EXPERIENCE ON SYSTEM INTEGRATION AND SIMULATION

Professor RUBENS MACIEL FILHO

•Laboratory of Optimization, Design and Advanced Process Control

•Department of Chemical Processes, School of Chemical• Engineering, State University of Campinas, Campinas - Brazil

e-mail maciel@feq.unicamp.br

VIRTUAL SUGAR CANE BIOREFINERY-CTBE - August 2009

Universidade Estadual de Campinas- UNICAMPSchool of Chemical Engineering

MOTIVATION• Process Simulation

– Evaluation of several possible routes –routes discrimination

–Investigation of different scenarios

- Process understanding

- Impact of operation variables on processperformance

Process Simulation (cont.)-Preliminary evaluation of costs, water andenergy consumption

-Studies of variable interaction and processdynamics

-Operator Training

-Dynamic simulation- process control strategiesmay be evaluated

Design of Equipments and plant conceptualdesign

PROCESS MODELLINGSteady State Model

Dynamic Model

Simplified versus Detailed Model

Physico-Chemisty based Models (Deterministic) versus Empiric and or Statistical Models

Hybrid Model

Single Unit Models

Large Scale Plant Model

Process Simulation

System –can be seen a set of subsystem depending upon of required investigation

Interaction among subsystems – made through mass and heat transfer parameters

Subsystem 1– an important component of the process, inside an equipment where the phenomena are intrinsically taking place- for instance catalyst particle, bagasse to be hydrolyzed and microorganism in biotechnological process. When considered explicitly a heterogeneous model is formulated.

Subsystem 2 - Equipment - peace of the plant where the changes (reactions, mixtures or separations) are occurring. In this category it may be place reactors, separation columns, fermentors, etc.

Subsystem 3 – large scale plant or a set of equipments in which there exist interest to study

Subsystem 1 and 2 – normally require software development if detailed representation are desired.

Subsystem 3 – simulators, including the commercial ones (Hysis , Aspen, Gproms etc)

System Integration

There exist an incentive for high operational performance operation

Process optimization begins with better process control

Large Plant Optimization and controlRTO: Integrate economic objectives and control

Stability, controllability and safety

System Integration

Large Plant Optimization and Control

RTO (Real Time Operation): Integrate economic objectives and control

Stability, controllability and safety- may be expressed as plant restriction

Refinery process ⇒large scale units, high products output, monitoring difficulties,data reconciliation

Two main strategies are to be implemented:

One layer approach

two layers approach

Hybrid approach may be necessary

Optimization Strategies

Economical optimization problem is solved together with the control problem

very sensitive to model mismatch

dimension of the optimization problem can be very large

( on line applications can be restrictive) use of simplified model may not be suitable

One layer approach

One layer approach

non-measuredinputs

measured inputs Process

non-measuredoutputs

measuredoutputs

Estimationblock

controller/optimizer

hierarchical control structure where there is an optimization layer that calculates set-

points to the advanced controller

the optimization layer is composed of an objective function and a process steady-

state model

Two layers approach

Two layer approach

non-measuredinputs

measured inputs measured outputs

non-measuredoutputs

Process

Estimationblock

Controller

Optimizer

setpoints

Advanced Controllers

• CONTROLADORES LINEARES• NON LINEAR CONTROLLERS• PREDICTIVE CONTROLLERS• ROBUST CONTROLLERS• ADAPTIVE CONTROLLERS• HYBRID CONTROLLERS (NEURALNETWORK AND FUZZY COUPLED WITH MODELBASED CONTROLLER)

Simulation – Applications

Subsystem 1

STRUCTURED MATHEMATICAL MODEL

FOR ETHANOL PRODUCTION

Possible to handle with substrate to drive the fermentation

STRUCTURED MATHEMATICAL MODEL

Representative Metabolic Route (F. Lei et al. Journal of Biotechnology 88 (2001) 205-221)

Mass balance equations and reaction rate of the model

( ) ( )eglufeedeglu SSDXRR

tS

cos71cos −++−=

∂

∂

( ) ( )pyruvatepyruvate SDXRRR

tS

−−−=∂

∂321978.0

( ) adeacetaldehyedeacetaldehyiglu

egluea

heglu

egluha

leglu

eglul Xs

KsKss

kXKs

skX

Kss

kR11

cos1

1cos

cos1

1cos

cos11 1 ++

++

++

=

aeglu

eglu XKs

skR

7cos

cos77 +

=

aegluipyruvate

pyruvate XsKKs

skR

11

cos2222 ++

=

apyruvate

pyruvate XKs

skR

34

4

33 +=

( ) ( )deacetaldehydeacetaldehy SDXRRR

tS

−−−=∂

∂6435.0

( ) ( )acetateacetate SDXRRRt

S−−−=

∂∂

854363.1

Acdhadeacetaldehy

deacetaldehy XXKs

skR

444 +

=

aethanolrdeacetaldehy

ethanolrdeacetaldehy XsKKs

skskR

66

666 ++

−=

aegluieacetate

acetateea

acetate

acetate XsKKs

skX

Kss

kR1

1

cos555

555 ++

++

=

aegluieacetate

acetate XsKKs

skR

11

cos5588 ++

=

( ) ( )ethanolethanol SDXR

tS

−=∂

∂6045.1

( ) ( )XDXRRtX

−+=∂∂

87 619.0732.0

( ) ( ) aa XRRRRRR

tX

8710987 619.0732.0619.0732.0 +−−−+=∂

∂

aeglu

egluca

egluieethanol

ethanole

eglu

eglu XKs

skX

sKKss

kKs

skR

9cos

cos9

cos999

9cos

cos99 1

1+

++

++

+=

aeethanol

ethanolea

eglu

eglu XKs

skX

Kss

kR10

1010cos

cos1010 +

++

=

( ) ( ) AcdhAcdh XRRRRt

X87119 619.0732.0 +−−=

∂∂

AcdhXkR 1111 =

X → biomass; Xa → active cell material; XAcdh → Acetaldehyde dehydrogenase; D → dilution rate;

Ki → rate constant; Ki → affinity constant;Kji → inhibition constant

• Mass balance equations → 8

• Kinetic parameter → 37

• Parameter adjust → Genetic Algorithm

CSTR simulations

TRS → Total Reductor Sugars

Batch simulations

SugarGlycose

Sacarose

FER

MEN

TATI

ON Etanol

Ácido acético

Ácido lático

Acetona Butanol Etanol

CH

EMIC

AL

SYN

THES

IS

Acetaldeído

Ácido acético

Anidrido acético

Acetato de etila

Acetato de vinila

Crotonaldeído

Paraldeído

Butanol

Acetato de butila

Piridina

Nicotinamida

Glicol

Butadieno

Glioxalato

Produtos químicos produzidos por fermentação

Some Chemical Products via fermentation

Etileno

Etanol

Acetaldeído

Ácido acético

Propano

Propileno

Ácido acrílico

Glicerol

Ácido lático

Butadieno

Butanodiol

Ácido succínico

BIOMASS H

YD

RO

LYSI

SSugar

Glicose

Sacarose

Xilose

Arabinose

FER

MEN

TATI

ON

Produção de novos produtos químicos a partir de biomassa

Other Products to be obtained from biomass

Fermentation process – piuvirate is formed in glycolysys

GLICOSE

Glicose 6-fosfato

Frutose 6-fosfato

Frutose 1,6-bifosfato

Gliceraldeído 3-fosfato 1,3-Difosfoglicerato 3-fosfoglicerato

2-fosfoglicerato Fosfoenolpiruvato PIRUVATO

ADP ATP

ADP ATP

ATP ADP

ATP ADP

NAD+ NADH +Pi +H+

10'1 146

2 2 Piruvato 2 2cos−

++

−=∆

++→+

kJmolG

HNADHNADeGli

10'1

2

61

2 2 2 2−=∆

+→+

kJmolG

OHATPPATP i

Processo de glicólise

Condições anaeróbiasCondições anaeróbias

2 Lactato

GLICOSE

2 Piruvato

2 Etanol + 2CO2

2 Acetil CoA

4 CO2 + H2 O

Rota (EMP)10 reações sucessivas

O2

CO2

Condições aeróbias

Ciclo do ácido TCA

O2

2 Ácido Acrílico + 2H2O

Rota glicolítica

Metabolic pathways for the synthesis of acrylic acid (Straathof et al., 2005)

STRUCTURED MODEL WITH IMOBILIZED CELLS

Structured Models based on the work of Lei et al. (2001) e Stremel (2001).

