging towards a new steady state. If too many

changes are introduced simultaneously, the discrepancies would build up leading to a crash after

few cycles.

However this process may be very time consuming, the results shown here indicate that these

improvements could make this model even more useful for pulp and paper mills, allowing them

to simulate possible scenarios and plan ahead of time for changes in the process.

111

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119

APPENDICES

120

APPENDIX A - FactSage Software

FactSage is an integrated thermodynamic databank system that provides both tools for managing

of thermochemic data of inorganic substances and tools for thermodynamic calculations. The

program was introduced in 2001 combining two popular software packages: FACT-Win and

ChemSage. This original package was designed to simulate pyrometallurgical processes. With

the release of FACT-Win and then FactSage, the applications have been expanded to include

hydrometallurgy, electrometallurgy, corrosion, glass technology, combustion, ceramic, etc [108].

The FactSage package runs on windows based environment and offers access to various modules

that allow the study of different processes. The modules are divided into three categories: i)

General Info, ii) Databases and iii) Calculations.

General Info

This module provides slide shows of all the modules as well as database documentation, answer

to frequently asked questions and a list of useful addresses and references. In addition, the

module also provides information on interfaces that allow access to the FactSage data and

software such as SimuSage for process modeling, OLI Sage Interface to link to OLI aqueous

databank and METSIM for coupled chemical process simulation.

Databases

In FactSage there are two types of thermochemical databases: pure substances databases and

solution databases. The latter contain the optimized parameters for solution phases. The former

contain the properties of stoichiometric compounds, either obtained from phase diagram

optimization or taken from standard compilations. The main databases are:

FToxid – oxide database for slags, glasses, minerals, ceramics, etc.

FTsalt – salt database contains data for pure salts and salt solutions formed among

various combinations of cations and anions.

FThall – Hall aluminum database contains data for all pure substances and 17

solution phases formed in the system Al-Mg-Na-Li-Ca-F-O.

FThelg – aqueous Helgeson database contains infinite dilution properties for 1400

aqueous solute species taken from the GEOPIG-SUPCRT Helgeson public

database including Helgeson equation of state for temperatures up to 350°C.

121

FTpulp – pulp and paper database containing information regarding combustion

and corrosion applications in recovery boilers including molten salt solutions

generated in the equipment.

Other databases developed for specific uses include alloy databases available upon request:

FScopp (copper alloy database), FSlead (lead alloy database), FSlite (light metal database),

FSstel (steel database) and FSup (ultrapure silicon database) [109].

Calculations

This group of modules is the main part of FactSage. They allow interaction with the databases to

perform calculations to establish thermochemical equilibria and build phase diagrams of systems

in a multitude of formats. The main modules are: Reaction, Predom, EpH, Equilib, OptiSage and

Phase Diagram.

The Reaction module calculates changes in extensive thermochemical properties (H, G, V, S, Cp,

A) for single species, a mixture of species or for a chemical reaction. The Predom module can

calculate and plot isothermal predominance area diagrams for one, two or three-metal systems

using data retrieved form the compound databases. The EpH module is similar to the Predom

module and permits one to generate Eh vs. pH (Pourbaix) diagrams for one, two or three metal

systems from the compound databases.

The Equilib module is the Gibbs energy minimization module in FactSage. It calculates the

concentrations of chemical species when specified elements or compounds react or partially react

to reach a state of chemical equilibrium. The module allows different choice of units, presence of

dormant phases, equilibria constrained with respect to T, P, V, H, S, G or Cp and user-specified

compound and solution data. A variety of calculations modes are also available including phase

targeting, composition targeting, phase transition, open systems with product removal.

The OptiSage module applies the CalPhad approach to generate a consistent set of Gibbs energy

parameters from a given set of experimental data using known Gibbs energy data from well-

established phases of a chemical system. Finally, the Phase Diagram module permits one to

calculate, plot and edit multicomponent phase diagram sections where the axes can be

combinations of T, P, V, composition, activity, chemical potential, etc.

122

APPENDIX B - OLI Software

The OLI Software is a commercial package used to calculate thermodynamic properties of

aqueous systems. Its unique predictive capability is based on its big databank, a thermodynamic

framework, and equation solvers.

The OLI databank consists of pertinent information of 79 inorganic elements and respective

aqueous species and over 3000 organic compounds, with the possibility to incorporate

supplementary databank. It is based on published experimental data for many chemical mixtures

over a wide range of temperature (–50 - 300ºC), pressures (0 - 1500 bar), and ionic strength (0 -

30 molal).

The OLI Software consists of a Stream Analyzer, a Lab Analyzer and a Corrosion Analyzer with

different capabilities. All of them can access the databank, the thermodynamic framework and

the equation solvers; however they are designed for different purposes.

The Stream Analyzer allows single stream calculations, being able to separate different phases or

mix several streams to create a new one. The Lab Analyzer deals with water analyses and can

reconcile the charge unbalance and the pH of the solution. The Corrosion Analyzer is a special

package to evaluate corrosion of alloys and metals under many different environments.

Although the databank covers most of the common chemicals found in the industry, specific

cases involving high concentrated multicomponent systems, particularly at high temperature can

present limited or no data at all. Thus, estimation techniques are used in the predictions which

can produce significant errors. They occur for systems at high temperatures (around 100ºC and

above) and result of the use of polynomial fits to the thermodynamic property formulations of

the Helgeson Equation of State. This Equation of State and the supporting databank permits OLI

to obtain equilibrium constants and other standard state molal thermodynamic properties for

different systems.

The expression involving the main partial molal properties (GI, H, S, Cp and V) is

Mi = Moi + ME

I (B.1)

where M is any thermodynamic property, o refers to the standard state term and E refers to the

excess term.

123

In the equation (B.1), OLI uses the Helgeson et al framework to obtain the standard state term,

and use the frameworks of Bromley, Zemaitis, Pitzer, Debye-Huckel and others to obtain the

excess term.

The standard state term can be described as:

Mo = M(T,P,, c1,c2,a1,a2,a3,a4) (B.2)

where , c1,c2,a1,a2,a3,a4 are Helgeson parameters and are specific to each species.

The excess term are connected to the determination of activity coefficients, which describe the

non-ideality of the solution and have the general form of

= DH (I) + BZ (I,T,m) (B.3)

The term DH is known as the Debye-Huckel expression for long-range electrostatic interactions.

This term describes the activity coefficients of dilute systems:

21

212

1 IIzA

DH ii

(B.4)

where A is the Debye-Huckel coefficient and varies with temperature and solvent properties, zi is

the number of charges of the ions and I is the ionic strength.

The BZ term in equation B.3 is the Bromley-Zemaitis expression for short-range interactions:

NO

jjijijij

ji

iiijjii mIDICB

zzI

zzBzzBZ

1

22

2

/5.11

6.006.02

(B.5)

where NO is the number of ions with opposite charges to “i”, Bij, Cij, Dij are empirical

temperature-dependent parameters for cation-anion interactions and mj is the molality of the

species j.

For other interactions such as ion-molecule and molecule-molecule, OLI uses other models.

