SIMULATION STUDIES USING ASPEN PLUS

Shuwana Tasleem, K. Anithaa and V. Ramesh Kumar *

Department of Chemical Engineering

University College of Technology, Osmania University, Hyderabad – 500 007, (A.P) India

ABSTRACT

Aspen is a Process simulation software package widely used in industry today and easy to use, powerful, flexible, process engineering tool for the design, steady-state simulation and optimization of process plants. In the present study an attempt has been made to carry out simulation studies on Extractive and Crude Distillation columns.

In the case study 1, Simulation studies are carried out on Extractive column. The acetone-methanol extractive distillation using water as an entrainer is simulated on Aspen Plus software package. Calculation of the vapor-liquid equilibrium for the ternary system is done by the UNIQUAC model and compared with other thermodynamic models. The effects of the solvent to feed molar ratio, reflux ratio, feed stage, feed solvent stage, and solvent feed temperature are studied to obtain the best design of the extractive distillation column with minimal energy requirements. The simulation results show the effect of the main variables on the extractive distillation process.

In the case study 2, Simulation studies are carried out on distillation of crude oil. Steady state simulation of Pre-flash, Atmospheric column (Pipestill) and Vacuum distillation units in a crude oil distillation plant is performed using ASPEN simulations. Primary processing of oil gives fractions such as gas, gasoline, kerosene, gas oil, atmospheric residue, oil fractions and heavy residue. The quantity of each fraction is specified by the composition of oil.

________________________________________________________________a Presently with NIT-Warangal, AP

Introduction

Process simulation refers to the usage of a mathematical model to describe a chemical process. The model is usually solved using a computer program; the results of which, either numerical or graphical, describe the operation of the plant. The simulation studies, for any system reduces the process development cost, as it minimizes the required experimental and pilot plant work and the waste generated. Steady state simulations provide powerful insight into the plant behavior that can be used to enhance design, safety and operation of process facilities while minimizing capital and operating costs.

Fig. 1 Classification of the types and uses of Process Simulation Programs

Type of Model

Uses

Program Type

Fig. 2 Structure of a Sequential Modular Simulation Program

Mathematical Model of the Chemical Process

Steady State Dynamic

Simulation Design Optimization Synthesis

Sequential Modular Program Specific Program

INPUT OUPUT

EXECUTIVE PROGRAM

Unit Modulessub-programs

Design and Optimization sub-programs

Library of Physical and thermodynamics Properties data

Simulation Tool – ASPEN PLUS

ASPEN PLUS is an easy to use and flexible process modeling tool for steady state simulation, design, performance monitoring, optimization and business planning of process plants such as chemicals, specialty chemicals petrochemicals and petroleum and metallurgy industries. Given a process design and an appropriate selection of thermodynamic models, ASPEN PLUS uses the mathematical models to predict the performance of the process. This information can then be used in an interactive fashion to optimize the design.

ASPEN PLUS is a great tool for the development of the chemical processes or carrying out analysis on existing process industry as a design tool because of its ability to simulate a variety of steady-state processes ranging from single unit operation to complex processes involving many units. Usually it provides the user with a comprehensive system of online prompts, hypertext help and expert system guidance at every step making it easy to build and run the simulation models.

Importantly, ASPEN PLUS does not design the process, it takes a design that the user supplies and simulates the performance of the process specified in that design. Therefore, a thorough understanding of the underlying chemical engineering principles is required to supply reasonable values of input parameters and to evaluate the suitability of the results obtained.

Aspen Engineering SuiteTM Simulation & Optimization – Steady-State provides a complete set of tools to enable users to quickly create steady-state models for their process. ASPEN PLUS dynamics extends ASPEN PLUS steady-state models into dynamic process models, enabling design and verification of process control schemes, safety studies, relief valve sizing, failure analysis, and development of start-up, shut-down, rate–change and grade transition policies. ASPEN PLUS dynamics is a core element of Aspen Tech’s ASPEN ONE® process engineering applications.

Literature Review

Stojić [1] report a computer simulation of the atmospheric distillation using Aspen Plus simulator. The operating parameters, they studied were taken from the “Badger” project documentation, which was designed for the oil refinery in Novi Sad. The simulation was performed for a type of crude oil, which is currently in use.

Massimiliano Errico et al [2], in their work on industrial crude oil distillation unit evaluated the possibility of modifying the feed conditions by installing a pre-flash drum or a pre-flash plate column. They performed simulation of the unit by means of the software package Aspen Plus 13.0. They compared the obtained results with the plant data in terms of flow rate and product quality utilizing the ASTM D-86 curves and a good agreement is reported.