Model of Lei et al. (2001) -a structured biochemical modelthat describes the aerobic growth of Saccharomycescerevisiae in a medium limited to glucose and / or ethanol.

Model of Stremel (2001) -alternative structured model torepresent the dynamic simulation of a tubular bioreactorwith immobilized cells of Saccharomyces cerevisiae foralcoholic fermentation.

Para desenvolvimento deste modelo foi considerado:

Continuous isothermal process

heterogeneous model ;

biomass composition: CH1,82O0,576N0,146;

spherical particles ;

heterofermentative processproduction associated with cell growth;

axial dispersion .

Solution by orthogonal collocation

Metabolic route

Model Reactions

aa

aa XKS

SkXKS

SkR1

11

11 ++

+=

a

i

X

KLKS

SkR

+

+=

2

222

1

1

aXKP

PkR3

33 +=

aXKL

LkR4

44 +=

ai

XSKKL

LkR

++

=55

55 11

aia

XAAKKL

LKS

SkR

+

+

++

=1

1

66666

aa

aa XKAA

AAkXKS

SkR

+

+

+

=7

77

77

Reaction Rates

Mass Balances for the solid phase Glicose

Piruvato

Lactato

Ácido Acrílico

Células

Células ativas

Enzima lactato desidrogenase

( ) XeRRrSr

rrR

Dt

S AAAKAS −+−

∂∂

∂∂

=∂

∂21

222

1

( ) XeRRrPr

rrRD

tP AAAKAP −−+

∂∂

∂∂

=∂∂

312

22 978,01

( ) XeRRRrLr

rrRD

tL AAAKAL −−−+

∂∂

∂∂

=∂∂

5432

22 023,11

( ) ( ) XeRRr

AArrrR

Dt

AA AAAKAAA −−+

∂∂

∂∂

=∂

∂74

222 8,01

( ) XkeX

XXRRtX

dAAAK

sat−

−+=

∂∂ − `

52 1821,0732,0

( ) ( ) aa XRRRRRR

tX

527652 821,0732,0821,0732,0 +−−−+=∂

∂

( ) LADHLADH XRRR

tX

526 821,0732,0 +−=∂

∂

Mass Balance for the Fluid Phase Glicose

Piruvato

Lactato

Ácido Acrílico

( )[ ]XeRRzSu

zSD

dtS AAAK

az−+

−−

∂∂

−

∂

∂=

∂212

2 1 ηε

ε

( )[ ]XeRRzPu

zPD

dtP AAAK

az−−

−+

∂∂

−

∂

∂=

∂312

2978,01 η

εε

( )[ ]XeRRRzLu

zLD

dtL AAAK

az−−−

−+

∂∂

−

∂

∂=

∂5432

2023,11 η

εε

( )[ ]XeRRz

AAuzAAD

dtAA AAAK

az−−

−+

∂∂

−

∂

∂=

∂742

28,01 η

εε

SIMULATION RESULTS

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32

60

75

90

105

120

135

150

Tempo (h)

Conc

entra

ção d

e Glic

ose (

kgm

-3)

0

5

10

15

20

25

30

Concentração de Ácido Acrílico (kgm-3)

0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 320,00

0,75

1,50

2,25

3,00

3,75

4,50

5,25

6,00

Tempo (h)

Conc

entra

ção d

e Piru

vato

(kgm

-3)

0,0

0,5

1,0

1,5

2,0

2,5

3,0

3,5

4,0 Concentração de Lactato (kgm-3)

0,0 0,2 0,4 0,6 0,8 1,0

60

75

90

105

120

135

150

Posição axial

Conc

entra

ção d

e Glic

ose (

kgm-3 )

0

5

10

15

20

25

30 Concentração de Ácido Acrílico (kgm-3)

Simulation – Applications

Subsystem 2

Multitubular Catalytic Reactor

Tube-side : catalytic fixed bed

Detailed modeling

where: A = Parallel flow in the baffles holes B = Flow near the baffle endC = Parallel flow in the space between bundle of tubes and shellD = Flow between baflles and shellE = Cross Flow in the window zones

Multitubular Fixed Bed Catalitic Reactor

Co-current Design Alternative Design

Temperature Profiles

Radial mean temperature profile along the reactor length for different reactor configurations

Heat Transfer Coefficient Profiles

Co-current Design

Alternative Design

RHYDROLY

REACTOR DESIGN FOR HYDROLYSE

Adsorption

Enzymes(Cellulase,

β-glucosidase)

R3

R2

R1

AdsorbtionCellulase on cellulose and lignin, β-Glucosidase on ligninR1Cellulose to Cellobiose (Catalized by cellulase adsorbed on cellulose)R2Cellulose to Glucose (Catalized by cellulase adsorbed on cellulose)R3Cellobiose to Glucose (Catalized by non-adsorbed β-Glucosidase)

REACTION SYSTEM

312 056.1 rr

dtdG

−=

32 053.1111.1 rrdtdG

+=

21 rrdtdC

−−=

Cellulose

Cellobiose

GlucoseFig. 1 Observed time course of glucose (G) andcellobiose (G2) profiles. Enzymatic hydrolysisof AHP-pretreated sugarcane bagasse at differentinitial solid loadings (% w/w).

0

5

10

15

20

25

0 12 24 36 48 60 72Time (h)

Glu

cose

[G

] -

Cel

lob

iose

[G

2]

(g/L

)

G-1% G-3% G-5% G2-1% G2-3% G2-5%

EXPERIMENTAL DATA ANDMASS BALANCES

REACTION SCHEMES

Three reaction Scheme(General)

Two reaction Scheme(No direct glucose formationfrom cellulose)

One reaction Scheme(Nor direct glucose formationfrom cellulose neithercellobiose accumulation)

MATHEMATICAL MODELING

Enzyme adsorption on cellulose and lignin• One site Langmuir isotherm• Two sites Langmuir Isotherm

Enzyme inhibition by cellobiose and cellulose• Competitive• Non-competitive

Recalcitrance• Substrate reactivity• Substrate susceptibility

Enzyme deativation (Thermal, mechanical)

• First order kinetic

Non-mechanistical, fit experimental data,most used in the literature

Both are used in the literature. There is no consensus

α(S/S0)n+cte (S:substrate)v=v0Exp(-Krec(1-(S/S0))) (v0:adsorbed enzyme)

Very important for design of continuous reactionsystems at industrial scale

EXPERIMENTAL PROCEDURE AND KINETIC PARAMETER ESTIMATION

Adsorption• Enzyme adsorption on pretreated substrate• Enzyme adsorption on hydrolyzed substrate• Enzyme adsorption on ligninHydrolysis• Hydrolysis of pretreated substrate• Hydrolysis of partially hydrolyzed susbtrate• Hydrolysis with backgrond sugars (Cellobiose, glucose)• Fed batch (enzyme and susbtrate) hydrolysisParameter estimation with global and local optimization techniques• Genetic algorithms + quasi Newton• Simulated annealing + quasi Newton• Particle swarm method + quasi NewtonModel validation

Enzyme Loading5 FPU –CBU/g cellulose

500 FPU –CBU/g cellulose

Substrate Loading1%(W/W) 8%(W/W)

CONTINUOUS REACTION SYSTEMS I

CSTR

•Continuous substrate and enzyme feeding

n-CSTR

Continuous substrate and enzyme feeding at the first tank

n-CSTR with distributed feeding

•Ad hoc distributed feeding strategy of substrate and/or enzyme

•Model-based distributed feeding strategy of substrate and/or enzyme

Goals•Subs conc.•Subs conv.•Enzy consump.•Power Consump.•Resid time

CONTINUOUS REACTION SYSTEMS II

PFR with or without side feeding

Bafled PFR with or without side feeding

•Continuous substrate and enzyme feeding

•Ad hoc side feeding strategy or model-based side feeding strategy of substrate and/or enzyme

Goals•Subs conc.•Subs conv.•Enzy consump.•Power Consump.•Resid time•Overcome viscositylimitations

λ λ

CONTINUOUS REACTION SYSTEMS III

+

Liquefactor

Goals•Subs conc.•Subs conv.•Enzy consump.•Power Consump.•Resid time•Overcome viscositylimitations

Reactors•Liquefactor + n-CSTR•Liquefactor + PFR•Liquefator + Bafled PFR

REACTOR MODELING

n-CSTR Microfluid model

n-CSTR Macrofluid model• Ideal residence time distribution

• Substrate conversion

)()1(

i

iiRii Sr

SSV −== −

ϕτ

itni

n

en

ttE τ

τ/

1

)!1()( −

−

−=

∫∞→

=

=−

t

t Batchh

hsh dttE

ss

X0 0

)(1

PFR

CFD based model•Virtual tracerExperiments•Virtual determination ofRTD•Application of macrofluid model

)( h

hR

SrdSdV

−=ϕ

RESULTS FOR n-CSTRMacrofluid Model

10

20

30

40

50

60

70

80

90

100

110

120

0,650 0,670 0,690 0,710 0,730 0,750Xc

tao[

h]

NR=1 NR=2NR=3 NR=5NR=20 PFR

Microfluid Model

10

20

30

40

50

60

70

80

90

100

110

120

0,650 0,670 0,690 0,710 0,730 0,750Xc

tao[

h]N=1 NR=2NR=3 NR=5NR=20 PFR

Initial bagasse concentrationST0=50 g/L; initial cellulose concentrationSC0=40g/L.