124

Appendix C - Preparation of Black Liquor Samples

A glass jar containing black liquor was placed in a water bath at 90°C. The liquor was then

stirred and small amounts are poured into a stainless steel tray to form a thin sheet, in order to

increase the surface area and facilitate the drying process. The sample was then placed in an

oven controlled at 135°C for 24 hours. After that the sample was crushed and placed back in the

oven for another 24 hours. Finally, the sample was crushed again and sieved to a desired particle

size range (<90 μm).

Figure C.1 – Black Liquor Sample Homogenization

Figure C.2 – Black Liquor Sample Drying

125

Appendix D - Calculation Routine for Multiple Effect Evaporators

The material balance calculation for a multiple effect evaporation plant is special for the

following reasons:

The liquor concentration and also the steam conditions change through the plant. This

influences the characteristics of the liquor so that each effect must be calculated with

different parameters.

The boiling point rise has a considerable influence on the heat transfer, since it defines

the available effective temperature difference.

In a multiple effect plant, the total temperature difference is split between the effects

according to various parameters.

The calculation employed for most simulators including CADSIM is iterative by its

nature. It is extremely elaborate to make manually especially when trying for high

precision. An easier way to explain the calculations is using a spreadsheet approach.

The following gives a typical sequence for the calculations in an evaporation plant.

Given parameters:

Dry solids flow; Liquor concentration into the evaporation plant;

Number of stages; Liquor temperature into the evaporation plant;

Steam pressure and temperature into the evaporation plant;

Warm water temperature from the surface condenser;

Liquor concentration from the evaporation plant;

Define the concentrations:

Inlet and outlet concentrations are given. This defines the total plant evaporation.

Assume that the evaporation in each stage is the same (for the first approximation). This gives

the evaporation in each stage and consequently the concentrations.

Define the liquor characteristics in all the stages:

Boiling point rise; Specific Heat; Heat transfer coefficients;

126

Define the effective temperature difference in all the stages:

The live steam saturation temperature and the cooling water outlet temperature define the

total temperature difference.

Pressure and temperature differences due to piping losses are defined if available. Most

important are to define the pressure loss of live steam before the first effect and the temperature

difference in the surface condenser.

When the effective temperature difference is defined, this is distributed to the different stages

with the method described in detail below.

Prepare the heat balance for each stage:

The main heat flow is in the steam or vapor transferred from one effect to another. The concepts

used in the evaporation calculations have the following definitions:

Total temperature difference = Total Δt = live steam saturation temperature of the heating

element of the first effect minus condensing temperature in the surface condenser.

Total effective temperature difference = t = total thermal driving force. It is calculated as the

total t minus sum of boiling point rises minus sum of temperature drops due to pressure losses.

The total effective t for multiple effect evaporation is the sum of the effective t of the effects:

t = t1 + t2+….+tn

For each effect, the general heat transfer formula is valid:

Q = U x A x t or t = Q / (U x A)

For the evaporator train, the equations for each effect would be:

t1 = Q1 / (U1 x A1), t2 = Q2 / (U2 x A2), t3 = Q3 / (U3 x A3), tn = Qn / (Un x An)

To solve this equation group, the relations between the effects are formed by dividing the

equations so that all are expressed as function of t1:

t2 = t1 x (Q2 x A1 x U1) / (Q1 x U2 x A2), t3 = t1 x (Q3 x A1 x U1) / (Q1 x U3 x A3)

The effective temperature difference for effect 1 can then be recalculated:

nn1

11n

331

113

221

1121

x UA x Qx UA x Q

.....x UA x Qx UA x Q

x UA x Qx UA x Q

1

tt

127

When t1 has been determined, the temperature difference for the other effects can be calculated

with the formulas for each t.

Figure D.1 – Schematic Diagram of Evaporator Body

Based on the diagram presented in Figure D.1, when mass in = mass out and heat in = heat out

the following equations are true:

mCo = mCi + mVi mLi = mLo + mVo

mVi hVi + mCi CpWi tCi + mLi CpLi tLi = mVo hVo + mCo CpWo tCo + mLo CpLo tLo

An Excel spreadsheet shows the calculation for three-stage evaporation. The basic design data

are indicated below. The calculation starts by assuming the evaporation in the first stage. This

then determines the heat energy available in the following effects. The characteristics of the

liquor and vapor are calculated for each stage. A flashing of the condensate from effects 2 and 3

has been considered. This is a simplified calculation. It does not consider internal pressure losses

between the effects or the losses of non-condensable gases that vent from the evaporation.

ASSUMPTION CALCULATION It is necessary to assume for the first iteration Once the new t is calculated it is inserted in that the evaporation in each body is equal and the respective places (D46, D47 and D48) and that the effective t is the same in each body. the new evaporation either (D16 and D17), the procedure is repeated until convergence.

128

SIMPLIFIED EVAPORATOR CALCULATION Basic Design Data Unit Value Formula

Number of Effects - 3 Input Value

Liquor Flow Pattern - - Effect 3 -> Effect 2 -> Effect1

Black Liquor Inlet Concentration - 0.2 Input Value Black Liquor Outlet Concentration - 0.5 Input Value Black Liquor Inlet Temp. Effect 3 °C 70 Input Value Evaporation Capacity kg/s 30 Input Value Live Steam Saturation Temp. to Effect 1 °C 120 Input Value Saturated Vapor Temp. out Effect 3 °C 60 Input Value

Dry Solids Calculation Dry Solids Flow kg/s 10 D9/(1/D6-1/D7) Evaporation Effect 1 kg/s 10 Input Value Effect 2 kg/s 9.56 Input Value Effect 3 kg/s 10.37 Input Value Total Evaporation kg/s 30 SUM(D15:D17) Liquors Flow Effect 1 out kg/s 20 D13/D7 Effect 2 out kg/s 30 D20+D15 Effect 3 out kg/s 39.56 D21+D16 Effect 3 in kg/s 49.93 D22+D17

Liquor Concentrations Effect 1 out - 0.5 D13/D20 Effect 2 out - 0.333333 D13/D21 Effect 3 out - 0.252781 D13/D22 Effect 3 in - 0.20028 D13/D23

Boiling Point Rise Effect 1 °C 8.628671 Effect 2 °C 4.256696 Effect 3 °C 2.702241

Liquor Temperatures Effect 1 out °C 105.6 D10-D46 Effect 2 out °C 81.91133 D52-D47 Effect 3 out °C 62.72463 D53-D48 Effect 3 in 70 Input Value

Heat Transfer Coefficient Effect 1 kW/m2°C 1.2 Input Value Effect 2 kW/m2°C 1.6 Input Value Effect 3 kW/m2°C 2 Input Value

Boiling Point Rise Calculation BPR = 6.173*D25-7.48*D25*(D25)^.5+32.747*(D25)^2 BPR = 6.173*D26-7.48*D26*(D26)^.5+32.747*(D26)^2 BPR = 6.173*D27-7.48*D27*(D27)^.5+32.747*(D27)^2

129

SIMPLIFIED EVAPORATOR CALCULATION Effective t Calculation

Total available t °C 60 D10-D11 Sum of BPR °C 15.58761 D30+D31+D32 Total Effective t °C 44.41239 D43-D44 Effective t, Effect 1 °C 14.4 D45/3 Effective t, Effect 2 °C 15.06 D45/3 Effective t, Effect 3 °C 14.93 D45/3 Check Sum 44.39 SUM(D46:D48)