GÓmez & Gil [3] report simulation and analysis of an extractive distillation process (extractive distillation and recovery columns) for the separation of Tetrahydrofuran (THF)-Water azeotropic mixture using Aspen Plus® and Aspen Split simulators. They employed the NRTL model for the calculation of vapor liquid equilibrium of the system. The solvent to feed molar ratio, reflux molar ratio, feed stage, feed solvent stage, and feed solvent temperature, were determined and their effects on the separation and the energy consumption in the two columns was studied.

Vafae et al [4] studied the steam distillation process for the oil recovery processes. They considered a Multi-Layer Perceptron (MLP) network as a new and effective method to simulate the distillate recoveries of 16 sets of crude oil data obtained from literature. API, viscosity, characterization factor and steam distillation factor were taken as input parameters of the network while distillate yield is the result of the model. Thirteen sets of data were used for training the network and three remaining sets were used to test the model. They also discussed the comparison between the developed MLP model, Equation of State (EOS)-based method and Holland–Welch correlations and found that the errors of the MLP model for training and test data sets are significantly lower than that of those methods. Also, the MLP network does not require oil characterization, which is a necessary and rigorous step in EOS and Holland–Welch methods.

Gonçalves et al [5] studied the dynamic simulation as applied to the Atmospheric Distillation Unit of a crude oil refinery. The unit considered was a representative of real refineries and was characterized by multiple interactions and high level of non-linearities. They have reported the behavior of the system for several process operating conditions.

Gil1 et al [6] studied the simulation of an extractive distillation column with the ASPEN PLUS software platform and using the RadFrac module for distillation columns, to investigate the effect of the ethylene glycol and glycerol composition, the entrainer feed entry stages, the entrainer split stream feed, and the azeotropic feed temperature on the separation. A rigorous simulation of the extractive distillation column finally established was also performed, including a secondary recovery column for the mixture of solvents and recycle loop, to simulate an industrially relevant situation.

Cui Xianbao [7] studied a batch extractive distillation in a column with a middle vessel. They simulated the process by a constant holdup model and solved by two point implicit method. They report that, for the system separating acetone-methanol mixture using water as solvent, the solvent at the bottom and the product at the top of the column can be with drawn simultaneously for a long period of time. It needs more time for the solvent to reach high purity that that required for the more volatile component to reach high purity, so that the time required to withdraw solvent from the bottom is delayed.

The work of Baharev et al [8] presents the computation of distillation columns with interval methods. They proposed a ‘branch-and-prune’ algorithm which is guaranteed to converge, and is fairly general at the same time. Power of the suggested method is demonstrated by solving, with guaranteed convergence, even the MESH equations of a 22 stage extractive distillation column with a ternary mixture.

Process Description

The development of a simulation model for a chemical process using ASPEN PLUS involves the following steps:

1. Define the process flowsheet configuration by specifying

a. Unit Operations.

b. Process Streams flowing between the units.

c. Unit operation models to describe each unit operation.

2. Specify the chemical components.

3. Choose a thermodynamic model to represent the physical properties of the components and mixtures in the process.

4. Specify the component flow rates and thermodynamic conditions (i.e., temperature, pressure or phase condition) of the feed streams.

5. Specify the operating conditions for the unit operations.

6. Run the simulation.

Case Study I - Simulation studies on Extractive column

Extractive distillation is a separation process used to separate mixtures that are difficult or impossible to separate by conventional distillation. In extractive distillation, a third component (solvent or entrainer) is added to the binary mixture to increase the relative volatility of the original components.

For the work presented here, a system of Acetone-Methanol mixture was selected. The acetone-methanol system has a minimum boiling point azeotrope, and the extractive distillation is a possible method used to separate this azeotropic mixture; with suggested entrainer as water. In this study, the effect of the main variables on mixture separation, were studied by simulation on Aspen Plus software. The thermodynamic package using UNIQUAC1 model was used to evaluate the vapor-liquid equilibrium (VLE), according to the experimental data obtained from literature. The simulation was carried out taking into account pressure drops and heat-integration, in order to compare the results with those reported previously in literature in which the property estimation was done by other thermodynamic models that cannot represent accurately the VLE or considered simplifying assumptions.1 UNIQUAC is a type of property method available in Aspen library.

Case Study II - Simulation studies on distillation of crude oil

Crude oil is a mixture of many thousands of components varying from light hydrocarbons such as methane, ethane, etc., to very high molecular weight components. The composition of crude oil depends also on the location of exploitation. These facts exclude the use of classical characterization methods for the crude oil composition by mole or mass frictions of individual components. In petroleum refining, the boiling point ranges are used instead of mass or mole frictions. Three types of boiling point analysis are known: ASTM D862, ASTM D158 and TBP (true boiling point). Properties of a petroleum stream are not specified in terms of composition. Instead, properties such as 5% point, 95% point, final boiling point, flashpoint and octane number are used.