Fig. 2 Total mean hydraulicresidence time (tao=τ) as afunction of cellulose conver-sion (Xc) predicted by the macrofluid and microfluidmodel.

CFD APPLIED TO REACTOR DESIGN I

ANSYS CFX (of Ansys Inc., EUROPE)xy velocity field Modeling approaches

Pseudo-homogeneous suspension with apparent rheological properties

‘or’

Multiphase

•Eulerian-Eulerian approach

•Eulerian-Lagrangian approach

CFD APPLIED TO REACTOR DESIGN IIBaffled PFR

Mesh details andPipe geometry

CFD APPLIED TO REACTOR DESIGN IIBaffled PFR

Predicted solids volume fraction distribution (1)and solid velocity (2)

1.

1.

2.

2.

HYDROTREATING OF MIDDLE DISTILLATES IN A TRICKLE BED REACTOR

The hydrodesulfurization (HDS), hydrodenitrogenation(HDN), hydrodeoxygenation, hydrocraking and saturativehydrogenation of middle distillates has been studied in thiswork.

An adiabatic diesel hydrotreating trickle bed packedreactor was simulated numerically by a heterogeneousmodel in order to check up the behaviour of this specificreaction system. Alternative design is proposed

The model consists of mass and heat balance equations for the fluid phase as well as for the catalyst particles, and take into account variations in the physical properties as well as of the heat and mass transfer coefficients. Heterogeneous model is developed

GAS in

LIQUID in

QUENCH

GAS out

LIQUID out

Bed 2

Bed 1

SHHnHydrocarboH2SnHydrocarbo 222 +=→+=

OHHnHydrocarboHOHnHydrocarbo 22 +−→+−

332 NHHnHydrocarboH3NnHydrocarbo +≡→+−

1 - Sulfur – containing hydrocarbons:

423 CHHnHydrocarboHCHnHydrocarbo +−→+−

2 - Oxygenated hydrocarbons:

3 - Nitrogenated hydrocarbons:

4- Hydrogenated hydrocrackable hydrocarbons:

5 - Unsaturated hydrocarbons with double bonds:

2HnHydrocarbo2HnHydrocarbo =→+

REACTOR PREDICTIONS

0 2 4 6 8 10650660670680690700710720730740750760770780

Tem

pera

ture

(K)

Bed length (m)

Figure 1 – Temperature profile along the reactor length.

0 2 4 6 8 100,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0,9

1,0

Conv

ersi

on

Bed length (m)

Figure 2 – Sulfur conversion profile along the reactor length.

Pressure : 96 atm

0 2 4 6 8 10

650

655

660

665

670

675

680

685

690

695Te

mpe

ratu

re (K

)

Bed length (m)

Figure 3 – Temperature profile along the reactor length.

0 2 4 6 8 100,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7Co

nver

sion

Bed length (m)

Figure 4 – Sulfur conversion profile along the reactor length.

Pressure: 68 atm

Efficient Mathematical Procedure for Calculating

Dynamic Adsorption Process

System for Adsorption Process

Different numericalmethods

Different equilibriumrelationships

Different operationalparameters,

and adsorbentcharacteristics

Different modelling approach

Feed Conditions:single adsorbate

binary or multicomponentcontinuos or pulse

Arrangement of the columns:fixed

in sequencysimulated moving bed

Equilibrium isothermsAdsorbent type and

characteristicsMass transfer model

Column parameters:dimensionsbed porosity

CONCENTRATION BREAKTHROUGH CURVESCONCENTRATION-DISTANCE PROFILES

MONOCOMPONENT ANDMULTICOMPONENT

ADSORBENT LOADING BREATHROUGH CURVESADSORBENT LOADING PROFILES

ELUTION CURVES (CHROMATOGRAPHY)

TYPES OF RESULTS

In the developed software:

•1 different numerical methods •1 different isotherms•1

were carried out in order to be possible to take decisions in relation to:

1 the evaluation of an operating adsorber 1 the possibility to apply this separation process for

recovering a given component from a mixture

Simulation of packed bed adsorption columnsusing the pore diffusion model, in which two masstransfer processes were considered: the external mass transfer from the bulk

liquid phase to the particle surface

internal pore diffusion within the adsorbent particle itself

Model and Solution

In the model formulation the following assumptions were made

• Diffusion coefficients independent of the mixture composition

• Spherical particles with equal sizes• Constant temperature and porosity• Not including axial dispersion

• Solution Procedure: orthogonal collocation method coupled with the DASSL routine

0 2000 4000 6000 8000 10000

0,0

0,20,40,6

0,81,0

1,2DELTA 200

c/c 0

t(s)

10 Elem 20 Elem 40 Elem 80 Elem Exper.

0 2000 4000 6000 8000 10000

0,0

0,2

0,4

0,6

0,8

1,0

1,2 DELTA 12.5

C/C

0

t (s)

10 Elem 20 Elem 40 Elem Experim.

0 2000 4000 6000 8000 10000

0,0

0,2

0,4

0,6

0,8

1,0

1,2 DELTA 25

C/C

0

t (s)

10 Elem 20 Elem 40 Elem Experim.

0 2000 4000 6000 8000 10000

0,0

0,20,40,6

0,81,0

1,2 DELTA 100

c/c 0

t(s)

10 Elem 20 Elem 40 Elem 80 Elem Exper.

Alternative Process Modeling

Fuzzy Logic

Artificial Neural Networks

Neuro Fuzzy

Hybrid Modeling

STATE UNIVERSITY OF CAMPINAS BRAZILDepartment of Chemical Engineering

SOFT SENSOR FOR MONITORING AND CONTROL OF AN INDUSTRIAL POLYMERIZATION PROCESS

OBJECTIVE:

To develop a Soft Sensor for polymer viscosity of an industrial PET Process.

PET Plant- the liquid phase (105.000 ton/year)

Figure 3- Schematic of virtual sensor.

RESULTS AND DISCUSSIONS

Input variable Name1 PE temperature T-12 SE temperature T-23 Temperature of the LP second stage T-44 Vacuum of the LP first stage P-15 Vacuum of the LP second stage P-26 HP temperature T-57 HP Vacuum P-38 Additive flow rate (catalyst). F-1

Output variable1 Measured viscosity by viscometer V-1

The variables, related to intrinsic viscosity, used for the neural net training are given in Table 1.Table 1- Variables for neural net training

0,980

0,990

1,000

1,010

1,020

0 4 8 13 17 21 25 29 33 38

Time (h)

Visc

osity

Viscosimeter Soft-Sensor

Figure 4 Viscosimeter versus Soft-Sensor (real time measurements - normalized values)

0,950

0,975

1,000

1,025

1,050

0 4 8 13 17 21 25

Time (h)

Visc

osity

Polymer viscosity Set-point

Figure 5. Process controlled using viscosity values estimated by Soft-Sensor (normalized values)

SETCIM INTEGRATION

(Industrial Test)

1340

1360

1380

1400

1420

1440

1460

1 6 11 16 21 26 31 36 41 46 51

Viscosímetro

Soft-

Sens

or

Viscosímetro Soft-Sensor

“Industrial Test”

R2 = 0.9086

1340

1360

1380

1400

1420

1440

1460

1340 1360 1380 1400 1420 1440 1460

Viscosímetro

Soft-

SEns

or

Soft-Sensor Linear (Soft-Sensor)

DATA DISPERSION (“Industrial test-several months

running ”)

H. POLIMERATION SCREEN OPERATION

HIGH POLIMERATION SCREEN OPERATION

Viscosimeter versus Soft-Sensor (Real Time Optimization)

Process Control by Soft-Sensor

Column Temperature- First Esterification Reactor

Extractive alcoholic fermentation process

•Usual existing processes: 3 or 4 tanks in series •Alternatives processes are under tests as flocculation and extractive

Flash

FilterFermentor

Vapour

Permeate

Feed

Purge

Return

Tf Pf

Ff

D pH TbT

Flash

FilterFermentor

Vapour

Permeate

Feed

Purge

Return

Tf Pf

Ff

D pH TbT

EXTRACTIVE FERMENTATION PLANT

Extractive Process

• This process was build up and validated for bioethanol production inbench scale by Atala (2004);

Development of Real-time State Estimators for Extractive Process - Introduction

- On-line monitoring by SS:- Allow real time monitoring of key variables of processes;

- Off-line monitoring:- Leads to time delay between sampling and results;- Requires advanced analytical instruments (including

near infrared spectrophotometers) → difficult to calibratedue to presence of CO2 in the media.

