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Universidade Estadual de Campinas- UNICAMP       School of Chemical Engineering   EXPERIENCE ON SYSTEMINTEGRATION AND SIMU...
MOTIVATION• Process Simulation– Evaluation of several possible routes – routes discrimination–Investigation of different s...
Process Simulation (cont.)-Preliminary evaluation of costs, water andenergy consumption-Studies of variable interaction an...
PROCESS MODELLINGSteady State ModelDynamic ModelSimplified versus Detailed ModelPhysico-Chemisty based Models (Determinist...
Process SimulationSystem –can be seen a set of subsystem dependingupon of required investigationInteraction among subsyste...
Subsystem 2 - Equipment - peace of the plant where thechanges (reactions, mixtures or separations) areoccurring. In this c...
System IntegrationThere exist an incentive for highoperational performance operationProcess optimization begins with bette...
System IntegrationLarge Plant Optimization and ControlRTO (Real Time Operation): Integrate economicobjectives and controlS...
Optimization Strategies    Two main strategies are to be           implemented:            One layer approach           ...
One layer approach Economical optimization problem is solved      together with the control problem       very sensitive ...
controller/                   optimizer                   Estimation                     block                            ...
Two layers approachhierarchical control structure where thereis an optimization layer that calculates set-     points to ...
Optimizer                         setpoints                  Controller                  Estimation                    blo...
Advanced Controllers• CONTROLADORES LINEARES• NON LINEAR CONTROLLERS• PREDICTIVE CONTROLLERS• ROBUST CONTROLLERS• ADAPTIVE...
Simulation – ApplicationsSubsystem 1
STRUCTURED MATHEMATICAL         MODEL FOR ETHANOL PRODUCTIONPossible to handle with substrate to drive             the fer...
STRUCTURED MATHEMATICAL MODEL           Representative Metabolic Route (F. Lei et al. Journal of Biotechnology 88 (2001) 2...
Mass balance equations and reaction rate    of the model     ∂S glu cos e                         = −(R1 + R7 )X + D (S fe...
∂S acetaldehyde                      = (0.5 R3 − R4 − R6 )X − D (S acetaldehyde )        ∂t                 s acetaldehyde...
∂S ethanol              = (1.045 R6 )X − D(S ethanol )      ∂t   ∂X      = (0.732 R7 + 0.619 R8 )X − D( X )   ∂t∂X a     =...
∂X Acdh        = (R9 − R11 ) − (0.732 R7 + 0.619 R8 )X Acdh  ∂tR11 = k11 X Acdh• Mass balance equations → 8• Kinetic param...
CSTR simulations
TRS → Total Reductor Sugars
Batch simulations
Some Chemical Products via fermentation                                                                            Acetal...
 Other Products to be obtained from biomass                                                               Etileno        ...
 Fermentation process – piuvirate is formed in glycolysys     GLICOSE               ATP               ADP  Glicose 6-fosf...
GLICOSE                                          Rota (EMP)                                     10 reações sucessivas     ...
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).      ...
Para desenvolvimento deste modelo foi considerado:    Continuous isothermal process    heterogeneous model ;    biomass co...
Metabolic route
Model Reactions
Reaction Rates              S                SR1 = k1           X a + k1a         Xa           S + K1           S + K1a   ...
Mass Balances for the solid phase Glicose                    ∂S        D AS 1 ∂  2 ∂S                                  ...
Mass Balance for the Fluid Phase Glicose Piruvato                    ∂S                    dt                           ...
 SIMULATION RESULTS                                  150                                                                 ...
150                                                                                 30                                    ...
Simulation – ApplicationsSubsystem 2
Multitubular Catalytic Reactor          Tube-side : catalytic fixed bed
Detailed modelingwhere: A = Parallel flow in the baffles holes       B = Flow near the baffle end       C = Parallel flow ...
Multitubular Fixed Bed    Catalitic ReactorCo-current Design   Alternative Design
Temperature ProfilesRadial mean temperature profile along the reactor length for different                      reactor co...
Heat Transfer Coefficient Profiles      Co-current Design                          Alternative Design
RHY    REACTOR DESIGN FORDRO        HYDROLYSELY
REACTION SYSTEMAdsorbtionCellulase on cellulose and lignin, β-Glucosidase on ligninR1Cellulose to Cellobiose (Catalized ...
EXPERIMENTAL DATA AND                                                            MASS BALANCES                            ...
REACTION SCHEMESThree reaction Scheme(General)Two reaction Scheme(No direct glucose formationfrom cellulose)One reaction S...
MATHEMATICAL MODELINGEnzyme adsorption on cellulose and lignin• One site Langmuir isotherm                                ...
EXPERIMENTAL PROCEDURE AND KINETIC              PARAMETER ESTIMATIONAdsorption                                          En...
CONTINUOUS REACTION SYSTEMS IGoals•Subs conc.                  CSTR•Subs conv.•Enzy consump.    •Continuous substrate and•...
CONTINUOUS REACTION SYSTEMS II                      λ                                   λGoals                 PFR with or...
CONTINUOUS REACTION SYSTEMS III                               Goals                               •Subs conc.             ...
REACTOR MODELINGn-CSTR Microfluid model                           PFR       VRi       S (i −1) − Si                    dVR...
RESULTS FOR n-CSTR                            Macrofluid Model         120         110      NR=1      NR=2                ...
CFD APPLIED TO REACTOR DESIGN IANSYS CFX (of Ansys Inc., EUROPE)xy velocity field                   Modeling approaches   ...
CFD APPLIED TO REACTOR DESIGN II                  Baffled PFRMesh details andPipe geometry
CFD APPLIED TO REACTOR DESIGN II           Baffled PFR  2.  1.  2.  1.       Predicted solids volume fraction distribution...
HYDROTREATING OF MIDDLE DISTILLATES     IN A TRICKLE BED REACTOR
The hydrodesulfurization (HDS), hydrodenitrogenation(HDN), hydrodeoxygenation, hydrocraking and saturativehydrogenation of...
GAS inLIQUID in               Bed 1 QUENCH                Bed 2             GAS out            LIQUID out
1 - Sulfur – containing hydrocarbons:Hydrocarbon = S + 2H 2 → Hydrocarbon = H 2 + H 2S2 - Oxygenated hydrocarbons:Hydrocar...
REACTOR PREDICTIONS                  780                  770                  760                  750                  7...
1,0               0,9               0,8               0,7               0,6  Conversion               0,5               0,...
695                  690                  685                  680Temperature (K)                  675                  67...