Vapor Saturation Temperatures

Live Steam Saturation °C 120 Input Value Effect 1 °C 96.97133 D34-D30 Effect 2 °C 77.65463 D35-D31 Effect 3 °C 60.02239 D36-D32

Vapor Enthalpy Live Steam kJ/kg 2704 Input Value Effect 1 kJ/kg 2661 Input Value Effect 2 kJ/kg 2631 Input Value Effect 3 kJ/kg 2609 Input Value

Condensate Enthalpy Live Steam kJ/kg 503 Input Value Effect 1 kJ/kg 384 Input Value Effect 2 kJ/kg 307 Input Value Effect 3 kJ/kg 251 Input Value

Liquor Specific Heat Effect 1 kJ/kg, °C 3.292643 Cp1 Effect 2 kJ/kg, °C 3.539172 Cp2 Effect 3 kJ/kg, °C 3.669803 Cp3 Inlet Liquor kJ/kg, °C 3.775786 Cp4

Energy in Liquor Effect 1 out kJ/s 6954.062 D20*D34*D67 Effect 2 out kJ/s 8696.949 D21*D35*D68 Effect 3 out kJ/s 9106.199 D22*D36*D69 Inlet Liquor kJ/s 13196.75 D23*D37*D70

Energy Demand Effect 1 kJ/s 24867.11 D15*D58+D72-D73 Effect 2 kJ/s 24114.47 (D15+D89)*(D58-D63) Effect 3 kJ/s 22477.62 (D82+D90)*(D59-D64)

New Evaporation Effect 1 kg/s 10 D15 Effect 2 kg/s 9.321066 (D78+D74-D73)/D59 Effect 3 kg/s 10.18328 (D79+D75-D74)/D60

Vapor Production / Demand

Effect 1 kg/s 11.2981 D77/(D57-D62) Effect 1 to Effect 2 kg/s 10.59046 D81+D89 Effect 2 to Effect 3 kg/s 9.671954 D82+D90 Cp1 = 4.216*(1-D25)+(1.675+(3.31*D34)/1000)*D25+(4.87-20*D34/1000)*(1-D25)*(D25)^3 Cp2 = 4.216*(1-D26)+(1.675+(3.31*D35)/1000)*D26+(4.87-20*D35/1000)*(1-D26)*(D26)^3 Cp3 = 4.216*(1-D27)+(1.675+(3.31*D36)/1000)*D27+(4.87-20*D36/1000)*(1-D27)*(D27)^3 Cp4 = 4.216*(1-D28)+(1.675+(3.31*D8)/1000)*D28+(4.87-20*D8/1000)*(1-D28)*(D28)^3

130

SIMPLIFIED EVAPORATOR CALCULATION Condensate Flash

Effect 1 to Effect 2 kg/s 0.590458 D85*(D62-D63)/(D58-D63) Effect 2 to Effect 3 kg/s 0.350889 D86*(D63-D64)/(D59-D64)

Heat Transfer Area Effect 1 m2 1040 Input Value Effect 2 m2 1040 Input Value Effect 3 m2 1040 Input Value

Heat Transfer Capacity Effect 1 kJ/s 17971.2 D39*D46*D92 Effect 2 kJ/s 25059.84 D40*D47*D93 Effect 3 kJ/s 31054.4 D41*D48*D94

New t

Effective t, Effect 1 °C 23.94307 D45/(1+(D83*D97*D39)/(D82*D98*D40)+(D84*D97*D39)/(D82*D99*D41))

Effective t, Effect 2 °C 12.00343 D100*(D82*D96*D39)/(D81*D97*D40) Effective t, Effect 3 °C 8.46589 D100*(D83*D96*D39)/(D81*D98*D41) Check Sum 44.41239 SUM(D100:D102)

131

Appendix E - Calculation Routine for Recovery Boilers

This appendix shows the calculations involved in the material and energy balance of a generic

kraft recovery boiler. The main functions of this equipment are: to recover inorganic chemicals

used to cook wood and to make use of the chemical energy in the organic portion of the liquor to

generate steam for the mill. The material and energy balances for recovery boilers are usually

complex due to the multiplicity of input and output streams, the chemical reactions occurring

within these units and the lack of consistent measuring methods for the reporting parameters.

In order to achieve a reasonable evaluation of a hypothetical recovery boiler, it will be assumed

the boundary for the material and energy balance as established in Figure E.1. As it is shown the

main input parameters are black liquor (fuel containing organic matter originated from dissolved

wood and inorganic chemicals used in the cooking process), air (needed for combustion), feed

water (to generate steam) and makeup chemicals (necessary to optimize process conditions). The

main outputs consists of smelt (inorganic portion of the black liquor), stack gas (flue gases from

combustion), steam (used in the process and also used to generate electricity). However other

inputs and outputs are present in recovery boilers, the ones assigned in Figure E.1 are the most

important for evaluation purposes.

Another important assumption concerns the chemical reactions taking place in the recovery

boiler. As mentioned before the black liquor contains an organic and an inorganic portion. This

will result in two different processes when the fuel is inserted in the boiler to burn; one is the

well known general reaction for combustion of organic matter as in equation 1:

Organic Matter (C,H,O) + O2 → CO2 + H2O (1)

The other reactions involve the inorganic portion of the black liquor and require individual

evaluation for the material balance to be properly addressed. They can be represented by the

general reaction as shown in equation 2 and contains the main components of the smelt stream

leaving the bottom of the recovery boiler:

Inorganic Matter (C, O, Na, S, K, Cl) + O2 → Na2S + Na2SO4+ NaCl + Na2CO3 + K2CO3 (2)

132

Figure E.1 – Major Inputs and Outputs in a Kraft Recovery Boiler

Table E.1 provides the necessary information regarding the black liquor which is the fuel burned

in recovery boilers. However black liquor composition varies from mill to mill as well as over

time, a typical composition is assumed here based on average data from different sources. In

order to simplify the calculations assume an input to the boiler of 100 kg of black liquor solids.

Table E.2 indicates the composition of the makeup chemical used in the recovery boiler. Finally,

Table E.3 gives the operating conditions for the equipment. Although these values can vary

significantly from one boiler to another, these numbers are considered consistent for most

recovery boilers operating under normal conditions.

Table E.1 – Sample Analysis of kraft black liquor Element wt% Black Liquor Solids (BLS)

Carbon C 35% Hydrogen H 3.30% Oxygen O 35.70% Sodium Na 19.70% Potassium K 1.60% Sulfur S 4% Chloride Cl 0.60% Inerts Si, Al, etc 0.10% Solids Sol 70% Higher Heat Value HHV (kJ/kg BLS) 13950 Net Heat Value NHV (kJ/kg BLS) 12093 Assumed Reduction 90%

133

Table E.2 – Sample Analysis of Makeup Chemicals Element wt% Sodium Na 32.40% Sulfur S 22.40% Oxygen O 44.80% Chloride Cl 0.30% Inert Si, Al, etc 0.10%

Based on data available in Tables E.1 to E.3, the material and energy balance calculation will be

performed for a Kraft recovery boiler using an Excel spreadsheet.