Software Sensor• Software sensor: an algorithm where several measurements are

processed together. The interaction of the signals from on-line instruments can be used for calculating or to estimate new quantities (e.g. state variables and model parameters) that cannot be measured in real-time.

• On-line measurements (input):- Temperatures;- Dilution rate;- pH;- Turbidity in the fermentor;- Pressure;- Feed flow rate in the flash vessel.• Off-line measurements (output):ethanol concentration in the fermentor and in the condensed stream from

the flash vessel.

Pf Ff Tf T D pH Tb

ANN-BASEDSOFT-SENSOR (1)

ANN-BASEDSOFT-SENSOR (2)

ESTIMATEDPferm

ESTIMATEDPflash

POTENTIAL INPUT VARIABLES

Pf Ff Tf T D pH Tb

ANN-BASEDSOFT-SENSOR (1)

ANN-BASEDSOFT-SENSOR (2)

ESTIMATEDPferm

ESTIMATEDPflash

POTENTIAL INPUT VARIABLES

ANN Structure Selection• Multilayer Perceptron (MLP) Neural Networks :- One of the most common ANN used in engineering;- understandable architecture and a simple mathematical form;• This NN consists of: input, output and one or more hidden

layers.• Numbers of neurons are N, M and K

fM(•)

θM

wM1

+

f2(•)

θ2

w21

+

f1(•)

θ1

w11

+

β1

W11

W12

W1M

x1

Input layer Hidden layer Output layer

xNw2N

w1N

wMN

F1(•)+

...

......

......

...

g1

fM(•)

θM

wM1

+

f2(•)

θ2

w21

+

f1(•)

θ1

w11

+

β1

W11

W12

W1M

x1

Input layer Hidden layer Output layer

xNw2N

w1N

wMN

F1(•)+

...

......

......

...

g1

θj

wj1

wj2

wjN

f(•)+...

yj

x1

x2

xN

θj

wj1

wj2

wjN

f(•)+...

yj

x1

x2

xN

(a) (b)

fM(•)

θM

wM1

+

f2(•)

θ2

w21

+

f1(•)

θ1

w11

+

β1

W11

W12

W1M

x1

Input layer Hidden layer Output layer

xNw2N

w1N

wMN

F1(•)+

...

......

......

...

g1

fM(•)

θM

wM1

+

f2(•)

θ2

w21

+

f1(•)

θ1

w11

+

β1

W11

W12

W1M

x1

Input layer Hidden layer Output layer

xNw2N

w1N

wMN

F1(•)+

...

......

......

...

g1

θj

wj1

wj2

wjN

f(•)+...

yj

x1

x2

xN

θj

wj1

wj2

wjN

f(•)+...

yj

x1

x2

xN

(a) (b)

Results and Discussion

• Even using on-line (input) datawith different levels of noise→The software sensor describedaccurately the ethanolconcentrations.

50

100

150

200

250

P f (m

mHg)

160

173

185

198

210

F f (L/

h)

32.5

33.3

34.0

34.8

35.5

T f (o C)

32.5

33.0

33.5

34.0

34.5

T (o C)

0.0

0.1

0.2

0.3

0.5

D (h-1 )

4.0

4.1

4.2

4.3

4.4

pH

19

22

25

28

31

200 250 300 350 400 450Time (h)

Tb (%

)

50

100

150

200

250

P f (m

mHg)

160

173

185

198

210

F f (L/

h)

32.5

33.3

34.0

34.8

35.5

T f (o C)

32.5

33.0

33.5

34.0

34.5

T (o C)

0.0

0.1

0.2

0.3

0.5

D (h-1 )

4.0

4.1

4.2

4.3

4.4

pH

19

22

25

28

31

200 250 300 350 400 450Time (h)

Tb (%

)

30

39

48

57

66

75Et

hano

l in th

e fe

rmen

tor (

g/L)

0.0

0.2

0.4

0.6

0.8

1.0

Dilut

ion fa

ctor (

h-1)

340

358

376

394

412

430

200 250 300 350 400 450Time (h)

Cond

ense

d et

hano

l (g/

L)

0.0

0.2

0.4

0.6

0.8

1.0

Dilut

ion fa

ctor (

h-1)

Dilution factor

(a)

(b)

30

39

48

57

66

75Et

hano

l in th

e fe

rmen

tor (

g/L)

0.0

0.2

0.4

0.6

0.8

1.0

Dilut

ion fa

ctor (

h-1)

340

358

376

394

412

430

200 250 300 350 400 450Time (h)

Cond

ense

d et

hano

l (g/

L)

0.0

0.2

0.4

0.6

0.8

1.0

Dilut

ion fa

ctor (

h-1)

Dilution factor

30

39

48

57

66

75Et

hano

l in th

e fe

rmen

tor (

g/L)

0.0

0.2

0.4

0.6

0.8

1.0

Dilut

ion fa

ctor (

h-1)

340

358

376

394

412

430

200 250 300 350 400 450Time (h)

Cond

ense

d et

hano

l (g/

L)

0.0

0.2

0.4

0.6

0.8

1.0

Dilut

ion fa

ctor (

h-1)

30

39

48

57

66

75Et

hano

l in th

e fe

rmen

tor (

g/L)

0.0

0.2

0.4

0.6

0.8

1.0

Dilut

ion fa

ctor (

h-1)

340

358

376

394

412

430

200 250 300 350 400 450Time (h)

Cond

ense

d et

hano

l (g/

L)

0.0

0.2

0.4

0.6

0.8

1.0

Dilut

ion fa

ctor (

h-1)

Dilution factor

(a)

(b)

SOFT SENSOR FOR CONCENTRATION

'

Penicillinprocess

RNN -+

Kalman filtertrainingweight

adjustment

State measurement

Kalman filter(NLSTC)

Error

SubstrateN

Air flow

The proposed non-linear Self-tuning controller scheme

0 20 40 60 80 100 1205

10

15

20

25

30

35

Process Kalman filter

Biom

ass

conc

entra

tion

(g/l)

Time (h)

Estimation of the biomass concentration

0 20 40 60 80 100 120-2000

0

2000

4000

6000

8000

10000

12000

14000

Process Kalman filterPe

nicillin

conc

entra

tion (

g/l)

Time (h)

Estimation of the Penicillin concentration with the multiple extended Kalman filter algorithm

Fractional Brownian motion as a model for an industrial Air-lift Reactor

fBm (Mandelbrot, 1968) BH(t+τ)-BH(t) é estatisticamente igual ao [BH(t+τr)-BH(t)]/rH

fGn: definido como derivado do fBm: fGn = BH(t+1)-BH(t)

Comparação entre o sinal de pressão e o ruído Gaussiano

fracionário (fGn)

0 500 1000 1500 2000 25003.18

3.2

3.22

3.24

3.26

3.28

3.3

3.32

Industrial Air-Lift Reactor Data Fractional Brownian Modelwith H = 0.7

0 500 1000 1500 2000 2500-4

-3

-2

-1

0

1

2

3

4

Synthesis of a fuzzy model for linking synthesis conditions with molecular

characteristics and performance properties of high density polyethylene

Cognitive Dynamic Modely(k)- prediction by linear equation – Takage Sugenoapproach:

y(k) = w0i + w1iu1(k-τu1) + w2iu1(k-τu1 -1) +...+ wp1iu1(k-τu1-p1)+w(p1+1)iu2(k-τu2) + w(p1+2)iu2(k-τu2 -1) +...+ w(p1+p2)1iu2(k-τu2-p2)+

w(p1+p2 +1)iy(k-1) + w(p1+p2 +2)iy(k-2) +...+w(p1+p2 +m)iy(k-m).