0,7             0,6             0,5Conversion             0,4             0,3             0,2             0,1             ...
Efficient Mathematical Procedure for CalculatingDynamic Adsorption Process
System for Adsorption              Process                  Different modelling approach                                  ...
Column parameters:                                   dimensions                                   bed porosity     Feed Co...
TYPES OF RESULTSCONCENTRATION BREAKTHROUGH CURVES    ADSORBENT LOADING BREATHROUGH CURVES  CONCENTRATION-DISTANCE PROFILES...
In the developed software:1• different numerical methods1 different isotherms•1•were carried out in order to be possible t...
Model and Solution      Simulation of packed bed adsorption columnsusing the pore diffusion model, in which two masstransf...
In the model formulation the following            assumptions were made• Diffusion coefficients independent of the  mixtur...
DELTA 200         1,2                                                                1,2         DELTA 12.5         1,0   ...
Alternative Process ModelingFuzzy LogicArtificial Neural NetworksNeuro FuzzyHybrid Modeling
STATE UNIVERSITY OF CAMPINAS BRAZIL      Department of Chemical Engineering  SOFT SENSOR FOR MONITORING AND CONTROL OF AN ...
PET Plant- the liquid phase (105.000 ton/year)
RESULTS AND DISCUSSIONSFigure 3- Schematic of virtual sensor.
The variables, related to intrinsic viscosity, used for the        neural net training are given in Table 1.        Table ...
Viscosimeter           Soft-Sensor              1,020              1,010  Viscosity              1,000              0,990 ...
Polymer viscosity         Set-point               1,050               1,025   Viscosity               1,000               ...
SETCIM INTEGRATION
(Industrial Test)              1460                 Viscosímetro        Soft-Sensor              1440              1420Sof...
“Industrial Test”                               Soft-Sensor     Linear (Soft-Sensor)              1460              1440So...
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
•Usual existing processes: 3 or 4 tanks in series•Alternatives processes are under tests as flocculation and extractive   ...
Ff                                                            Vapour                                                    Fl...
Extractive Process• This process was build up and validated for bioethanol production in  bench scale by Atala (2004);
Development of Real-time State Estimators for         Extractive Process - Introduction- On-line monitoring by SS:- Allow ...
Software Sensor• Software sensor: an algorithm where several measurements are   processed together. The interaction of the...
ANN Structure Selection•  Multilayer Perceptron (MLP) Neural Networks :-  One of the most common ANN used in engineering;-...
Results and Discussion                                                  250                                      Pf (mmHg)...
(a)                             75                                                 1.0                                    ...
Kalman filter                        training  weight                            adjustment                              ...
35                                       30         Biomass concentration (g/l)                                       25  ...
14000      Penicillin concentration (g/l)   12000                                       10000                             ...
Fractional Brownian motion as a model    for an industrial Air-lift Reactor                        fBm (Mandelbrot,       ...
Comparação entre o sinal de           pressão e o ruído Gaussiano                 fracionário (fGn)3.32                   ...
Synthesis of a fuzzy model for linking   synthesis conditions with molecularcharacteristics and performance properties    ...
Cognitive Dynamic Modely(k)- prediction by linear equation – Takage Sugenoapproach:y(k) = w0i + w1iu1(k-τu1) + w2iu1(k-τu1...
Implementations• Du PONT Polymerization Process• Rhodia Nylon-6,6 Process   • High Non Linear Process – large scale plant ...
Copolymer molar fraction             0,75                                              PLANTA                             ...
Polymer Molecular Weight                                               PLANTA                       37000                 ...
Nylon-66 Molecular weight                              38000                                                      par de d...
Phenol Hydrogentation Reactor          Módulo                    Reactants                    Coolant
Condição             1      2       3        4        5        6  Ordem das entradas       23     17       7        23    ...
1,05                                                                             Modelo determinísticoTemperatura adimensi...
Properties Correlations                                       MolecularCrystallinity   Weight molecular         Weight    ...
Properties Product modelling fromoperationals dates throght Fuzzy Logic                                          Output va...
Properties Product modelling from      operationals dates throght Fuzzy LogicMonomer                                      ...
Results – Fuzzy model type AType A. Such model considers the linking of the property of flow stress exponent (SE)versus th...
Optimization to achieve products    with required properties
UFBA   Optimization Based Polymer       Resin Development
Introduction                                             Output ConditionsInput Conditions                          Temper...
Braskem Ethylene continuous polymerization   in solution with Ziegler-Natta catalyst-               Industrial PlantStirre...
Mathematical ModelStirred Configuration                                            Product                              W1...
Polymer Specification    Melt Index (MI): MI = α ⋅ (MW )                                        β                         ...
Objective Function• Different operating policies can yield the same  resin                    Maximize Profit• Objective F...
Decision VariablesStirred Configuration                                    Tubular Configuration                          ...
Multi-stage Systems• Discontinuities ⇒ new stage system                            DAE              Event          Event  ...
Reactor Profile Tubular configuration                                                      Stirred configuration          ...
Multi-stage Process    Steady-state                                                DAE (axial coordinate)    Analogy: ax...
Results – Stirred Configuration                        0.20                                                               ...
Results – Tubular Configuration                                     One H2 injection point at a pre-specified length (4 st...
Benefits of the developed toolDevelopment of a potential tool able to improve the polymer quality or to create new resins ...
Large Scale Plant Simulation
MODELING A FCC UNIT
RESULTS                   700       Through CENPES/PETROBRAS                        Molecular                             ...
SEPARATION SECTION OF      THE FCCU
Product                       Industrial data (ton/day)   Simullation Result (ton/day)   Error (%)Fuel Gas                ...
Green Ethyl Acrylate                           SUBSTRATOS       Glicose                                                   ...
ETHANOL                                              R EC 3                                                       R EC 2  ...
Reactor Mathematical Model Equações adimensionalizadas Balanço de Massa para o Ácido Acrílico  ∂G           ∂G      ∂ 2G ...
Reactor simulationConversion for several temperatures Tubular reactor 5,0 meters long                           0,7       ...