The method for quantitative calculations of the petroleum frictions is to break them into pseudo components. Each pseudo component has its average boiling point, specific gravity, and molecular weight.

The process consists of the following steps

1. The process feed (MIXCRUDE), consisting of a blend of two crude oils (OIL-1 and OIL-2; goes to the pre flash furnace.

2. The pre flash tower (PREFLASH) removes light gases and some naphtha from the partially vaporized feed.

3. Pre flash bottoms (CDU-FEED) are further processed in the crude distillation unit (CDU). The CDU consists of a crude unit furnace and an atmospheric tower. First, the crude unit furnace partially vaporizes the bottoms from the pre flash. Then the atmospheric tower separates the preflash bottoms into five cuts

Heavy naphtha (HNAPHTHA)

Kerosene (KEROSENE)

Diesel (DIESEL)

Atmospheric gas oil (AGO)

Reduced crude (RED-CRD)

4. Reduced crude goes to the vacuum distillation unit (VDU) for further fractionation under vacuum conditions. The VDU consists of a vacuum unit furnace and vacuum tower. The vacuum tower produces the following additional cuts.

Overhead (OFF-GAS)

Light vacuum gas oil (LVGO)

2 ASTM- American Society for Testing and Materials, is an international standards organization that develops and publishes voluntary consensus technical standards for a wide range of materials, products, systems, and services

Asphaltic residue (RESIDUE)

ASPEN PLUS SIMULATION

Case Study I - Simulation studies on Extractive column

The process flow diagram of the extractive distillation process is presented on Figure 3. The process has two columns, one for extractive separation and another for solvent recuperation. The azeotropic mixture and the solvent are fed to the first column, in which the components boil and are separated allowing the less volatile components to be collected in the bottoms and in the top of the column the light key component is collected. The bottom product is fed to the second column in which the solvent is recovered and recycled to the extractive distillation column.

Figure 3. Process Flow Sheet for Extractive Distillation

Table 1 Feed stream conditions

stream 1 4

Feed mole flow (kmol/h) 100 1

Temperature(OC) 20 47

Pressure (atm) 1 1

Acetone 0.7775 0

Methanol 0.2225 0

Water 0 1

Table 2 Configuration of Extractive and Recovery column

Parameter Extractive Recovery

column column

number of stages 52 26

Feed stage 48 14

Entrainer feed stage 22 -

Reflux ratio 5 3

Condenser total total

Pressure drop per stage (atm) 1 1

Distillate mole flow (kmol/hr) 76 25

Figure 4. X-Y plot for Acetone- Figure 5. Residue curve for Methanol Acetone-Methanol

Case Study II - Simulation studies on distillation of crude oil

Figure 6 shows the steady-state simulation scheme of Preflash and atmospheric columns in ASPEN Plus.

Figure 6. Flow Sheet of Crude Distillation

Table 3 Oil-I Data (Dubai crude)

TBP Distillation

Light End API3 Gravity Curve

3 API – American petroleum Institute

Azeotrope @ 55.24oC

VOL % Temp( C ) ComponentsLiq. Vol.

Frac.Mid Vol.

%Gravity

2.1 27.8 C1 - Methane 0 2.1 122.5

9.1 93.3 C2 - Ethane 0.02 5.6 76.9

20.2 148.9 C3 - Propane 0.29998 14.65 61.2

26.7 193.3 iC4 - Isobutane 0.17999 23.45 51.3

47.1 304.4 nC4 - n Butane 0.84994 36.9 41.6

53.8 348.9 iC5 - Isopentane 0.73 50.45 31.5

64.1 415.6 nC5 - n Pentane 1.18 58.95 25.8

81.8 565.6 Cy5 - Cyclopentane 0.08 72.95 20.1

100 722.3 90.9 4.4

Table 4 Oil-II Data (Basara Crude)

TBP Distillation   Light End   API Gravity Curve

VOL % Temp( C ) ComponentsLiq. Vol. Frac.