Table E.3 – Sample Operating Parameters for a kraft recovery boiler Item Units Air Excess forced draft air 6% Stoichiometric Infiltration air 9% Stoichiometric Total excess air 15% Stoichiometric Ambient temperature 27 ºC Humidity in air 0.013 kg H2O/kg dry air FD air preheat temp. 150 ºC Black Concentration Solids 70% liquor Temperature 108 ºC Salt cake makeup 3% wt. of BLS Direct heating liquor 1.7% wt. of wet BL Dust recycle 10% wt. of initial Na Smelt Smelt reduction eff. 90% Organic C in smelt 1% wt. of smelt Temperature 850 ºC Enthalpy 1350 kJ/kg Stack Exit gas temperature 210 ºC gas TRS as H2S 15 ppmv dry flue gas SO2 75 ppmv dry flue gas CO 200 ppmv dry flue gas H2 200 ppmv dry flue gas Particulate 0.1 g/DSCM Na2SO4 particulate 80% wt as Na2SO4 Na2CO3 particulate 8% wt as Na2CO3 K2SO4 particulate 7.3% wt as K2SO4 K2CO3 particulate 0.7% wt as K2CO3 NaCl particulate 4% wt as NaCl Na % in particulate 31% S % in particulate 19.4% Stream Blowdown flow 11% of BLS Circuit Sootblowing steam 18% of BLS Feed water temp. 110 ºC Steam temperature 482 ºC Steam pressure 62.25 Bar Enthalpy of steam 3372 kJ/kg

134

Material Balance

In order to calculate the material balance, it will be assumed that complete combustion takes

place and minor losses are ignored. Thus, the first step is to consider the input as 100 kg of black

liquor dry solids. Since the heavy black liquor injected in the boiler has a 70% dry solids content

the total input to the boiler can be obtained by:

Black Liquor Input = (100*100)/70 = 142.86 kg

From Table E.3 it is also known that the saltcake makeup is 3% of the black liquor solids flow,

which gives an input of 3 kg. Therefore, at this point two streams are already identified for the

material balance as shown in Figure E.2.

Figure E.2 – Partial Material Balance.

The next step is to obtain the amount of smelt generated. As discussed in the introduction the

smelt consists mainly of the inorganic portion of the black liquor that will be recovered by the

kraft recovery process. It is a mixture of fused salts which constituents are described in equation

2. In order to properly account for each of the inorganic salts, individual reactions will be

considered for the material balance. Considering the first chemical given in the equation 2, Na2S,

the simplest reaction leading to its formation would be described by equation 3:

2Na + S → Na2S (3)

Molecular Weights 46 g + 32 g = 78 g

135

Therefore the amount of Na2S can be calculated multiplying the total amount of black liquor

solids entering the boiler (100 kg) by the amount of sulfur content in the liquor (4%) and the

smelt reduction efficiency of the boiler (90%). The smelt reduction efficiency indicates how

much of the sulfur exits the boiler in the reduced form, or in other words as Na2S. This

calculation gives the output of sulfur, but not Na2S. But by the equation above and the respective

molecular weights of S and Na2S, it is indicated that each 32g of S will generated 78g of Na2S,

since Na is in excess in the boiler. Thus the amount of Na2S from the BLS is

Na2S from BLS = 100 * 0.04 * 0.9 * (78/32) = 8.78 kg/100kg BLS

Since the makeup also contains Na and S, part of the Na2S can be generated by reactions in the

boiler, thus to account for the makeup the calculation would include the rate of makeup input

that is 3% and the sulfur content in the makeup (22.4%) calculation becomes:

Na2S from makeup = 100 * 0.224 * 0.9 * 0.03* (78/32) = 1.47 kg/100kg BLS

Finally the total amount of Na2S is the sum of both amounts that is:

Na2S total = 10.25 kg/100kg BLS

The second chemical is Na2SO4 and it results from an incomplete reduction of the sulfur in the

boiler. Similarly a simplified equation to its generation is given by equation 4:

2Na + S + 2O2 → Na2SO4 (4)

Molecular Weights 46 g +32g+ 64g = 142 g

The amount of Na2SO4 can be calculated similarly to the previous situation, however considering

not the reduction efficiency, but the remaining sulfur (100-90% or 10%). Once obtained the

sulfur not reduced from the BLS the amount of Na2SO4 is calculated considering that 32g of S

can produce 142 g of Na2SO4. This will give:

Na2SO4 from BLS = 100 * 0.04 * (1-0.9) * (142/32) = 1.78 kg/100kg BLS

Considering the portion that comes from the makeup as in the previous situation, the calculation

will be:

Na2SO4 from makeup = 100 * 0.224 * (1-0.9) * 0.03* (142/32) = 0.30 kg/100kg BLS

136

Therefore the total amount of Na2SO4 is given by the sum of both:

Na2SO4 = 2.08 kg/100kg of BLS

The next chemical considered in the equation 2 is NaCl. Similarly to the previous cases part of it

is generated from the BLS and part from the makeup chemicals. Considering the BLS the

calculation, equation 5 accounts for NaCl generation:

Na + Cl → NaCl (5)

Molecular Weights 23g + 35.5g = 58.5g

Then the amount of NaCl is obtained by multiplying the black liquor solids input (100 kg) by the

content of Cl (0.6%) and the proportion of Cl per each NaCl generated (58.5/35.5), then

NaCl from BLS = 100 * 0.006 * (58.5/35.5) = 0.99 kg/100kg BLS

To calculate the amount inserted with the makeup it is considered in the calculation the makeup

amount and the content of Cl in it (0.3%):

NaCl from makeup = 100 * 0.003 * 0.03 * (58.5/35.5) = 0.01 kg/100kg BLS

And the total amount is again the sum of both amounts:

NaCl total = 1.00 kg/100kg of BLS

The next chemical is Na2CO3. Based on measurements the remaining Na available in the smelt

consists of this chemical. In order to obtain the amount of Na2CO3 it is necessary to consider the

equation 6:

4Na + 2C + 3O2 → 2Na2CO3 (6)

Molecular Weights 46g + 12g + 48g = 106 g

Thus the total amount of Na2CO3 is obtained by subtracting the total amount of Na minus the

amounts contained in the other chemicals already calculated. The total amount is given by the

flow of black liquor solids (100 kg) multiplied by the Na content in the BLS (19.7%) minus the

amounts with Na2S, Na2SO4 given by the sulfur content multiplied by the (46/32) proportion and

NaCl given by the chloride content multiplied by the (23/35.5) proportion. Thus

137

Remaining Na = (100) * (0.197 – (46/32) * 0.04 – (23/35.5) * 0.006)

This calculation will provide the amount of sodium not of sodium carbonate. To obtain the

sodium carbonate we must consider the equation 6, where each 46g of sodium generates 106 g of

sodium carbonate, then:

Na2CO3 = (100) * (0.197 – (46/32) * 0.04 – (23/35.5) * 0.006) * (106/46) = 31.25 kg/100kg BLS

The last chemical in the equation 2 is K2CO3 and the amount in the smelt is obtained by the

content of K in the black liquor dry solids. Therefore the calculation is done by multiplying the

total black liquor dry solids flow (100 kg) by the potassium content (1.6%) and the equation 7 for

potassium carbonate generation:

4K + 2C + 3O2 → 2K2CO3 (7)

Molecular Weights 78.2g + 12g + 48g = 138.2g

Thus the potassium carbonate amount is:

K2CO3 = 100 * 0.016 * (138.2/78.2) = 2.83 kg/100kg BLS

Finally the inerts can be considered for a complete assessment of the smelt components. Its

amount is calculated by multiplying the black liquor dry solids flow and the inert content and

adding with the makeup flow multiplied by its inert content. Thus:

Inerts = 100 * 0.001 + 100 * 0.03 *0.001 = 0.103 kg/100kg BLS

With these calculations it is possible to obtain the flow of smelt out of the boiler and its

composition based on simplifications in the reactions considered. According to experimental data

from different sources, the results are close to what is a common composition for recovery boiler

smelt.