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
Experience on System Integration and Simulation
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Experience on System Integration and Simulation

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Presentation of Rubens Maciel for the "Workshop Virtual Sugarcane Biorefinery"

Apresentação de Rubens Maciel realizada no "Workshop Virtual Sugarcane Biorefinery "

Date / Data : Aug 13 - 14th 2009/
13 e 14 de agosto de 2009
Place / Local: ABTLus, Campinas, Brazil
Event Website / Website do evento: http://www.bioetanol.org.br/workshop4

Published in: Technology, Business
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Experience on System Integration and Simulation

  1. 1. Universidade Estadual de Campinas- UNICAMP School of Chemical Engineering EXPERIENCE ON SYSTEMINTEGRATION 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
  2. 2. MOTIVATION• Process Simulation– Evaluation of several possible routes – routes discrimination–Investigation of different scenarios- Process understanding- Impact of operation variables on process performance
  3. 3. Process Simulation (cont.)-Preliminary evaluation of costs, water andenergy consumption-Studies of variable interaction and processdynamics-Operator Training-Dynamic simulation- process control strategiesmay be evaluatedDesign of Equipments and plant conceptualdesign
  4. 4. PROCESS MODELLINGSteady State ModelDynamic ModelSimplified versus Detailed ModelPhysico-Chemisty based Models (Deterministic) versusEmpiric and or Statistical ModelsHybrid ModelSingle Unit ModelsLarge Scale Plant Model
  5. 5. Process SimulationSystem –can be seen a set of subsystem dependingupon of required investigationInteraction among subsystems – made through massand heat transfer parametersSubsystem 1– an important component of the process,inside an equipment where the phenomena areintrinsically taking place- for instance catalyst particle,bagasse to be hydrolyzed and microorganism inbiotechnological process. When considered explicitly aheterogeneous model is formulated.
  6. 6. Subsystem 2 - Equipment - peace of the plant where thechanges (reactions, mixtures or separations) areoccurring. In this category it may be place reactors,separation columns, fermentors, etc.Subsystem 3 – large scale plant or a set of equipments inwhich there exist interest to studySubsystem 1 and 2 – normally require softwaredevelopment if detailed representation are desired.Subsystem 3 – simulators, including the commercialones (Hysis , Aspen, Gproms etc)
  7. 7. System IntegrationThere exist an incentive for highoperational performance operationProcess optimization begins with betterprocess controlLarge Plant Optimization and controlRTO: Integrate economic objectives andcontrolStability, controllability and safety
  8. 8. System IntegrationLarge Plant Optimization and ControlRTO (Real Time Operation): Integrate economicobjectives and controlStability, controllability and safety- may beexpressed as plant restrictionRefinery process ⇒large scale units, highproducts output, monitoring difficulties,data reconciliation
  9. 9. Optimization Strategies Two main strategies are to be implemented:  One layer approach  two layers approach Hybrid approach may be necessary
  10. 10. One layer approach 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
  11. 11. controller/ optimizer Estimation block measured outputsmeasured inputs Process non-measured non-measured outputs inputs One layer approach
  12. 12. Two layers approachhierarchical control structure where thereis 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
  13. 13. Optimizer setpoints Controller Estimation blockmeasured inputs Process measured outputsnon-measured non-measured inputs outputs Two layer approach
  14. 14. Advanced Controllers• CONTROLADORES LINEARES• NON LINEAR CONTROLLERS• PREDICTIVE CONTROLLERS• ROBUST CONTROLLERS• ADAPTIVE CONTROLLERS• HYBRID CONTROLLERS (NEURALNETWORK AND FUZZY COUPLED WITH MODELBASED CONTROLLER)
  15. 15. Simulation – ApplicationsSubsystem 1
  16. 16. STRUCTURED MATHEMATICAL MODEL FOR ETHANOL PRODUCTIONPossible to handle with substrate to drive the fermentation
  17. 17. STRUCTURED MATHEMATICAL MODEL Representative Metabolic Route (F. Lei et al. Journal of Biotechnology 88 (2001) 205-221)
  18. 18. Mass balance equations and reaction rate of the model ∂S glu cos e = −(R1 + R7 )X + D (S feed − S glu cos e ) ∂t s glu cos e s glu cos e s glu cos eR1 = k1l X a + k1h X a + k1e s glu (K 1i s acetaldehyde + 1) + K 1e s acetaldehyde X a s glu cos e + K 1l s glu cos e + K 1h s glu cos e R7 = k 7 Xa s glu cos e + K 7 ∂S pyruvate = (0.978 R1 − R 2 − R3 )X − D (S pyruvate ) ∂t s pyruvate 1 R2 = k 2 Xa s pyruvate + K 2 K 2i s glu cos e + 1 s4 R3 = k 3 pyruvate Xa s 4 pyruvate + K3
  19. 19. ∂S acetaldehyde = (0.5 R3 − R4 − R6 )X − D (S acetaldehyde ) ∂t s acetaldehydeR4 = k 4 X a X Acdh s acetaldehyde + K 4 s acetaldehyde − k 6 r s ethanolR6 = k 6 Xa s acetaldehyde + K 6 + K 6 r s ethanol∂S acetate = (1.363R4 − R 5 − R8 )X − D(S acetate ) ∂t s acetate s acetate 1R5 = k 5 X a + k 5e Xa s acetate + K 5 s acetate + K 5e K 5i s glu cos e + 1 s acetate 1R8 = k 8 Xa s acetate + K 5e K 5i s glu cos e + 1