together with cognitive information

Implementations

• Du PONT Polymerization Process

• Rhodia Nylon-6,6 Process

• High Non Linear Process – large scale plantDeterministic model – difficult to assembly

Copolymer molar fraction

-200 0 200 400 600 800 1000 1200 1400 1600 18000,40

0,45

0,50

0,55

0,60

0,65

0,70

0,75

Y ap

tempo (h)

PLANTA MODELO

Teste para a fração molar do copolímero

0 200 400 600 800 1000

33000

34000

35000

36000

37000M

pw (k

g/km

ol)

tempo (h)

PLANTA MODELO

Validação para o peso molecular do copolímero

Polymer Molecular Weight

Nylon-66 Molecular weight

33000 34000 35000 36000 37000 38000

33000

34000

35000

36000

37000

38000

Mpw

(kg/

kmol

) - m

odel

o

Mpw (kg/kmol) - planta

par de dados da planta e do modelo

Phenol Hydrogentation Reactor

Módulo

ReactantsCoolant

Condição 1 2 3 4 5 6Ordem das entradas 23 17 7 23 17 7

Ordem do estado interno 1 1 1 2 2 2Regras 7 7 5 7 7 5

Fator erro indexado (J) 1.19e-3 1.2 e-3 1.22 e-3 1.17 e-4 1.19 e-4 1.21 e-3

100 200 300 400 500 600 7000,80

0,85

0,90

0,95

1,00

1,05

J = 1.2E-3

Tem

pera

tura

adi

men

siona

l dos

reag

ente

s Modelo determinístico Modelo Fuzzy

Tempo100 200 300 400 500 600 700

0,80

0,85

0,90

0,95

1,00

1,05

J = 1,21E-3

Tem

pera

tura

adi

men

siona

l dos

reag

ente

s

Tempo

Modelo determinístico Modelo Fuzzy

Ordem 7 para a entrada e1 para estado interno

Ordem 17 para a entrada e 1 para estado interno

Modelo Cognitivo Modelo Cognitivo

100 200 300 400 500 600 7000,80

0,85

0,90

0,95

1,00

1,05

J = 1,19E-3Te

mpe

ratu

ra a

dim

ensio

nal d

os re

agen

tes Modelo determinístico

Modelo Fuzzy

TempoOrdem 23 para a entrada e 1 para estado interno

Modelo Cognitivo

Density

MecanicalProperties

ThermicProperties

TensileProperties

Melt indexWeight molecularMolecular

Weightdistribution

Reologic properties

Correlation

Fuzzy model

Crystallinity

Properties Correlations

Output variablescontrol in deterministic

model

Density

MI

Performanceproperties

Thermicproperties

MechanicalProperties

RheologicProperties

TensileProperties

Weightmolecular

ProductFuzzyModel

Fuzzy model

FuzzyModel

Plant

Properties Product modelling from operationals dates throght Fuzzy Logic

Properties Product modelling from operationals dates throght Fuzzy Logic

PFR - trimer

Product

PFR

CSTR

Fuzzy Model - type A

Process

ConversionRate

productionMnMw

DensityPdMISE

StifnessImpact Strength

HardnessMelt StrengthStress CrackResistance

Tensile StrengthTmTcTg

crystallization percentmelt swell

softening Point

FuzzyModel -type C

Fuzzy Model - type B

Performance Properties

H2

CATCO-CAT

MonomerCo-monomer

Solvent

T PFRT CSTRP system

Feed Lateral

Results – Fuzzy model type A

Type A. Such model considers the linking of the property of flow stress exponent (SE) versus the variables of the synthesis process. The SE of a polymer is a measure of melt viscosity and is a direct measure of molecular weight distribution. The Stress Exponent, determined by measuring the flow (expressed as weight, in grams) through a melt index approaches (ASTM D 1238).

Optimization to achieve products with required properties

Optimization Based Polymer Resin Development

UFBA

Introduction

Polymerization process model

TemperatureConcentrations

Flow Rate

Input Conditions

0.10

0.20

0.30

0.40

0.50

0.60

0.00 0.20 0.40 0.60 0.80 1.00

Reactor Length (dim.)

SE (d

im.)

TemperatureConcentration

Conversion

Polymer Properties

Output Conditions

Optimization model

Improve Quality

Design of new products

Goal: Determine optimal operating policies in order to produce pre-specified polymer resins

PFR1

PFR2

CAT CC

Product

CSTR

CAT CC

H2

EthyleneHydrogenSolvent

EthyleneHydrogenSolvent

EthyleneHydrogenSolvent

Tubular ConfigurationStirred Configuration

Braskem Ethylene continuous polymerization in solution with Ziegler-Natta catalyst-

Industrial Plant

Mathematical Model

Product

CSTR

PFR2

CAT CC

MonomerH2Solvent

WoutWR

FZ 1

W0

B2

W1

Br+1

Wr

Br

Wr-1

BR

WR-1

.... ....CSTR1 CSTRr CSTRR

PFRJ+1

FZ r FZ R

PFR1

PFR2Product

CSTR

CAT CCH2

MonomerH2Solvent

W1 Wj WJ

WoutWR

....

Fj FJ

WP

B2

W1

Br+1

Wr

Br

Wr-1

BR

WR-1

.... ....

PFR1

CSTR1 CSTRr CSTRR

PFR J+1

PFRj PFRJ

Stirred Configuration

Tubular Configuration

Polymer Specification

• Melt Index (MI):

• Stress Exponent (SE):

• Density (DS):

( )βα wMWMI ⋅=

( )PDSE

⋅⋅+=

βγα exp1

SEMIDS ⋅+⋅+= γβα )log(

Desired polymer properties

end-point constraints of the optimization

• Specification at the end of reaction (z=zf)Embiruçu et al. (2000)

Objective Function

• Different operating policies can yield the same resin

Maximize Profit

( ) €/h SSCCCCCATCATHHMMPE WbWbWbWbWbWa ⋅+⋅+⋅+⋅+⋅−⋅=Φ

where

a: polyethylene sales price (€/kg)

b: reactant costs (€/kg)

W: mass flow rates (kg/h)

• Objective Function

Decision Variables

CSTR

PFR2Ws

CAT CC

MH2,0

TinPin

Wt

• Monomer Input Concentration (M)• Hydrogen Input Concentration (H2,0)

• Catalyst Input Concentration (CAT)

• Inlet Temperature (Tin)

• Inlet Pressure (Pin)

• Total Solution Rate (Wt)

• Side Feed (Ws)

Stirred Configuration

• Lateral Hydrogen Concentration (H2,j)• Lateral Hydrogen injection point (j)

PFR1

PFR2

CSTR

CAT CCH2,j

MH2,0

Wt

TinPin

Tubular Configuration

• Discontinuities ⇒ new stage

• Examples– Injection of mass along a tubular reactor– Reactor switch

Multi-stage Systems

z(0) z(1) z(2)

Event Event Event

f(1) f(2) ( )knf

( )1nkz − ( ) (f)n zz k =

kkkkkk ,...,nkz 1 ]z,[zz , 0),,,,,( k1-k =∈=puyxxf

0 ),,,,( =zkkkk puyxg

0)( 000 =− xx z

DAE system

0),,,,,,,,,( )()()()()()()()()( =zJ jjjjkkkkkj puyxxuyxx

Stage Transition

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.00 0.20 0.40 0.60 0.80 1.00

Reactor Length (dim.)

MI (

dim

.)Reactor Profile

PFR

PFR

CSTR

CAT CC H2

Tubular configuration

CSTR

PFR

CAT CC

Stirred configuration

PFR

0.20

0.25

0.30

0.35

0.40

0.45

0.50

0.00 0.20 0.40 0.60 0.80 1.00

Reactor Length (dim.)

MI (

dim

.)

Stage nº: 1 2 3 4 Stage nº: 1 2

Multi-stage Process

CSTR

CAT CC

Tubular configuration

CSTR

PFR2

CAT CC

Stirred configuration

Dynamic Optimization Techniques for multi-stage systems

DAE (axial coordinate) Steady-state

Analogy: axial coordinate ⇔ time

PFR1

PFR2

)(1 zf

H2

)(1 zf )(1 zf )(2 zf )(2 zf

3g3g3g

3g

)(4 zf )(4 zf )(4 zf )(4 zf

)(1 zf )(2 zf 3g )(4 zfz

)(zkfkg

: differential equation: algebraic equation: stage numberk: axial coordinatez

Results – Stirred Configuration

0.05

0.10

0.15

0.20

0.240 0.260 0.280 0.300 0.320SE (dim.)