Total Smelt Flow = 10.25 + 2.08 + 1.00 + 31.25 + 2.83 + 0.10 = 47.51 kg/100kg BLS

Figure E.3 shows the calculations done till now. The next step is to calculate the combustion air

stream based on the oxygen requirement for stoichiometric combustion of the organic portion of

the black liquor dry solids. With that the amount of air necessary can be calculated.

138

Figure E.3 – Partial Material Balance.

Based on equation 1 it will be possible to obtain how much CO2 is generated by the boiler due to

the combustion of the black liquor dry solids. However it is important to remember that part of

the carbon is in the form of Na2CO3 and K2CO3 and this must be accounted for. Then the total

amount of carbon entering the recovery boiler can be calculated by multiplying the BLS flow

(100 kg) by the carbon content (35%) and subtracting the amounts in the sodium and potassium

carbonate. The calculation is:

Remaining C = 100 * 0.35 – (12/106) * 31.25 – (12/138.2) * 2.83

In order to obtain the amount of CO2 generated we must consider that each 12 g of carbon will

produce 44 g of carbon dioxide. Thus we must multiply the numbers above by the ratio (44/12)

to obtain the amount of CO2:

CO2 = (100 * 0.35 – (12/106) * 31.25 – (12/138.2) * 2.83) * (44/12) = 114.46 kg/100kg BLS

Equation 1 also accounts for the generation of H2O. The amount generated in the combustion is

obtained by multiplying the amount of black liquor dry solids flow (100 kg) by the hydrogen

content in the black liquor (3.3%) and the proportion (18/2) remembering that each 2g of

hydrogen will generate 18 g of water under the proper combustion conditions. Thus the

calculation results:

H2O = 100 * 0.033 * (18/2) = 29.70 kg/100kg BLS

139

Now based on the amounts of chemicals generated by the organic and inorganic reactions in the

recovery boiler, it is possible to estimate the stoichiometric oxygen requirement for the boiler.

This is done based on the equations 1 through 7 given above. Thus the oxygen needed for

sodium sulfate generation is calculated by multiplying the amount of Na2SO4 formed (2.08 kg)

by the amount of oxygen needed according to the equation 4 (64/142). Similar calculation is used

for the other chemicals (Na2CO3, K2CO3, CO2 and H2O):

O2 needed for Na2SO4 formation = 2.08 * (64/142) = 0.93 kg/100kg BLS

O2 needed for Na2CO3 formation = 31.25 * (48/106) = 14.15 kg/100kg BLS

O2 needed for K2CO3 formation = 2.83 * (48/138.2) = 0.98 kg/100kg BLS

O2 needed for CO2 formation = 114.46 * (32/44) = 83.24 kg/100kg BLS

O2 needed for H2O formation = 29.70 * (16/18) = 26.40 kg/100kg BLS

Once all the oxygen needed is obtained the total required is the sum of all individual reactions:

Total O2 required = 0.93 + 14.15 + 0.98 + 83.24 + 26.40 = 125.7 kg/100kg BLS

In order to calculate the total air flow necessary it is important to consider that some oxygen

enters with the black liquor as well as with the makeup chemicals. Thus this amount has to be

calculated. To do that it is multiplied the flow of BLS (100 kg) by the oxygen content in the BLS

(35.7%) and added with the amount of makeup flow multiplied by the oxygen content in the

makeup. Thus the calculation is:

Oxygen in BLS and Makeup = 100 * 0.357 + 100 * 0.03 * 0.448 = 37.04 kg/100kg BLS

Considering the amount in the BLS and makeup are already fed to the boiler, the remaining

oxygen required need to be supplied by the combustion air stream. This is given by:

Oxygen required from Air = 125.7 – 37.04 = 88.66 kg/100kg BLS

Assuming that the composition of the air is 23.2% of O2 and 76.8% of N2, the stoichiometric

amount of air need can be obtained by:

Stoichiometric Amount of Air = 88.66/0.232 = 382.16 kg/100kg BLS

Nitrogen in combustion Air = 382.16 * 0.768 = 293.53 kg/100kg BLS

140

To identify the amount of air admitted to the boiler, it is important to consider that the required

combustion air is driven to the interior of the boiler by induced draft fans to provide the

temperature and amount needed during different operating conditions of the boiler. At normal

operating conditions the system provides an amount of air in excess of 6% of the stoichiometric

amount. Thus the total forced draft air is:

Excess air = 382.16 * 0.06 = 22.93 kg/100kg BLS

Total Forced Draft Air = 382.16 + 22.93 = 405.09 kg/100kg BLS

Finally it is necessary to consider that an equipment of the size of a recovery boiler has

numerous leaks that permit a significant amount of air to infiltrate in the boiler. Most boiler

manufacturers agree that this number varies greatly, but an acceptable estimate would be around

9% of the stoichiometric air. Thus the infiltration air is:

Infiltration air = 382.16 * 0.09 = 34.40 kg/100kg BLS

Total Excess Air = 22.93 + 34.40 = 57.33 kg/100kg BLS

This will lead to the total air flow that is:

Total Air Flow = 382.16 + 57.33 = 439.49 kg/100kg BLS

Figure E.4 contains the calculations done till now. The last stream to be calculated to complete

the material balance is the flue gas that is generated by the recovery boiler. To calculate that

stream it is necessary to consider some other information. Some water enters with the black

liquor, since the heavy liquor contains 70% dry solids, being the remaining amount water. This

water will come out with the flue gases. Also the air that enters the boiler has some humidity that

has to be accounted for the flue gas stream. Finally part of the steam generated in the boiler is

used in devices called sootblowers to keep the surfaces of the heat exchange tubes in the upper

boiler clean. The steam used will end up as part of the flue gases.