  20. 20. ∂S ethanol = (1.045 R6 )X − D(S ethanol ) ∂t ∂X = (0.732 R7 + 0.619 R8 )X − D( X ) ∂t∂X a = (0.732 R7 + 0.619 R8 − R9 − R10 ) − (0.732 R7 + 0.619 R8 )X a ∂t   k9 s glu cos e s ethanol   1 s glu cos e R9 = + k 9e X a + k 9c Xa  s K s s ethanol + K 9e  9i glu cos e + 1 s glu cos e + K 9  glu cos e + K 9 s glu cos e s ethanol R10 = k10 X a + k10e Xa s glu cos e + K 10 s ethanol + K 10e
  21. 21. ∂X Acdh = (R9 − R11 ) − (0.732 R7 + 0.619 R8 )X Acdh ∂tR11 = k11 X Acdh• Mass balance equations → 8• Kinetic parameter → 37• Parameter adjust → Genetic Algorithm X → biomass; Xa → active cell material; XAcdh → Acetaldehyde dehydrogenase; D → dilution rate; Ki → rate constant; Ki → affinity constant; Kji → inhibition constant
  22. 22. CSTR simulations
  23. 23. TRS → Total Reductor Sugars
  24. 24. Batch simulations
  25. 25. Some Chemical Products via fermentation Acetaldeído Ácido acético Anidrido acético FERMENTATION CHEMICAL SYNTHESIS Etanol Acetato de etila Ácido acético Acetato de vinila Sugar Ácido lático Crotonaldeído Glycose Acetona Butanol Etanol Paraldeído Sacarose Butanol Acetato de butila Piridina Nicotinamida Glicol Butadieno Glioxalato Produtos químicos produzidos por fermentação
  26. 26.  Other Products to be obtained from biomass Etileno Etanol Acetaldeído FERMENTATION Ácido acético HYDROLYSIS Propano PropilenoBIOMASS Sugar Ácido acrílico Glicose Glicerol Sacarose Ácido lático Xilose Butadieno Arabinose Butanodiol Ácido succínico Produção de novos produtos químicos a partir de biomassa
  27. 27.  Fermentation process – piuvirate is formed in glycolysys GLICOSE ATP ADP Glicose 6-fosfato Frutose 6-fosfato ATP ADP Frutose 1,6-bifosfato NAD+ NADH +Pi +H+ Gliceraldeído 3-fosfato 1,3-Difosfoglicerato ADP ATP 3-fosfoglicerato Gli cos e + 2 NAD +  2 Piruvato + 2 NADH + 2 H + → 2-fosfoglicerato ∆G10 = −146kJmol − 1 Fosfoenolpiruvato ADP 2 ATP + 2 Pi  2 ATP + 2 H 2 O → ∆G10 = 61kJmol − 1 ATP PIRUVATO Processo de glicólise
  28. 28. GLICOSE Rota (EMP) 10 reações sucessivas 2 PiruvatoCondições anaeróbias Condições anaeróbias O2 2 Etanol + 2CO2 Condições aeróbias 2 Lactato CO2 2 Acetil CoA 2 Ácido Acrílico + 2H2O O2 Ciclo do ácido TCA 4 CO2 + H2 O Rota glicolítica
  29. 29. Metabolic pathways for the synthesis of acrylic acid (Straathof et al., 2005)
  30. 30.  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 model that describes the aerobic growth of Saccharomyces cerevisiae in a medium limited to glucose and / or ethanol. Model of Stremel (2001) -alternative structured model to represent the dynamic simulation of a tubular bioreactor with immobilized cells of Saccharomyces cerevisiae for alcoholic fermentation.
  31. 31. Para desenvolvimento deste modelo foi considerado: Continuous isothermal process heterogeneous model ; biomass composition: CH1,82O0,576N0,146; spherical particles ; heterofermentative process production associated with cell growth; axial dispersion . Solution by orthogonal collocation
  32. 32. Metabolic route
  33. 33. Model Reactions
  34. 34. Reaction Rates S SR1 = k1 X a + k1a Xa S + K1 S + K1a S 1R2 = k 2 Xa S + K2  L  1+  K    2i  PR3 = k 3 Xa P + K3 LR4 = k 4 Xa L + K4 L  1 R5 = k 5 1+ K S X a  L + K5   5i   S L  1 R6 = k 6  S +K + L + K 6a  K AA + 1  X a    6  6i   S   AA R7 =  k 7  S+K  X a +  k 7a   X a   7   AA + K 7 a 
  35. 35. Mass Balances for the solid phase Glicose ∂S D AS 1 ∂  2 ∂S   − (R1 + R2 )e A X − K AA = r ∂t R 2 r 2 ∂r  ∂r  Piruvato ∂P D AP 1 ∂  2 ∂P   + (0,978 R1 − R3 )e A X − K AA = r ∂t R 2 r 2 ∂r  ∂r  Lactato ∂L D AL 1 ∂  2 ∂L   + (1,023R3 − R4 − R5 )e A X − K AA = r ∂t R 2 r 2 ∂r  ∂r  Ácido Acrílico ∂AA D A( AA ) 1 ∂  2 ∂AA   + (0,8 R4 − R7 )e A X − K AA = r ∂t R 2 r 2 ∂r  ∂r  Células ∂X  X  − K A `AA = (0,732 R2 + 0,821R5 )X 1 − e − kd X ∂t   X sat   Células ativas ∂X a = (0,732 R2 + 0,821R5 − R6 − R7 ) − (0,732 R2 + 0,821R5 )X a ∂t Enzima lactato desidrogenase ∂X LADH = R6 − (0,732 R2 + 0,821R5 )X LADH ∂t
  36. 36. Mass Balance for the Fluid Phase Glicose Piruvato ∂S dt  ∂ 2 S   ∂S  1 − ε = Daz   − u  −  ∂z 2   ∂z  ε [ η (R1 + R2 )e − K A AA X ]   ∂P  ∂ 2 P   ∂P  1 − ε = Daz   ∂z  − u  + 2  ε [ η (0,978R1 − R3 )e − K A AA X ] dt    ∂z  Lactato ∂L  ∂ 2 L   ∂L  1 − ε = Daz   ∂z  − u  + 2  ε [ η (1,023R3 − R4 − R 5 )e − K A AA X ] dt    ∂z  Ácido Acrílico ∂AA dt  ∂ 2 AA   ∂AA  1 − ε = Daz   − u  ∂z 2   ∂z  + ε [ η (0,8R4 − R7 )e − K A AA X ]  
  37. 37.  SIMULATION RESULTS 150 Concentração de Ácido Acrílico (kgm-3) 30 135Concentração de Glicose (kgm-3) 25 120 20 105 15 90 10 75 5 60 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 Tempo (h) 6,00 4,0 Concentração de Lactato (kgm-3) 5,25 3,5 Concentração de Piruvato (kgm-3) 4,50 3,0 3,75 2,5 3,00 2,0 2,25 1,5 1,50 1,0 0,75 0,5 0,00 0,0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 Tempo (h)
  38. 38. 150 30 Concentração de Ácido Acrílico (kgm-3) 135Concentração de Glicose (kgm-3) 25 120 20 105 15 90 10 75 5 60 0 0,0 0,2 0,4 0,6 0,8 1,0 Posição axial
  39. 39. Simulation – ApplicationsSubsystem 2
  40. 40. Multitubular Catalytic Reactor Tube-side : catalytic fixed bed
  41. 41. Detailed modelingwhere: A = Parallel flow in the baffles holes B = Flow near the baffle end C = Parallel flow in the space between bundle of tubes and shell D = Flow between baflles and shell E = Cross Flow in the window zones
  42. 42. Multitubular Fixed Bed Catalitic ReactorCo-current Design Alternative Design