Prof

it (d

im.)

0.0

0.2

0.4

0.6

0.8

1.0

0.240 0.260 0.280 0.300 0.320SE (dim.)

Con

cent

ratio

n (d

im.) H 2,0

CATWsM

H2,0

Ws

0.55

0.60

0.65

0.70

0.75

0.80

0.24 0.26 0.28 0.30 0.32SE (dim.)

Rev

enue

, Cos

t (di

m.)

RevenueCost

0.40

0.50

0.60

0.70

0.80

0.24 0.26 0.28 0.30 0.32SE (dim.)

Q, W

PE(d

im.)

QWPEWPE

Results – Tubular Configuration

One H2 injection point at a pre-specified length (4 stages)

0.05

0.10

0.15

0.20

0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75SE (dim.)

Prof

it (d

im.)

0.0

0.2

0.4

0.6

0.8

1.0

0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75SE (dim.)

Con

cent

ratio

n (d

im.)

H 2,0CATH 2,jM

H2,0

H2,j

Benefits of the developed toolDevelopment of a potential tool able to improve

the polymer quality or to create new resins in a simple and quick manner.

– Better customer satisfaction.

• Robust approach

– Use of Dynamic Optimization algorithms for a stationary multi-stage process.

• Versatile tool, since other polymerization processes can be used as basis.

Large Scale Plant Simulation

MODELING A FCC UNIT

RESULTS

The distillation curve was determined from thetemperature and the percentage of distillateobtained experimentally through moleculardistillation and using ASTM D1160.

0 20 40 60 80 100

0

100

200

300

400

500

600

700

Temp

eratur

e (o C)

% Distillate accumulated (% w)

Through CENPES/PETROBRAS Through Molecular Distillation

Molecular Distillation of

the Alfa petroleum

obteined 10 %of distillate acumullated

SEPARATION SECTION OF THE FCCU

Product Industrial data (ton/day) Simullation Result (ton/day) Error (%)

Fuel Gas 360.0 360.4 0.11

LPG 1167.0 1191.4 2.09

Gasoline 3534.0 3436.2 2.77

LCO 667.0 677.0 1.50

Slurry 1107.0 1067.5 3.57

Products recovery: industrial data and simulation results.

Green Ethyl Acrylate

O

O-

CCH3

CH

NH3+

O

O-

CCH3

CH

OH

O

O-

CCH3

CH2L-Alanina Lactato

Propianato

O

O-

CCH2

CH

Ácido Acrílico

Glicose Lactose Sacarose VáriosC5 e C6

S U B S T R A T O S

1 2

4

3

5 6

Fermentação1) Fermentação de ácido Láctico (ex. Lactobacilli, Bacilli Streptokokki).2) Fermentação de ácido Propiónico.3) Redução Direta (ex. Clostridium propionicum).4) Desidratação5) Conversão Química6)Caminho Oxidativo (ex. Pseudomonas aeroginosa)

FEED

REC1

TOPO

COOL

WATER

RAF

EXT

REC3

ACRYLATE

REC2

ETHANOL

STRIPPER

COOLER

EXTRACT

DISTIL2

DISTIL1

REACTOR

ACID

WASTE

Conceptual Plant design for Green Ethyl Acrylate

Reactor Mathematical ModelEquações adimensionalizadasBalanço de Massa para o Ácido Acrílico

Balanço de Energia no Tubo

Balanço de Energia do Fluido Térmico

Queda de Pressão

Aad

rBuGu

uGB

zG ... 22

2

1 +

∂∂

+∂∂

=∂∂

All

ad

l rBu

uu

Bz

... 42

2

3 +

∂∂

+∂∂

=∂∂ θθθ

( )FNTad

BdzdQ θθ −= .5

7BdzdP

ad

ad =

Solução por Colocação Ortogonal

Reactor simulationConversion for several temperatures Tubular reactor 5,0 meters long

0,0 0,2 0,4 0,6 0,8 1,0

0,0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

Conv

ersão

Coordenada Axial

Conversão @ 75 C Conversão @ 80 C Conversão @ 85 C

Conceptual Plant design for Green Ethyl Acrylate

FEED

REC1

TOPO

COOL

WATER

RAF

EXT

REC3

ACRYLATE

REC2

ETHANOL

STRIPPER

COOLER

EXTRACT

DISTIL2

DISTIL1

REACTOR

ACID

WASTE

Vazão(kmol/h) FEED

REC1 TOP COOL

WATE

RAF EXT ACRYL REC2 WAST REC3

Ácido Acríl 20,82 20,82 0,00 0,00 0,00 0,00 0,00 0,00 0,0000 0,0000 0,0000

Etanol 20,82 0,00 20,82 20,82 0,00 0,00 20,82 0,00 0,0000 0,3790 20,4409

Água 29,18 0,00 29,18 29,18 20,00 4,59 44,58 0,00 4,5999 36,3804

8,1995

Acril de Etila 29,18 0,18 29,00 29,00 0,00 22,64 6,36 19,74 2,9003 0,00 6,3595

Total (kmol/h) 100,0 21,00 79,00 79,00 20,00 27,23 71,70 19,74 7,5000 36,76 35,00

Total (Kg/h) 5.907 1.518 4.388 4.388 360 2349 2.399 1976 373,53 672,91 1726,11

Temp. (ºC) 78,0 140,5 79,0 25,00 25,0 29,9 29,3 99,4 82,42 97,10 77,71

Pressão (atm) 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0 1 0

Green Acrylic Acid – Unicamp/CTC/Braskem

Cana –fonte de açúcar

Seleção de microrganismos Otimização do meio de cultura

Seleção das rotas metabólicas

Fermentação

Ácido LácticoSeparação/purificação Cinética

Processodesidratação reduçãoÁcido Acrílico

Ácido Propiônico

Otimização dos processos

Cinéticas Modelagem Controle dos processos

Fontes petroquímicas

Acetaldeído (CH3CHO)

Lactonitrila (CH3CHOHCN

Mistura racêmica DL - ácido láctico

Adição de HCN e catalisador

Hidrólise por H2SO4

Fontes renováveis

Carboidratos fermentescíveis

Caldo fermentado

Ácido láctico L(+) ou D(-) opticamente puro

Pré-tratamento (hidrólise)

Fermentação microbiana

Recuperação e purificação

Síntese química Fermentação microbiana

SSF

PROCESSOS DE PRODUÇÃO DE ÁCIDO LÁCTICO[1]

PRODUTOS OBTIDOS A PARTIR DO ÁCIDO LÁCTICO [2]

Ácido Láctico

Açúcar

Ferm

enta

ção

descarboxilação

desidratação

redução

condensação

+ CO + H2OCO2+ H2

O

H

O

OH

O

OH + H2O

Acetaldeído

Ácido Acrílico

Ácido Propiônico

H2+ 1/2 O2

O

O

+ CO2 + 2H2O

2,3 pentanodiona

Proposed Process Intensification and Green Technology for Ethyl Acetate Production

45 ton/month

The proposed system design: coupled reactor/column configuration.

Reactor is the column reboiler

Configuration of the overall ethyl acetate process by Reactive Distillation

Sustainable Global Process (SGP)

Process environmentally non aggressive, including total water recovery and the lowest energy consumption.