Thus considering the water entering with the black liquor, the steam in stack will calculated

considering the amount of black liquor dry solids (100 kg) multiplied by the water content (30%)

divided by the dry solid concentration (70%), giving:

Steam from black liquor = 100 * (1-0.7) / 0.7 = 42.86 kg/100kg BLS

141

For the humidity of air, it is given an estimate of 0.013 kg of H2O per kg of dry air, which would

result in an amount of water equal to:

Humidity in Air = 439.49 * 0.013 = 5.71 kg/100kg BLS

Figure E.4 - Partial Material Balance

The last addition to the flue gases stream comes from the steam released by the sootblowers in

the upper boiler. Based on the average consumption of the sootblowers in different boilers an

estimate was given in Table E.3. Using the amount of black liquor dry solids entering the boiler

the amount of steam consumed can be calculated:

Sootblowing Steam = 100 * 0.18 = 18 kg/100kg of BLS

The total water vapor added to the flue gases is then:

Total Water Vapor = 42.86 + 5.71 + 18 = 66.57 kg/100kg BLS

With this information the total stack gas can be found by the addition of the total of gases

generated in the combustion process (CO2 and H2O), the total air flow and the total water vapor

entering the flue gas stream, thus:

Total Stack Gas = 114.46 + 29.70 + 439.49 + 66.57 = 650.22 kg/100kg BLS

This closes the material balance and the overall look for the recovery boiler balance is seen in

Figure E.5:

142

Figure E.5 – Complete Material Balance for Recovery Boiler Considered

Energy Balance

In order to calculate the energy balance for the recovery boiler it will be used the Heat Loss

method. This method is based on calculating the total heat input to the equipment by adding all

energy input from the different streams. After the heat output of all streams are also calculated as

well as the energy consumed by any chemical reaction. The difference in the heat input and

output is the energy left to be transferred to any fluid or media present in the equipment.

For the case of the recovery boiler the heat input consists of the energy entering with the black

liquor, the combustion air and the feed water. The heat output consists of the losses in the flue

gases, smelt, energy to form sulfide, evaporate water in the black liquor, sootblowing and

blowdown steam and radiation losses.

Since the heating value for black liquor is usually reported in kJ/kg of BLS, the energy balance

will be calculated on a 1 kg basis for black liquor and not for a 100 kg basis. Therefore:

Heating Input for 1 kg of Black Liquor Solids = 13950 kJ/kg

The sensible heat entering the boiler with the black liquor solids is calculated by multiplying the

amount of black liquor (1kg/70% solids content), the heat capacity (2.65 kJ/kg*°C) and the

temperature difference. It is important to notice that the reference temperature is considered 25°C

and the liquor entrance temperature is 108°C, thus:

143

Sensible Heat in Black Liquor = (1/0.7) * 2.65 * (108-25) = 314 kJ/kg

The air also contains sensible heat and it is calculated similarly, considering the total flow of air

calculated previously and a heat capacity of 1.006 kJ/kg*°C, thus:

Sensible heat in Air = (439.49/100) * 1.006 * (27-25) ≈ 9 kJ/kg

The forced draft air in most recovery boilers is preheated before entering the combustion

chamber, which makes necessary to consider this condition in the energy balance. This

calculation is similarly to the previous one just considering only the forced draft air and a new

temperature difference, which results:

Heat to preheat air = (405.09/100) * 1.006 * (150-27) = 501 kJ/kg

The next streams to be considered are the sootblower that is used to remove deposits that may

build up in the tube banks in the heat exchange area. The sootblower uses steam that comes from

the feedwater for the boiler. The amount of heat in the feedwater for the sootblowers is

calculated multiplying the flow of sootblowing steam (18 kg/100kg BLS) by the heat capacity of

water (4.19 kJ/kg*°C) and the temperature difference.

Heat in the feedwater for sootblowing steam = (18/100) * 4.19 * (110-25) = 64 kJ/kg

Finally it is necessary the blowdown stream that is the amount of low pressure steam that is

distributed to the mill for different purposes. The calculation is similar to the previous one since

the flow of blowdown is already known (11% of BLS flow)

Heat in the feedwater for blowdown = 0.11 * 4.19 * (110-25) = 39 kJ/kg

Once all input heat is obtained the total input heat is obtained by adding the all the input values:

Total Heat Input = 13950 + 314 + 9 + 501 + 64 + 39 = 14878 kJ/kg of BLS

To complete the energy balance the heat output from all streams as well as the energy consumed

by the chemical reactions has to be considered. Thus to start the sensible heat that is lost with the

dry flue gas should be calculated. It is done by considering the total amount of dry flue gas (CO2

+ N2 + O2) and a heat capacity of the mixture equal to 1.02 kJ/kg.

Sensible Heat in Flue Gas (dry) = (114.46 + 439.49)/100 * 1.02 * (210-25) = 1045 kJ/kg

144

Since in the combustion process water vapor is generated the calculation must to be done

separately since the heat capacity of the water is bigger than the dry flue gas. In this case it will

be considered the heat capacity constituted of one term regarding the latent heat of vaporization

(2770 kJ/kg*°C) and a steam heat capacity of 1.98 kJ/kg*°C at a reference temperature of 200°C.

Then the calculation for the heat loss from the water generated during the combustion is:

Heat loss from water formed = (29.70/100) * (2770 + 1.98 * (210-200)) = 829 kJ/kg

Other source of heat loss is the water that enters with the black liquor solids, since its content is

70%, being the remaining water. Therefore the heat loss with the steam from evaporating this

water is given by:

Heat loss from water in the black liquor = ((1/0.7) – 1) * (2770 + 1.98 * (210-200)) = 1196 kJ/kg

Considering now the smelt stream, it is necessary to find the heat loss with the fused salts leaving

the boiler. Considering the given enthalpy of smelt the calculation becomes:

Sensible heat in smelt = (47.51/100) * 1350 = 641 kJ/kg

The most important chemical reaction that consumes energy in the recovery boiler is the

reduction of the sulfur compounds to sodium sulfide (Na2S). This reaction consumes 12900

kJ/kg for the average boiler conditions. Considering the amount of Na2S formed the heat

consumed can be estimated by:

Heat to form sulfide = (10.25/100) * 12900 = 1322 kJ/kg

Another source of heat loss is the steam leaving the boiler from the sootblower use and the

blowdown. They are calculated similarly to the previous equations:

Heat loss from Sootblowing = (18/100) * (2770+ 1.98 * (210-200)) = 502 kJ/kg

Heat Loss from Blowdown = 0.11 * 1150 (blowdown enthalpy) = 127 kJ/kg

Finally it is necessary to consider radiation losses from the boiler and other minor losses due to

leaks and minor streams. A rule of thumb for these numbers according to many boiler

manufacturers is to consider the radiation losses equal to 0.3% of the total heat input and the

145

unaccounted minor losses as 2% of the total heat input. Considering the numbers already

obtained, it is possible to calculate:

Heat losses from radiation = 0.003 * 14878 = 45 kJ/kg

Heat losses from unaccounted minor streams = 0.02 * 14878 = 298 kJ/kg

The total heat loss is then the sum of all individual losses:

Total heat loss = 1045 + 829 + 1196 + 641 + 1322 + 502 + 127 + 45 + 298 = 6005 kJ/kg

The heat available to steam generation is then the difference between the heat input and the heat

losses:

Heat to Steam = 14878 – 6005 = 8873 kJ/kg

This amount of energy will represent a thermal efficiency of:

Thermal Efficiency = (8873/14878) * 100 ≈ 60%

Finally it would be possible to identify the amount of steam generated per kilogram of black

liquor solids burned. Considering the given enthalpy of the final steam product (3372 kJ/kg), the

data for the feedwater and the energy available for steam generation an estimate of the steam

production can be made. To calculate that it is considered the energy available for steam

generation and divide this amount by the energy increase in the steam considering the final

enthalpy minus the enthalpy of the feedwater and the latent heat of vaporization. Then:

Steam for the mill = 8873 / (3372 – 104.8 – 4.19 * (110-25)) = 3.05 kg steam/kg BLS

Based on literature data from boiler manufacturers, the amount of steam generated per kilogram

of BLS burned usually is between 3 to 3.5 depending on specific operating conditions and black

liquor heating value. The total amount of feedwater would be the sum of the steam for the mill

and sootblowing steam and the blowdown water, thus:

Feedwater to boiler = 3.05 + 0.18 + 0.11 = 3.34 kg of water/kg BLS

Based on the data obtained it is possible to see that improvements in the efficiency of the boiler

can be achieved if the flue gas temperature at the exit of the boiler could be lowered by using the

flue gas to exchange heat with other points in the boiler. Another important factor would be to

146

increase the solids content in the black liquor prior entering the boiler. Also to increase the

efficiency of the sootblower devices would lead to minor losses of steam that could be used in

other areas.

B C D E J K L 4 INPUT DATA - BLACK LIQUOR PROPERTIES 5 Element wt% BLS Element wt% 6 Carbon C 35% Sodium Na 32.40% 7 Hydrogen H 3.30% Sulfur S 22.40% 8 Oxygen O 35.70% Oxygen O 44.80% 9 Sodium Na 19.70% Chloride Cl 0.30% 10 Potassium K 1.60% Inert Si, Al, etc 0.10% 11 Sulfur S 4% 12 Chloride Cl 0.60% 13 Inerts Si, Al, etc 0.10% 14 Solids Sol 70% 15 Higher Heat HHV 13950 kJ/kg BLS 16 Net Heat V. NHV 12093 kJ/kg BLS 17 Ass. Red. 90%

O P Q R 4 INPUT DATA - BOILER OPERATING DATA 5 Item Units 6 Air Excess forced draft air 6% Stoichiometric 7 Infiltration air 9% Stoichiometric 8 Total excess air 15% Stoichiometric 9 Ambient temperature 27 ºC 10 Humidity in air 0.013 kg H2O/kg dry air 11 FD air preheat temp. 150 ºC 12 Black Concentration Solids 70% 13 Liquor Temperature 108 ºC 14 Saltcake makeup 3% wt. of BLS 15 Direct heating liquor 1.7% wt. of wet BL 16 Dust recycle 10% wt. of initial Na 17 Smelt Smelt reduction eff. 90% 18 Organic C in smelt 1% wt. of smelt 19 Temperature 850 ºC 20 Enthalpy 1350 kJ/kg 21 Stack Exit gas temperature 210 ºC 22 Gas TRS as H2S 15 ppmv dry flue gas 23 SO2 75 ppmv dry flue gas 24 CO 200 ppmv dry flue gas 25 H2 200 ppmv dry flue gas 26 Particulate 0.1 g/DSCM 27 Na2SO4 particulate 80% wt as Na2SO4 28 Na2CO3 particulate 8% wt as Na2CO3 29 K2SO4 particulate 7.3% wt as K2SO4 30 K2CO3 particulate 0.7% wt as K2CO3 31 NaCl particulate 4% wt as NaCl 32 Na % in particulate 31% 33 S % in particulate 19.4%

147

34 Stream Blowdown flow 11% of BLS 35 Circuit Sootblowing steam 18% of BLS 36 Feedwater temp. 110 ºC 37 Steam temperature 482 ºC 38 Steam pressure 62.25 bar 39 Enthalpy of steam 3372 kJ/kg 40 Reference temp. 25 ºC

W X Y Z 4 MATERIAL BALANCE - SMELT CALCULATIONS 5 Item Calculation kg/100 kg BLS 6 Smelt Na2S from BLS 100*Q17*(78/32)*D11 8.78

7 (no minor loss) Na2S from saltcake 100*Q17*(78/32)*Q14*L7 1.47

8 Na2S total Z6+Z7 10.25 9 Na2SO4 from BLS 100*D11*(142/32)*(1-Q17) 1.78

10 Na2SO4 from saltcake 100*Q14*(142/32)*(1-Q17)*L7 0.30

11 Na2SO4 total Z9+Z10 2.07 12 NaCl from BLS 100*D12*(58.5/35.5) 0.99 13 NaCl from saltcake 100*Q14*(58.5/35.5)*L9 0.01 14 NaCl total Z12+Z13 1.00

15 Na2CO3 100*(106/46)*(D9-(46/32)*D11-(23/35.5)*D12) 31.25

16 K2CO3 100*D10*(138.2/78.2) 2.83 17 Inert 100*D13 + 100*Q14*L10 0.10 18 Total smelt Z8+Z11+Z14+Z15+Z16+Z17 47.50

19 Stoichiometric CO2 (44/12)*(100*D6-(12/106)*Z15-(12/138.2)*Z16) 114.46

20 Combustion H2O 100*D7*(18/2) 29.70 21 Products O2 for Na2SO4 Z11*(64/142) 0.93 22 Oxygen O2 for Na2CO3 Z15*(48/106) 14.15

23 requirement O2 required for K2CO3 Z16*(48/138.2) 0.98

24 O2 required for CO2 Z19*(32/44) 83.24 25 O2 required for H2O Z20*(16/18) 26.40 26 total O2 required Z21+Z22+Z23+Z24+Z25 125.71

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AC AD AE AF 4 MATERIAL BALANCE - FLUE GAS CALCULATIONS

5 Item Calculation kg/100 kg

BLS 6 Air Oxygen in BLS and saltcake 100*(D8+Q14*L8) 37.04 7 Total oxygen required Z26 125.71 8 Oxygen required from air AF7-AF6 88.67 9 Stoichiometric combustion air AF8/0.232 382.19

10 Nitrogen in stoichiometric combustion air AF9*0.768 293.52

11 Forced draft excess air AF9*Q6 22.93 12 Total FD air AF9+AF11 405.12 13 Infiltration air AF9*Q7 34.40 14 Total excess air AF11+AF13 57.33 15 Total air flow AF9+AF14 439.52

16 Water vapor Steam in stack from water in BL 100*(1-Q12)/Q12 42.86

17 in stack Humidity in air AF15*Q10 5.71 18 gas Sootblowing steam 100*Q35 18.00 19 Total added water vapor AF16+AF17+AF18 66.57 20 Flue gas Total flue gas flow Z19+Z20+AF9+AF14+AF19 564.01

AV AW AX AY AZ 4 ENERGY BALANCE FOR THE RECOVERY BOILER

5 Item Calculation kJ/kg BLS %

6 Heat Heating value of BL solids D15 13950 93.76 7 Input Sensible heat in BL (1/Q12)*2.65*(Q13-25) 314 2.11 8 Sensible heat in air (AF15/100)*1.006*(Q9-25) 9 0.06 9 Heat to preheat air (AF12/100)*1.006*(Q11-Q9) 501 3.37