  43. 43. Temperature ProfilesRadial mean temperature profile along the reactor length for different reactor configurations
  44. 44. Heat Transfer Coefficient Profiles Co-current Design Alternative Design
  45. 45. RHY REACTOR DESIGN FORDRO HYDROLYSELY
  46. 46. REACTION SYSTEMAdsorbtionCellulase on cellulose and lignin, β-Glucosidase on ligninR1Cellulose to Cellobiose (Catalized by cellulase adsorbed on cellulose)R2Cellulose to Glucose (Catalized by cellulase adsorbed on cellulose)R3Cellobiose to Glucose (Catalized by non-adsorbed β-Glucosidase) Enzymes (Cellulase,β-glucosidase) Adsorption R3 R1 R2
  47. 47. EXPERIMENTAL DATA AND MASS BALANCES G-1% G-3% G-5% G2-1% G2-3% G2-5% Cellulose 25Glucose [G] - Cellobiose [G2] dC 20 = −r1 − r2 dt 15 (g/L) 10 Cellobiose 5 dG2 = 1.056r1 − r3 0 dt 0 12 24 36 48 60 72 Time (h) Glucose Fig. 1 Observed time course of glucose (G) and cellobiose (G2) profiles. Enzymatic hydrolysis dG of AHP-pretreated sugarcane bagasse at different = 1.111r2 + 1.053r3 initial solid loadings (% w/w). dt
  48. 48. REACTION SCHEMESThree reaction Scheme(General)Two reaction Scheme(No direct glucose formationfrom cellulose)One reaction Scheme(Nor direct glucose formationfrom cellulose neithercellobiose accumulation)
  49. 49. MATHEMATICAL MODELINGEnzyme adsorption on cellulose and lignin• One site Langmuir isotherm Non-mechanistical, fit experimental data,• Two sites Langmuir Isotherm most used in the literatureEnzyme inhibition by cellobiose and cellulose• Competitive• Non-competitive Both are used in the literature. There is no consensusRecalcitrance• Substrate reactivity α(S/S0)n+cte (S:substrate)• Substrate susceptibility v=v0Exp(-Krec(1-(S/S0))) (v0:adsorbed enzyme)Enzyme deativation (Thermal, mechanical)• First order kinetic Very important for design of continuous reaction systems at industrial scale
  50. 50. EXPERIMENTAL PROCEDURE AND KINETIC PARAMETER ESTIMATIONAdsorption Enzyme Loading• Enzyme adsorption on pretreated substrate 5 FPU – 500 FPU –• Enzyme adsorption on hydrolyzed substrate CBU/g CBU/g• Enzyme adsorption on lignin cellulose celluloseHydrolysis Substrate Loading• Hydrolysis of pretreated substrate 1%(W/W) 8%(W/W)• 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
  51. 51. CONTINUOUS REACTION SYSTEMS IGoals•Subs conc. CSTR•Subs conv.•Enzy consump. •Continuous substrate and•Power Consump. enzyme feeding•Resid time 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
  52. 52. CONTINUOUS REACTION SYSTEMS II λ λGoals PFR with or without side feeding•Subs conc. Bafled PFR with or without side feeding•Subs conv.•Enzy consump. •Continuous substrate and enzyme feeding•Power Consump.•Resid time •Ad hoc side feeding strategy or model-based•Overcome viscosity side feeding strategy of substrate and/orlimitations enzyme
  53. 53. CONTINUOUS REACTION SYSTEMS III Goals •Subs conc. •Subs conv. •Enzy consump. •Power Consump. •Resid time •Overcome viscosityLiquefactor limitations Reactors •Liquefactor + n-CSTR •Liquefactor + PFR •Liquefator + Bafled PFR +
  54. 54. REACTOR MODELINGn-CSTR Microfluid model PFR VRi S (i −1) − Si dVR dS hτi = = ϕ =− ϕ r ( Si ) r (S h )n-CSTR Macrofluid model CFD based model• Ideal residence time distribution •Virtual tracer t n −1 E (t ) = e −t / τ i Experiments (n − 1)!τ in •Virtual• Substrate conversion determination of t →∞  sh 1 − X sh = ∫ s   E (t )dt RTD t =0  h 0  Batch •Application of macrofluid model
  55. 55. RESULTS FOR n-CSTR Macrofluid Model 120 110 NR=1 NR=2 Fig. 2 Total mean hydraulic residence time (tao=τ) as a NR=3 NR=5 100 NR=20 PFR 90 80 function of cellulose conver- sion (Xc) predicted by thetao[h] 70 60 50 macrofluid and microfluid 40 model. 30 20 10 0,650 0,670 0,690 0,710 0,730 0,750 Microfluid Model Xc 120 110 N=1 NR=2 NR=3 NR=5 100 NR=20 PFR 90 Initial bagasse concentration 80 tao[h] 70 ST0=50 g/L; 60 initial cellulose concentration 50 40 SC0=40g/L. 30 20 10 0,650 0,670 0,690 0,710 0,730 0,750 Xc
  56. 56. CFD APPLIED TO REACTOR DESIGN IANSYS 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
  57. 57. CFD APPLIED TO REACTOR DESIGN II Baffled PFRMesh details andPipe geometry
  58. 58. CFD APPLIED TO REACTOR DESIGN II Baffled PFR 2. 1. 2. 1. Predicted solids volume fraction distribution (1) and solid velocity (2)
  59. 59. HYDROTREATING OF MIDDLE DISTILLATES IN A TRICKLE BED REACTOR
  60. 60. 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 proposedThe model consists of mass and heat balance equations forthe fluid phase as well as for the catalyst particles, and takeinto account variations in the physical properties as well asof the heat and mass transfer coefficients. Heterogeneousmodel is developed
  61. 61. GAS inLIQUID in Bed 1 QUENCH Bed 2 GAS out LIQUID out
  62. 62. 1 - Sulfur – containing hydrocarbons:Hydrocarbon = S + 2H 2 → Hydrocarbon = H 2 + H 2S2 - Oxygenated hydrocarbons:Hydrocarbon − OH + H 2 → Hydrocarbon − H + H 2O3 - Nitrogenated hydrocarbons:Hydrocarbon − N + 3H 2 → Hydrocarbon ≡ H 3 + NH 34- Hydrogenated hydrocrackable hydrocarbons:Hydrocarbon − CH 3 + H 2 → Hydrocarbon − H + CH 45 - Unsaturated hydrocarbons with double bonds:Hydrocarbon + H 2 → Hydrocarbon = H 2
  63. 63. REACTOR PREDICTIONS 780 770 760 750 740Temperature (K) 730 720 710 700 690 680 670 660 650 0 2 4 6 8 10 Bed length (m) Figure 1 – Temperature profile along the reactor length.