Mid Vol.% Gravity

2.2 27.8 C1 - Methane 0 2.2 120.2

8.9 93.3 C2 - Ethane 0.0199986 5.55 72.3

19.5 148.9 C3 - Propane 0.2899793 14.2 58.1

25.5 193.3 iC4 - Isobutane 0.2299836 22.5 47.8

47.5 304.4 nC4 - n Butane 0.8599386 36.5 37.2

55.2 348.9 iC5 - Isopentane 0.76 51.35 28.6

66.1 415.6 nC5 - n Pentane 1.07 60.65 23.4

84.4 565.6 Cy5 - Cyclopentane 0.11 75.25 16.6

100 722.3 92.2 8

Figure 7 TBP curves of Dubai, Basara & Blend Crudes

Figure 8 TBP curves of Dubai, Bombay & Blend Crudes

Results and Analysis

Case Study I - Simulation studies on Extractive column

Figure 9 Temperature Profile for Extractive Column

Figure 10 Molar – Flow rates for Extractive Column

Figure 11 Vapor Composition Profiles for Extractive Column

SENSITIVITY ANALYSIS

Table 5 Comparison of Simulation Results with Other Studies

Parameter langston This study

Model Wilson UNIQUAC

Acetone purity 99.7% 99.0%

E/F Ratio 4 2

Table 6 Extractive Column conditions for all above mentioned methods

Parameter langston This study

Number stages 73 52

Binary feed stage 5 48

Entrainer feed stage 25 22

Reflux ratio 4 5

Binary feed composition (%mol)

Acetone 50 77.75

Methanol 50 22.25

Pressure drop per stage(atm) 0.02 0.01

Table 7 Recovery Column conditions for all above mentioned methods

Parameter langston This study

Number of stages 34 26

Feed stage 18 14

Reflux ratio 3.86 3

Pressure drop per stage(atm) 0.02 0.01

The best primary column configuration shows that the entrainer is fed above the binary feed, which allows higher acetone purity. Similar results have been reported in literature.

Figure 8 Temperature Profile for Extractive Column – Wilson

Method Figure 9Molar – Flow rates for

Extractive Column – Wilson Method

Figure 10 Vapor Composition for Extractive Column – Wilson Method

Case Study II - Simulation studies on distillation of crude oil

Table 8 Results for Dubai and Basara crude data

Mass flow(kg/hr) Yield(wt%)

Light 3635.708 0.615437

Naptha 86607.92 14.66061

H Naptha 40468.6 6.850348

Diesel 73077.25 12.3702

Kerosene 62740.9261 10.62051

AGOO 50195.5212 8.49688

Off Gas 9086.717 1.53816

Lvgo 71312.2749 12.07143

Hvgo 109898.901 18.60321

Residue 73660.1838 14.17321

ASTM curves for the products are plotted using stream results. The ASTM curves are on the Stream Results | Vol. % Curves sheet.

Figure 11 Block Crude (PetroFrac) Stream results -Temperature Vs Vol %

Figure 12 Block VDU (PetroFrac) Stream results - Temperature Vs Vol %

TBP curve for streams LVGO, HVGO, and RESIDUE are plotted. The True Boiling Point curves appear on the Stream Results | Vol. % Curves sheet.

Process Limitations

The feed temperature is the most important limitation because at a certain temperature the oil starts to crack and coke appears in the column, which results in flooding of the column and consequently the process collapses.

The limitation is the column pressure for the same reason. The number of stages is also included in limitations, because plant has to be tested as there is. However, the stage from which liquid is drawn for stripers and stage of return wasn’t limitations, so it was tested in purpose of getting more quantity as well as quality of light fraction with regard on composition of gasoline fraction get

from OIL-I crude oil on this plant. It was used in case of OIL-II type of crude oil.

Sensitivity Analysis

The first simulation was run with Dubai and Basara crude oil. The simulation was successfully done, because the matching of the results with the project data was satisfying. The comparison between the project data and the data obtained by the simulation is given.

The simulation was run again with a different crude oil. The Dubai and Bombay type of crude oil was used for the simulation. Because of the process limitations, mentioned above, no further pressure and temperature adjustment is possible or even necessary. Results of this analysis in terms of the fraction flow rates. Comparing these results to those for the Dubai and Basara type of crude oil, significant change can be seen.

Aspen Plus can be successfully used to simulate the atmospheric and vacuum crude unit with the existing project parameters for the crude oil Dubai and Basara. What is more important is the fact that this unit can be used for a different crude oil, such as the Dubai and Basara crude oil, with not so significant changes in basic operating parameters. These changes were in accordance to the main process limitations and unit capability.