10 Heat in feedwater for sootblower steam AF19/100*4.19*(Q36-25) 64 0.43

11 Heat in feedwater for blowdown Q34*4.19*(Q36-25) 39 0.26 12 Total heat input AY6+AY7+AY8+AY9+AY10+AY11 14878 100 13 Heat Sensible heat in dry flue gas (AK7/100)*1.02*(Q21-25) 878 5.90 14 Output Heat loss from hydrogen in BLS (Z20/100)*(2770+1.98*(Q21-200)) 829 5.57 15 Heat loss from water in BL (1/Q12-1)*(2770+1.98*(Q21-200)) 1196 8.04 16 Sensible heat in smelt (Z18/100)*Q20 641 4.31 17 Heat of form sulfide (Z8/100)*12900 1322 8.89 18 Heat loss from sootblowing steam (AF19/100)*(2770+1.98*(Q21-200)) 502 3.38 19 Heat loss from blowdown Q34*1150 127 0.85 20 Radiation loss 0.3% of heat input 45 0.30 21 Unaccounted losses 2% of heat input 298 2.00 22 Total of losses SUM(AY13:AY21) 5837 39.23 23 Heat to steam AY12-AY22 9041 60.77 24 Total heat output AY22+AY23 14878 100

149

Appendix F - Calculation Routine for Causticizing Plants

This appendix shows the material interrelationships within the recausticizing system as well as

between it and the digester system. Specifically, it lists all the major material balance equations

for the recausticizing system, starting with the smelt stream from the recovery furnace and

continuing through the white liquor stream to the digester. All equations relate to only a few

independent variables from the digester and recausticizing operations.

For each major press stream - recovery smelt, green liquor, raw white liquor, white liquor

(clarified), lime mud and weak wash - equations are given for each of the following:

Overall material flow rates

Chemical component breakdown in Na2O equivalents

Chemical component breakdown in actual chemical form

Overall water flow rate

Water content on each stream

It is significant that the equations are based on only six digester variables and three

recausticizing variables. The requirements of the recausticizing system are dependent on the

digester requirements and thus the following six independent digester variables form most of the

basis for the material balance equations:

Digester air-dry pulp production, kg (lb)

Digester yield

Active alkali charge to the digester (%)

White liquor active alkali strength

White liquor sulfidity

Total titratable alkali strength in white liquor

The other independent variables are from the recausticizing and lime burning systems. They are:

Excess lime

Average availability of reburned and make-up lime

Solids in the unwashed lime mud slurry (%)

150

For the sake of practical convenience, there are simplifications and assumptions contained in the

equations. When such simplifications occur, they represent only very small inaccuracies. For

those who are interested in the highest order of accuracy, they can easily revise the equations to

suit their particular needs.

RECAUSTICIZING PLANT MASS BALANCE

Item Unit Amount Calculation Pulp yield % 48 Input Value AA on o.d. wood % 16.5 Input Value WL total alkali lb/cf 7.5 Input Value WL activity % 85 Input Value WL sulfidity on AA % 25 Input Value Lime availability % 85 Input Value WL spec. grav. 95C 1.13 Input Value GL spec. grav. 95C 1.16 Input Value Makeup lime lb/ODT 22 Input Value

Balance for 1 ODT = 2000 lbs Item Unit Amount Calculation

1. Alkali to Digester Active Alkali lb 687 (2000/.48) x .165 as Na2O Total Alkali lb 809 687/.85 as Na2O

2. White Liquor to Digester White liquor volume cf 108 809/7.5 White liquor weight lb 7615 108 x 1.13 x 62.4 Weight of chemicals lb 1134 108 x 10.5

Water in WL lb 6481 7615 - 1134 3. Lime Calculation

NaOH lb 516 687 x 0.75 CaO lb 466 516 x 56/62 Lime lb 548 466/.85

4. Lime Mud Calcination CaCO3 lb 832 466 x 100/56 Inerts lb 82 548-466

Total Solids lb 914 832+82 5. White Liquor Clarifier

Underflow Solids % 40 Input Value Total Underflow lb 2285 914/.4

Liquor lb 1371 2285-914 TTA in Underflow as Na2O lb 146 (1371/(62.4 x 1.13)) x 7.5 Weight of Chemical in Und. lb 204 (1371/(62.4 x 1.13)) x 10.5

Water in the Clarifier lb 1167 1371-204 6. Lime Mud Washer Und.

Uderflow Solids % 45 Input Value Recirculation from Filter % 10 Input Value

Total Solids lb 1005 914 x 1.1 Total Underflow lb 2234 (914 x 1.1)/.45

Liquor lb 1229 2234 - 1005 TTA in Underflow as Na2O lb 21 1229/(62.4 x 1.01) x1.1 Na2O

151

Weight of Chemical in Und. lb 31 1229/(62.4 x 1.01) x1.6 Na2O Water in LMW underflow lb 1198 1229 - 31

7. Green Liquor to Slaker Total alkali to digester Na2O lb 809 Calculated previously

Total alkali with lime mud lb 146 Calculated previously Alkali in slaker grits as Na2O lb 1 Input Value Total alkali to slaker as Na2O lb 956 Calculated previously

RECAUSTICIZING PLANT MASS BALANCE 8. Green Liquor Clarifier Und.

Volume of GL to slaker ft^3 127 956/7.5 GL dregs lb/ODT 10 Input Value

Underflow Solids % 8 Input Value Total Underflow lb 125 10/.08

Liquor lb 115 125-10 TTA in Underflow as Na2O lb 12 115/(62.4 x 1.16) x 7.5 Weight in Chemical Und. lb 19 115/(62.4 x 1.16) x 12.2 Water in GLC Underflow lb 96 115 - 19

9. Green liquor from dregs filter Cake discharge % 50 Input Value

Total cake including lime mud lb 13 Input Value (3:1) Weight of discharge lb 26 13/0.5

Water with cake lb 13 26 - 13 Sodium to filter as Na2O lb 12 Input Value

Filter Efficiency % 99 Input Value Sodium with cake as Na2O lb 0.1 Input Value (loss) Sodium in filtrate as Na2O lb 12 Input Value

Showers on filter lb 25 2.5 x 10 Filtrate recycled to GLC lb 127 115+25-13

Excess water in recycle to GLC lb 12 Input Value Water in filtrate lb 108 96+25-13

10. Green Liquor from Diss. Tank Total alkali to slaker as Na2O lb 956 Calculated previously

Total alkali in discharged dregs lb 0.1 Input Value Total alkali from Diss. Tank lb 956 956 - .1

Water in total GL lb 7688 ((956/7.5)x62.4x1.16-(956/7.5)x12.2) Water in GL from Dissolving lb 7676 7688-12

11. Lime Mud Filter Lime Mud Solids lb 1005 914 x 1.1

Feed Solids % 25 Input Value Weight of Feed lb 4020 1005/.25 Water in feed lb 3015 4020-1005

Filter Discharge % 75 Input Value Filter Discharge lb 1340 1005/.75 Water with cake lb 335 1340-1005 Filter Showers lb 335 335 x 1

Total filtrate lb 3015 3015-335+335 TTA with cake as Na2O % 0.1 Input Value TTA with cake as Na2O lb 1 1005*.001 TTA in filtrate as Na2O lb 20 21 - 1