  64. 64. 1,0 0,9 0,8 0,7 0,6 Conversion 0,5 0,4 0,3 0,2 0,1 0,0 0 2 4 6 8 10 Bed length (m)Figure 2 – Sulfur conversion profile along the reactor length.Pressure : 96 atm
  65. 65. 695 690 685 680Temperature (K) 675 670 665 660 655 650 0 2 4 6 8 10 Bed length (m) Figure 3 – Temperature profile along the reactor length.
  66. 66. 0,7 0,6 0,5Conversion 0,4 0,3 0,2 0,1 0,0 0 2 4 6 8 10 Bed length (m)Figure 4 – Sulfur conversion profile along the reactor length.Pressure: 68 atm
  67. 67. Efficient Mathematical Procedure for CalculatingDynamic Adsorption Process
  68. 68. System for Adsorption Process Different modelling approach Different operationalDifferent numerical Different equilibrium parameters, methods relationships and adsorbent characteristics
  69. 69. Column parameters: dimensions bed porosity Feed Conditions: Arrangement of the columns: Equilibrium isotherms single adsorbate fixed Adsorbent type andbinary or multicomponent in sequency characteristics continuos or pulse simulated moving bed Mass transfer model
  70. 70. TYPES OF RESULTSCONCENTRATION BREAKTHROUGH CURVES ADSORBENT LOADING BREATHROUGH CURVES CONCENTRATION-DISTANCE PROFILES ADSORBENT LOADING PROFILES MONOCOMPONENT AND ELUTION CURVES (CHROMATOGRAPHY) MULTICOMPONENT
  71. 71. In the developed software:1• different numerical methods1 different isotherms•1•were carried out in order to be possible totake decisions in relation to:1 the evaluation of an operating adsorber1 the possibility to apply this separation process for recovering a given component from a mixture
  72. 72. Model and Solution 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
  73. 73. 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
  74. 74. DELTA 200 1,2 1,2 DELTA 12.5 1,0 1,0 0,8 0,8 0,6 c/c0 10 Elem 0,6 C/C0 0,4 20 Elem 40 Elem 0,4 0,2 80 Elem 10 Elem Exper. 20 Elem 0,0 0,2 40 Elem Experim. 0,0 0 2000 4000 6000 8000 10000 t(s) 0 2000 4000 6000 8000 10000 t (s) 1,2 1,2 DELTA 100 DELTA 25 1,0 1,0 0,8 0,8 0,6 10 Elem c/c0 0,6 20 ElemC/C0 0,4 40 Elem 0,4 10 Elem 0,2 80 Elem 20 Elem Exper. 0,2 0,0 40 Elem 0,0 Experim. 0 2000 4000 6000 8000 10000 0 2000 4000 6000 8000 10000 t(s) t (s)
  75. 75. Alternative Process ModelingFuzzy LogicArtificial Neural NetworksNeuro FuzzyHybrid Modeling
  76. 76. STATE UNIVERSITY OF CAMPINAS BRAZIL Department of Chemical Engineering SOFT SENSOR FOR MONITORING AND CONTROL OF AN INDUSTRIAL POLYMERIZATION PROCESSOBJECTIVE:To develop a Soft Sensor for polymer viscosity ofan industrial PET Process.
  77. 77. PET Plant- the liquid phase (105.000 ton/year)
  78. 78. RESULTS AND DISCUSSIONSFigure 3- Schematic of virtual sensor.
  79. 79. The variables, related to intrinsic viscosity, used for the neural net training are given in Table 1. Table 1- Variables for neural net training Input variable Name 1 PE temperature T-1 2 SE temperature T-2 3 Temperature of the LP second stage T-4 4 Vacuum of the LP first stage P-1 5 Vacuum of the LP second stage P-2 6 HP temperature T-5 7 HP Vacuum P-3 8 Additive flow rate (catalyst). F-1 Output variable 1 Measured viscosity by viscometer V-1
  80. 80. Viscosimeter Soft-Sensor 1,020 1,010 Viscosity 1,000 0,990 0,980 0 4 8 13 17 21 25 29 33 38 Tim e (h)Figure 4 Viscosimeter versus Soft-Sensor (real time measurements- normalized values)
  81. 81. Polymer viscosity Set-point 1,050 1,025 Viscosity 1,000 0,975 0,950 0 4 8 13 17 21 25 Tim e (h)Figure 5. Process controlled using viscosity values estimated by Soft-Sensor (normalized values)
  82. 82. SETCIM INTEGRATION
  83. 83. (Industrial Test) 1460 Viscosímetro Soft-Sensor 1440 1420Soft-Sensor 1400 1380 1360 1340 1 6 11 16 21 26 31 36 41 46 51 Viscosímetro
  84. 84. “Industrial Test” Soft-Sensor Linear (Soft-Sensor) 1460 1440Soft-SEnsor 1420 1400 R2 = 0.9086 1380 1360 1340 1340 1360 1380 1400 1420 1440 1460 Viscosímetro
  85. 85. DATA DISPERSION(“Industrial test-several months running ”)
  86. 86. H. POLIMERATION SCREEN OPERATION
  87. 87. HIGH POLIMERATION SCREEN OPERATION
  88. 88. Viscosimeter versus Soft-Sensor (Real Time Optimization)
  89. 89. Process Control by Soft-Sensor
  90. 90. Column Temperature- First Esterification Reactor
  91. 91. •Usual existing processes: 3 or 4 tanks in series•Alternatives processes are under tests as flocculation and extractive Extractive alcoholic fermentation process
  92. 92. Ff Vapour Flash Tf Pf Feed ReturnT D pH Tb Fermentor Filter Purge Permeate EXTRACTIVE FERMENTATION PLANT
  93. 93. Extractive Process• This process was build up and validated for bioethanol production in bench scale by Atala (2004);
  94. 94. 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 calibrate due to presence of CO2 in the media.
  95. 95. 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. POTENTIAL INPUT VARIABLES• On-line measurements (input): Pf Ff Tf T D pH Tb- Temperatures;- Dilution rate;- pH; ANN-BASED ANN-BASED SOFT-SENSOR (1) SOFT-SENSOR (2)- Turbidity in the fermentor;- Pressure;- Feed flow rate in the flash vessel. ESTIMATED ESTIMATED Pferm Pflash• Off-line measurements (output):ethanol concentration in the fermentor and in the condensed stream from the flash vessel.