All reactants are renewable

Steady State and Dynamic simulation – Reactive Distillation is more stable then configuration coupling reactor and column

Sugar and Ethanol Integrated Process

Separation units

The hydrous ethanol, with 96°GL, is an azeotropicmixture of ethanol and water, and therefore can not be more concentrated by pure distillation. The additional water removal is accomplishedin the so called dehydration process:•Azeotropic distillation

•Extraction distillation with monoethyleneglycol

•Molecular sieves

Ethanol dehydration technologies Technology Dehydrating Steam consumption Agent (kg steam/L ethanol) Azeotropic Cyclohexane 1.7 Extraction Monoethyleneglycol 0.7 Molecular sieves zeolite beads 0.6

Simulation of bioethanol production processes from sugarcane juice and

bagasse, using an Organosolv process with dilute acid hydrolysis

L O PCALaboratório de Otimização, Projeto e Controle Avançado

Goals

To perform the simulation of conventional bioethanol production process from sugarcane juice,

considering the introduction of technologies that improve its energetic efficiency

Integrate the production of bioethanol from sugarcane bagasse, using an Organosolv process

with dilute acid hydrolysis

Block flow diagram – conventional bioethanol production process

Block flow diagram –bioethanol production process from bagasse

Process simulation

• Mass balance based on literature and industry data was made on a spreadsheet

• Simulation using Hysys• Thermodynamic models:

– Until distillation: NRTL and SRK– Hydrolysis: NRTL and SRK– Extractive distillation: UNIQUAC and SRK– Azeotropic distillation: NRTL and SRK

Process simulation

• Production of 1000 m³/day of anhydrous bioethanol from sugarcane juice 500 tons of sugarcane per hour

• Integrated process: use of sugarcane bagasse as raw material. along with sugarcane juice

• Fermentation of the hydrolyzed liquor in a mixture with juice

Simulation components – hypothetical

• Since not all components present in bioethanol production are available at Hysys database, some hypothetic components were created to represent:– Conventional process components: sugarcane

bagasse (cellulose, hemicellulose and lignin), dirt, impurities (salts, organic acids), lime, phosphoric acid, yeast

– Hydrolysis components: pentose and HMF

Unit operations

• Splitters were used to represent the following equipments/operations: Sugarcane dry cleaning system Mills Screens and hydrocyclones Settler Filters and separators

• Multiple effect evaporators were represented by a system comprised by separator, valve and heat exchanger

Unit Operations

• Conversion reactors Fermentation: conversion data based on those provided

by the industry Hydrolysis: data based on experiments available in the

literature (pre-hydrolysis of sugarcane bagasse and hydrolysis of chemical grade cellulose)

• Centrifuges were simulated as solids separators

Simulation of anhydrous bioethanol production process from sugarcane juice

Sugarcane bagasse hydrolysis

Ethanol dehydration

Ethanol dehydration processes

• Two different processes were analyzed:– Extractive distillation: both conventional and

alternative configuration– Azeotropic distillation

• Solvents evaluated:– Extractive distillation: monoethyleneglycol (MEG) and

glycerin– Azeotropic distillation: cyclohexane and n-heptane

Extractive distillation with MEG –conventional configuration

Extractive distillation – alternative configuration

Azeotropic distillation

Comparison between extractive and azeotropic distillation

Parameter

Extractive Distillation Azeotropic Distillation

Conventional Alternative Ciclo-hexane

n-HeptaneMEG Glyc. MEG Glyc.

Vapor consumption (kg/L anydr ethanol) 0.43 0.47 0.41 0.56 8.0 6.1

Saturated steam pressure (bar) 6 10 / 65 6 65 2.5 2.5

Ethanol losses (%) 10-5 10-5 9x10-5 6x10-5 0.017 0.017Solvent losses (%) 0.01 0.01 0.49 0.02 0.001 0.008

Solvent in anhydrous ethanol (wt%) No contamination with solvent 0.017 0.04

Double effect distillation

Steam consumption on column reboilers – conventional and double effect

distillation

Parameter Distillation processConventional Double-effect

2.5bar steam consumption – column A 1.53 0.002.5bar steam consumption – column B 0.27 0.386bar steam consumption – extractive

column 0.35 0.35

6bar steam consumption – recovery column 0.07 0.07

Total steam consumption 2.21 0.80Steam consumption - [kg/L anhydrous ethanol]

Anhydrous bioethanol production –integrated process

Energy consumption on conventional and integrated production process

Parameter

Energy consumptionConv Int Conv Int

(kJ/kg anhydrous ethanol)

(kJ/kg sugarcane)

Heating operations 15529 22771 1052 1803Cooling operations 9940 16951 672 1342Increase on heating (%) 46 71Increase on cooling (%) 71 100Conv: conventional bioethanol production process; Int: integrated process with hydrolysis of 70 % generated sugarcane bagasse

Process integration of the biorefinery

– Thermal integration considers that 50 % of sugarcane straw is used as a fuel in the boilers

– When the amount of energy produced is equal to that required by the biorefinery, the real amount of bagasse available for hydrolysis is determined

• It was found that 60 % of the bagasse generated in the mills may be used as raw material for hydrolysis and still make the biorefinery self sufficient on its energy production, in a conventional distillation system

Products and inputs of the biorefinery after process integration

Parameter Units Conventionalprocess

Integratedprocess

Sugarcane input t/h 493 493Bagasse input t/h 0 70.7Anhydrous bioethanolproduction

m3/day 1004 1178L/t cane 84.8 99.6

Increase in production % - 17.5Pentose liquor (9 wt%) t/h - 10.3Integrated process: sugarcane juice and 60 % of sugarcane bagasse as raw materials

Fermentation cooling

• Dias, M.O.S., Maciel Filho, R., Rossell, C.E.V., Efficient cooling of fermentation vats in ethanol production, Sugar Journal, V. 70, p. 11-17, 2007

• Fermentation is usually done at 34°C• Limits ethanol content of the wine• Increases energy consumption (centrifuges and

distillation columns)• Increases stillage volume• Promotes infection and yeast inhibition

Fermentation cooling

• Fermentation cooling is done using water from rivers– increasing environmental restrictions – or coolingtowers of low efficiency

• Carrying fermentation at lower temperatures (28°C)increases ethanol content of the wine, but demandsthe use of more efficient cooling equipment

• Options considered:• More efficient cooling tower• Water accumulator• Steam jet ejector• Absorption machine

Ethanol content of the wine – impact on ethanol losses in vinasse

Considering the production of 1000 m³/day of anhydrous

bioethanol and vinasse with 0.02 wt% ethanol

Ethanol content of the wine – impact on ethanol losses in vinasse

For lower ethanol content of the wine, more wine is

necessary to produce the same amount of anhydrous bioethanol (1000 m³/day),

thus increasing energy consumption on

fermentation and distillation as well as capital costs

Fermentation cooling – water accumulator

Cooling Tower

PHEVat

Water Accumulator

Cooling water

Cooling water in

Cooling water in

Cooling water out

Fermentation cooling – steam jet ejector

Vat PHE

Evaporator

Ejector1,4 bar Steam

Make-up water

Purge

Cooling water out

Cooling water in

t=28ºC

t2

T1

T2q1

q2

q3

q4

m1

q5

Water

Control and Real Time Optimization

Non Linear Intelligent Controller ^

Processo Controlador

Modelo Neural Ajuste Controlador

Dados Passados Aprendizagem Redes Modelo Neural

Filtro Referência

J - 1 Y r

Y

Rotina de Otimização

)()()( kÛkUkê r −=

( ) ( ) ( )kYkYke wˆˆ −=

On-line Lerning

Dados Atuaisdo Processo

RNA 2Base - Padrão

ERRO 2

MenorErro

RNA 1Padrão

ERRO 1

RNA 3Nova

ERRO 3

Se erro 1 for menor

Peso

s Pa

drõe

s

Aju

ste

de P

esos

Aju

ste

de P

esos

Dados Atuaisdo Processo

RNA 2Base - Padrão

ERRO 2

MenorErro

RNA 1Padrão

ERRO 1

RNA 1Padrão

ERRO 1

RNA 3Nova

ERRO 3

RNA 3Nova

ERRO 3

Se erro 1 for menor

Peso

s Pa

drõe

s

Aju

ste

de P

esos

Aju

ste

de P

esos

Caso de Estudo 1

Processo Extrativo de Fermentação Alcoólica Contínua

Perturbação Estocástica

0 20 40 60 80 100 120 140 160 180 200

38

39

40

41

42

43

44

45

46

47C

once

ntra

ção

de P

rodu

to (k

g/m

3 )

Tempo (h)

Referência Malha Fechada Resultado com Perturbação Estocástica

CRAQUEAMENTO CATALÍTICO

Configuração da Rede

Rede composta por três camadas:

Camada de entrada: 8 variáveis para o

instante atual;

Camada de saída: 4 variáveis para o instante

futuro;

Camada oculta: 20 neurônios;

Erro inferior a 0,005;

Configuração final - 8 x 20 x 4;

RNA - FCC

Comparação entre o MPC - Neural e o PID

Controle preditivo do tipo baseado em modelo - MPC

Real Time Process Integration

Large Scale Cumene Oxidation Reactor

4 Air-Lifts Reactors in Sequence

TCR1

AIR1

FCR1

AIR1

FCR1

TCR1

AIR1

FCR1

AIR1

AIR1

TCR1

AIR1

FCR1

AIR1

TCR1

AIR1

FCR1

AIR1

Results from Industrial On Line Control

%HPOC R104 D

25,00

26,00

27,00

28,00

29,00

30,00

31,00

32,00

33,00

34,00

35,00

15/0

7/99

12:

00

16/0

7/99

00:

00

16/0

7/99

12:

00

17/0

7/99

00:

00

17/0

7/99

12:

00

18/0

7/99

00:

00

18/0

7/99

12:

00

19/0

7/99

00:

00

19/0

7/99

12:

00

20/0

7/99

00:

00

20/0

7/99

12:

00

21/0

7/99

00:

00

21/0

7/99

12:

00

22/0

7/99

00:

00

22/0

7/99

12:

00

23/0

7/99

00:

00

23/0

7/99

12:

00

24/0

7/99

00:

00

24/0

7/99

12:

00

25/0

7/99

00:

00

25/0

7/99

12:

00

26/0

7/99

00:

00

26/0

7/99

12:

00

27/0

7/99

00:

00

27/0

7/99

12:

00

28/0

7/99

00:

00

data

HPO

C (%

)

AI-114AC-114_SV

tempo

AC140_PVAC140_SP

∆SP

= 3

,5%

%HPOC R104 A

10,00

10,50

11,00

11,50

12,00

12,50

13,00

13,50

14,00

14,50

15,00

15/0

7/99

12:

00

16/0

7/99

00:

00

16/0

7/99

12:

00

17/0

7/99

00:

00

17/0

7/99

12:

00

18/0

7/99

00:

00

18/0

7/99

12:

00

19/0

7/99

00:

00

19/0

7/99

12:

00

20/0

7/99

00:

00

20/0

7/99

12:

00

21/0

7/99

00:

00

21/0

7/99

12:

00

22/0

7/99

00:

00

22/0

7/99

12:

00

23/0

7/99

00:

00

23/0

7/99

12:

00

24/0

7/99

00:

00

24/0

7/99

12:

00

25/0

7/99

00:

00

25/0

7/99

12:

00

26/0

7/99

00:

00

26/0

7/99

12:

00

27/0

7/99

00:

00

27/0

7/99

12:

00

28/0

7/99

00:

00

data

HPO

C (%

)

AI-111AC-111_SV

%HPOC R104 B

14,00

15,00

16,00

17,00

18,00

19,00

20,00

21,00

15/0

7/99

12:

00

16/0

7/99

00:

00

16/0

7/99

12:

00

17/0

7/99

00:

00

17/0

7/99

12:

00

18/0

7/99

00:

00

18/0

7/99

12:

00

19/0

7/99

00:

00

19/0

7/99

12:

00

20/0

7/99

00:

00

20/0

7/99

12:

00

21/0

7/99

00:

00

21/0

7/99

12:

00

22/0

7/99

00:

00

22/0

7/99

12:

00

23/0

7/99

00:

00

23/0

7/99

12:

00

24/0

7/99

00:

00

24/0

7/99

12:

00

25/0

7/99

00:

00

25/0

7/99

12:

00

26/0

7/99

00:

00

26/0

7/99

12:

00

27/0

7/99

00:

00

27/0

7/99

12:

00

28/0

7/99

00:

00

data

HPO

C (%

) AI-112AC-112_SV

%HPOC R104 C

20,00

21,00

22,00

23,00

24,00

25,00

26,00

27,00

28,00

29,00

30,00

15/0

7/99

12:

00

16/0

7/99

00:

00

16/0

7/99

12:

00

17/0

7/99

00:

00

17/0

7/99

12:

00

18/0

7/99

00:

00

18/0

7/99

12:

00

19/0

7/99

00:

00

19/0

7/99

12:

00

20/0

7/99

00:

00

20/0

7/99

12:

00

21/0

7/99

00:

00

21/0

7/99

12:

00

22/0

7/99

00:

00

22/0

7/99

12:

00

23/0

7/99

00:

00

23/0

7/99

12:

00

24/0

7/99

00:

00

24/0

7/99

12:

00

25/0

7/99

00:

00

25/0

7/99

12:

00

26/0

7/99

00:

00

26/0

7/99

12:

00

27/0

7/99

00:

00

27/0

7/99

12:

00

28/0

7/99

00:

00

data

HPO

C (%

)

AI-113AC-113_SV

%HPOC R104 D

25,00

26,00

27,00

28,00

29,00

30,00

31,00

32,00

33,00

34,00

35,00

15/0

7/99

12:

00

16/0

7/99

00:

00

16/0

7/99

12:

00

17/0

7/99

00:

00

17/0

7/99

12:

00

18/0

7/99

00:

00

18/0

7/99

12:

00

19/0

7/99

00:

00

19/0

7/99

12:

00

20/0

7/99

00:

00

20/0

7/99

12:

00

21/0

7/99

00:

00

21/0

7/99

12:

00

22/0

7/99

00:

00

22/0

7/99

12:

00

23/0

7/99

00:

00

23/0

7/99

12:

00

24/0

7/99

00:

00

24/0

7/99

12:

00

25/0

7/99

00:

00

25/0

7/99

12:

00

26/0

7/99

00:

00

26/0

7/99

12:

00

27/0

7/99

00:

00

27/0

7/99

12:

00

28/0

7/99

00:

00

data

HPO

C (%

)

AI-114AC-114_SV

Reator AReator B

Reator C Reator D

tempo

tempo tempo

tempo

Results from Industrial Reactor On Line Control

DMC + PCA

-4

-3

-2

-1

0

1

2

3

4

0 20 40 60 80 100n

Y

AC110AC120AC130AC140

-4

-3

-2

-1

0

1

2

3

4

0 20 40 60 80 100n

Y

AC110AC120AC130AC140

On-line Optimization

• Perfis inadequados de temperatura e concentração levam ao aumento da formação de subprodutos

• O otimizador em linha funciona em malha fechada com controladoresmultivariáveis, mantendo as concentrações e as temperaturas nos seus valores ótimos.

Função objetivo:

f(x) = (% impurezas) mínimoΣ

Restrições:

g(x) = [T1, T2, T3, T4, produção]

As concentrações das impurezas são calculadas pelo modelo

SQP Temperaturas máximas dos reatores

OTIMIZAÇÃO EM LINHA

Etapas envolvidas na otimização em linha

• Aquisição de dados do processo em tempo real;• Verificação da qualidade dos dados (filtro, intervalos de validade); • Teste de estado estacionário;• Execução do modelo e comparação entre a simulação e os dados reais do processo;• Ajuste de parâmetros do modelo para minimização dos erros;• Verificação da qualidade do ajuste;

SIMULAÇÃO EM LINHA

• Execução da subrotina de otimização;• Verificação da qualidade dos ótimos;• Decisões sobre os valores que serão enviados ao SDCD (trajetória de otimização);• Entrada no modo de espera (tempo entre otimizações);• Saída do modo de espera e re-início do algorítmo;

SP - valor do setpoint atualPV - valor atual da variável de processoOPT - valor ótimo calculadoDmax - máxima variação permitida do setpoint com relação

à variável de processoNSP - novo setpoint

OTIMIZAÇÃO EM LINHA

Trajetória de Otimização

Situações possíveis para NSP:

NSP = SP (nenhuma modificação é feita)

NSP = SP + / - Dmax (mudança incremental na direção do ótimo)

NSP = OPT (a solução ótima é admitida)

On-line Optimization with a Multivariable Controller

Unicamp software

22.00

22.20

22.40

22.60

22.80

23.00

23.20

23.40

16/0700:00

16/0706:00

16/0712:00

16/0718:00

17/0700:00

17/0706:00

17/0712:00

17/0718:00

18/0700:00

18/0706:00

18/0712:00

18/0718:00

19/0700:00

date

CH

P (%

)

CHP_PV reactor 3CHP_SP reactor 3

..... AC130_PVAC130_SP

On-line Optimization

600

650

700

750

800

850

900

07/0700:00

08/0700:00

09/0700:00

10/0700:00

11/0700:00

12/0700:00

13/0700:00

14/0700:00

15/0700:00

16/0700:00

17/0700:00

18/0700:00

19/0700:00

20/0700:00

21/0700:00

22/0700:00

date

DM

PC (k

g/h)

DMPC (kg/h)

(σ1 , x1)

(σ2 , x2)

without optimization

with online optimization

Impurities Reduction

Impureza 1 (kg/h)

σ1 / σ2 = 1.846

x1 / x2 = 1.012

Simulation Tools are able to help in process design, operation, optimization and control