ASPEN RESULTS FOR SENSTIVITY ANALYSIS

Table 9 Results for Dubai and Basara crude data

Mass Flow(lb/hr)

Yield (wt %)

Mass flow(kg/hr)

Yield (wt %)

LIGHT 8015.3647 0.6154 3635.708 0.615437

NAPTHA 190937.775 14.6606 86607.92 14.66061

HNAPTHA 89217.9902 6.8503 40468.6 6.850348

DIESEL 161107.765 12.3702 73077.25 12.3702

KEROSENE 138320.068 10.6205 62740.9261 10.62051

AGO 110662.184 8.4968 50195.5212 8.49688

OFF GAS 20032.7823 1.5381 9086.717 1.53816

LVGO 157216.658 12.0714 71312.2749 12.07143

HVGO 242285.608 18.6032 109898.901 18.60321

RESIDUE 162392.911 14.1732 73660.1838 14.17321

Figure 13 Block VDU Stream Figure 14 Block VDU Stream results Temperature Vs Vol % results Temperature Vs Vol % Dubai & Basara Crude Dubai & Basara Crude

Table 10 Results for Dubai and Bombay crude data

Mass Flow

(lb/hr)

Yield

(wt %)

Mass flow

(kg/hr)

Yield

(wt %)

LIGHT 3797.58623 0.3021 1722.5561 0.30211

NAPTHA 284700.082 22.6487 129137.782 22.64877

HNAPTHA 95581.8548 7.6038 43355.1991 7.603832

DIESEL 180511.959 14.36083 81878.8455 14.36028

KEROSENE 140940.474 11.2122 63929.5222 11.21225

AGO 108169.128 8.6052 49064.6917 8.605188

OFF GAS 20040.0979 1.59425 9090.0353 1.594252

LVGO 148983.658 11.8521 67577.849 11.85211

HVGO 230901.33 18.3689 104735.079 18.36891

RESIDUE 21145.8867 3.4522 9591.61265 3.452292

Figure 15 Block VDU Stream Figure 16 Block VDU Stream results Temperature Vs Vol % results Temperature Vs Vol % Dubai & Bombay Crude Dubai & Bombay Crude

Conclusion

The following conclusions can be made from the present study:

1. Aspen Plus can be successfully used to simulate different process like the extractive distillation and the atmospheric and vacuum crude units

2. The separation of azeotropic mixtures like acetone-methanol is a feasible by extractive distillation if the solvent is selected properly. For this study, water was a feasible entrainer but the use of a solvent with a higher boiling point could be more appropriate for this azeotropic separation.

3. From the results of simulation of different crudes ( yield %), it can be concluded that using Dubai & Bombay crude mixture yields more distillates than Dubai & Basara type of mixed Oil.

4. BK-10 method yields good results than other methods; In many Petroleum industries BK-10 method is used for its ability to produce results for all types of crudes.

References:

1. “Simulation of Atmospheric Crude Unit "Badger" Using Aspen Plus”, Milana M. Stojić*, Svetlana Lj. Nedeljkov, Darko M. Krstić, Siniša Mauhar, Petroleum & Coal, 46(2), 57 - 62 , 2004.

2. “Energy Saving In A Crude Distillation Unit By A Preflash Implementation”, Massimiliano Errico, Giuseppe Tola, Michele Mascia

Applied Thermal Engineering 29 (2009) 1642–1647

3. “Simulation Of The Tetrahydrofuran Dehydration Process By Extractive Distillation”, P.A. GÓMEZ And I.D. GIL, Latin American Applied Research 39:275-284 (2009)

4. “Simulation Of Steam Distillation Process Using Neural Networks”, M.T. Vafaei, R. Eslamloueyan∗, Sh. Ayatollahi, Chemical Engineering Research And Design 8 7 ( 2 0 0 9 ) 997–1002

5. “Dynamic Simulation And Control: Application To Atmospheric Distillation Unit Of Crude Oil Refinery”, David D. Gonçalves, Fernando G. Martins, Sebastião Feyo De Azevedo, 20th European Symposium On Computer Aided Process Engineering – ESCAPE20

6. “Simulation Of Ethanol Extractive Distillation With A Glycols Mixture As Entrainer”, I.D. Gil1*, A.M. Uyazán1, J.L. Aguilar1, G. Rodríguez1, L.A. Caicedo1, 2nd Mercosur Congress On Chemical Engineering, 4th

Mercosur Congress On Process Systems Engineering

7. “Batch Extractive Distillation In A Column With A Middle Vessel”, CUI Xianbao, YANG Zhicai, Zhai Yarui And Pan Yujan, Chinese J. Chem. Eng., 10(5), 529 – 534 (2002)

8. “Computation Of An Extractive Distillation Column With Affine Arithmetic”, Ali Baharev, Tobias Achterberg, Endre Rév, Aiche Journal, 2009, 55 (7), 1695-1704