  96. 96. 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 Input layer Hidden layer Output layer θ1 θj w11 + f1(•) ... ... ... ... x1 w1N x1 wj1 θ2 β1 w21 W11 x2 wj2 + f(•) yj ... ... ... ... + f2(•) W12 + F1(•) g1 ... ... ... ... ... ... ... ... ... ... ... ... xN w2N ... W1M θM wM1 xN wjN + fM(•) ... ... ... ... wMN (a) (b)
  97. 97. Results and Discussion 250 Pf (mmHg) 200• Even using on-line (input) data 150 100 with different levels of noise 50 210 →The software sensor described 198 Ff (L/h) 185 accurately the ethanol 173 160 35.5 concentrations. 34.8 Tf ( C) 34.0 o 33.3 32.5 34.5 34.0 T ( C) 33.5 o 33.0 32.5 0.5 0.3 D (h ) -1 0.2 0.1 0.0 4.4 4.3 pH 4.2 4.1 4.0 31 28 Tb (%) 25 22 19 200 250 300 350 400 450 Time (h)
  98. 98. (a) 75 1.0 Dilution factor (h-1) fermentor (g/L) 66 0.8 Ethanol in the 57 0.6 48 0.4 39 Dilution factor 0.2 30 0.0 Condensed ethanol (g/L)(b) 430 1.0 Dilution factor (h-1) 412 0.8 394 0.6 376 0.4 358 0.2 340 0.0 200 250 300 350 400 450 Time (h) SOFT SENSOR FOR CONCENTRATION
  99. 99. Kalman filter training  weight adjustment Error Kalman filter (NLSTC) + - RNN N Substrate Air flow State measurement Penicillin processThe proposed non-linear Self-tuning controller scheme
  100. 100. 35 30 Biomass concentration (g/l) 25 20 Process 15 Kalman filter 10 5 0 20 40 60 80 100 120 Time (h)Estimation of the biomass concentration
  101. 101. 14000 Penicillin concentration (g/l) 12000 10000 8000 6000 4000 2000 Process 0 Kalman filter -2000 0 20 40 60 80 100 120 Time (h)Estimation of the Penicillin concentration with the multiple extendedKalman filter algorithm
  102. 102. 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)
  103. 103. Comparação entre o sinal de pressão e o ruído Gaussiano fracionário (fGn)3.32 4 3.3 3 23.28 13.26 03.24 -13.22 -2 3.2 -33.18 -4 0 500 1000 1500 2000 25000 500 1000 1500 2000 2500 Industrial Air-Lift Reactor Data Fractional Brownian Model with H = 0.7
  104. 104. Synthesis of a fuzzy model for linking synthesis conditions with molecularcharacteristics and performance properties of high density polyethylene
  105. 105. 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
  106. 106. Implementations• Du PONT Polymerization Process• Rhodia Nylon-6,6 Process • High Non Linear Process – large scale plant Deterministic model – difficult to assembly
  107. 107. Copolymer molar fraction 0,75 PLANTA MODELO 0,70 0,65 0,60 Yap 0,55 0,50 0,45 0,40 -200 0 200 400 600 800 1000 1200 1400 1600 1800 tempo (h) Teste para a fração molar do copolímero
  108. 108. Polymer Molecular Weight PLANTA 37000 MODELO 36000 Mpw (kg/kmol) 35000 34000 33000 0 200 400 600 800 1000 tempo (h)Validação para o peso molecular do copolímero
  109. 109. Nylon-66 Molecular weight 38000 par de dados da planta e do modelo 37000 Mpw (kg/kmol) - modelo 36000 35000 34000 33000 33000 34000 35000 36000 37000 38000 Mpw (kg/kmol) - planta
  110. 110. Phenol Hydrogentation Reactor Módulo Reactants Coolant
  111. 111. Condição 1 2 3 4 5 6 Ordem das entradas 23 17 7 23 17 7Ordem do estado interno 1 1 1 2 2 2 Regras 7 7 5 7 7 5Fator 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 1,05 1,05 Temperatura adimensional dos reagentes J = 1,21E-3 Temperatura adimensional dos reagentes Modelo determinístico J = 1.2E-3 Modelo determinístico Modelo Cognitivo Modelo Fuzzy Modelo Cognitivo Modelo Fuzzy 1,00 1,00 0,95 0,95 0,90 0,90 0,85 0,85 0,80 0,80 100 200 300 400 500 600 700 100 200 300 400 500 600 700 Tempo Tempo Ordem 7 para a entrada e Ordem 17 para a entrada e 1 para 1 para estado interno estado interno
  112. 112. 1,05 Modelo determinísticoTemperatura adimensional dos reagentes Modelo Cognitivo J = 1,19E-3 Modelo Fuzzy 1,00 0,95 0,90 0,85 0,80 100 200 300 400 500 600 700 Tempo Ordem 23 para a entrada e 1 para estado interno
  113. 113. Properties Correlations MolecularCrystallinity Weight molecular Weight Density Melt index distribution Correlation Fuzzy model Mecanical Thermic Tensile Reologic Properties Properties Properties properties
  114. 114. Properties Product modelling fromoperationals dates throght Fuzzy Logic Output variables control in deterministic Performance model properties Thermic properties Product Density Fuzzy Fuzzy Model Model Rheologic Properties MI Mechanical Properties Weight molecular Tensile Properties Plant Fuzzy model
  115. 115. Properties Product modelling from operationals dates throght Fuzzy LogicMonomer Performance PropertiesCo-monomer StifnessCAT Impact StrengthCO-CAT Conversion Hardness Rate Melt StrengthSolvent Produc production t Stress CrackH2 Mn Resistance PFR PFR - trimer Mw Tensile StrengthT PFR Density Tm CSTR FuzzyT CSTR Pd Tc Model -P system MI Tg type C SE crystallization percentFeed Lateral Process melt swell softening Point Fuzzy Model - type A Fuzzy Model - type B
  116. 116. Results – Fuzzy model type AType 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 ofmelt viscosity and is a direct measure of molecular weight distribution. The StressExponent, determined by measuring the flow (expressed as weight, in grams) through a melt indexapproaches (ASTM D 1238).
  117. 117. Optimization to achieve products with required properties
  118. 118. UFBA Optimization Based Polymer Resin Development
  119. 119. Introduction Output ConditionsInput Conditions Temperature Concentration 0.60 Temperature 0.50 Polymerization Conversion SE (dim.) 0.40 0.30Concentrations process model 0.20 0.10 Flow Rate Polymer 0.00 0.20 0.40 0.60 0.80 Reactor Length (dim.) 1.00 Properties Improve Quality Optimization Design of new products model Goal: Determine optimal operating policies in order to produce pre-specified polymer resins
  120. 120. Braskem Ethylene continuous polymerization in solution with Ziegler-Natta catalyst- Industrial PlantStirred Configuration EthyleneTubular Configuration Hydrogen Product Solvent PFR2Ethylene EthyleneHydrogen HydrogenSolvent Solvent PFR1 CSTR H2 CAT CC CAT CC
  121. 121. Mathematical ModelStirred Configuration Product W1 W r-1 Wr W R-1 PFR2 W0 CSTR1 .... CSTRr .... CSTRR B2 Br Br+1 BR Monomer FZ 1 FZ r FZ R H2 Solvent CSTR WR W out PFRJ+1 CAT CCTubular Configuration W1 Wj WJ PFR1 PFRj .... PFRJ Product Monomer PFR2 Fj FJ H2 Solvent PFR1 W1 W r-1 Wr W R-1 WP CSTR1 .... CSTRr .... CSTRR CSTR B2 Br Br+1 BR H2 CAT CC WR W out PFR J+1
  122. 122. Polymer Specification Melt Index (MI): MI = α ⋅ (MW ) β w• 1 SE =• Stress Exponent (SE): α + γ ⋅ exp(β ⋅ PD ) DS = α + β ⋅ log(MI ) + γ ⋅ SE• Density (DS): Embiruçu et al. (2000) • Specification at the end of reaction (z=zf) Desired polymer properties end-point constraints of the optimization
  123. 123. Objective Function• Different operating policies can yield the same resin Maximize Profit• Objective Function Φ = a ⋅ WPE − (bM ⋅ WM + bH ⋅ WH + bCAT ⋅ WCAT + bCC ⋅ WCC + bS ⋅ WS ) €/h where a: polyethylene sales price (€/kg) b: reactant costs (€/kg) W: mass flow rates (kg/h)
  124. 124. Decision VariablesStirred Configuration Tubular Configuration M PFR2 Ws PFR2 H2,0 Tin M Tin Pin Wt H2,0 Pin PFR1 Wt CSTR CSTR H2,j CAT CC CAT CC• Side Feed (Ws) • Monomer Input Concentration (M) • Lateral Hydrogen injection point (j) • Hydrogen Input Concentration (H2,0) • Lateral Hydrogen Concentration (H2,j) • Catalyst Input Concentration (CAT) • Inlet Temperature (Tin) • Inlet Pressure (Pin) • Total Solution Rate (Wt)
  125. 125. Multi-stage Systems• Discontinuities ⇒ new stage system DAE Event Event Event f k (x k , x k , y k , u k , p, z ) = 0 , z ∈ [z k -1 , z k ]  k = 1,...,nk g k ( x k , y k , u k , p, z ) = 0 f(1) f(2) f (n k ) x 0 ( z0 ) − x 0 = 0 ( n k −1)z(0) z(1) z(2) z z (n k ) = z (f) Stage Transition J (j k ) (x ( k ) , x ( k ) , y ( k ) , u ( k ) , x ( j ) , x ( j ) , y ( j ) , u ( j ) , p, z ) = 0  • Examples – Injection of mass along a tubular reactor – Reactor switch
  126. 126. Reactor Profile Tubular configuration Stirred configuration PFR PFR PFR PFR CSTR CSTR H2 CAT CC CAT CCStage nº: 1 2 3 4 Stage nº: 1 2 0.60 0.50 0.50 0.45 0.40 0.40 MI (dim.) 0.30 MI (dim.) 0.35 0.20 0.30 0.10 0.25 0.00 0.20 0.00 0.20 0.40 0.60 0.80 1.00 0.00 0.20 0.40 0.60 0.80 1.00 Reactor Length (dim.) Reactor Length (dim.)
  127. 127. Multi-stage Process Steady-state DAE (axial coordinate) Analogy: axial coordinate ⇔ time Tubular configuration Stirred configuration f 4 ( z )f 4 ( z 4 (fz4)( z ) f) PFR2 PFR2 g3 g3 f1 ( zf1 ( f1)(f 2)( z ) ( z ) ) z z f2 g3 PFR1 g3 CSTR CSTR H2 CAT CC CAT CC z f1 ( z ) f2 ( z) g3 f4 ( z)f k (z ) : differential equationg k : algebraic equationk : stage numberz : axial coordinate Dynamic Optimization Techniques for multi-stage systems
  128. 128. Results – Stirred Configuration 0.20 1.0 0.8 Concentration (dim.) H 2,0 H2,0 CAT 0.15 Profit (dim.) 0.6 Ws Ws M 0.4 0.10 0.2 0.05 0.0 0.240 0.260 0.280 0.300 0.320 0.240 0.260 0.280 0.300 0.320 SE (dim.) SE (dim.) 0.80 0.80 Revenue, Cost (dim.) 0.75 Q, WPE(dim.) 0.70 0.70 Q Revenue 0.60 W PE WPE 0.65 Cost 0.50 0.60 0.40 0.55 0.24 0.26 0.28 0.30 0.32 0.24 0.26 0.28 0.30 0.32 SE (dim.) SE (dim.)
  129. 129. Results – Tubular Configuration One H2 injection point at a pre-specified length (4 stages) 0.20 1.0 Concentration (dim.) 0.8 0.15Profit (dim.) 0.6 H 2,0 H 2,0 CAT H 2,j 2,j 0.4 M 0.10 0.2 0.05 0.0 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.75 SE (dim.) SE (dim.)
  130. 130. 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.
  131. 131. Large Scale Plant Simulation
  132. 132. MODELING A FCC UNIT
  133. 133. RESULTS 700 Through CENPES/PETROBRAS Molecular Distillation of Through Molecular Distillation 600 500 the AlfaTemperature (oC) 400 petroleum obteined 10 % 300 200 100 of distillate 0 acumullated 0 20 40 60 80 100 % Distillate accumulated (% w) The distillation curve was determined from the temperature and the percentage of distillate obtained experimentally through molecular distillation and using ASTM D1160.
  134. 134. SEPARATION SECTION OF THE FCCU
  135. 135. Product Industrial data (ton/day) Simullation Result (ton/day) Error (%)Fuel Gas 360.0 360.4 0.11LPG 1167.0 1191.4 2.09Gasoline 3534.0 3436.2 2.77LCO 667.0 677.0 1.50Slurry 1107.0 1067.5 3.57 Products recovery: industrial data and simulation results.
  136. 136. Green Ethyl Acrylate SUBSTRATOS Glicose Vários Lactose Sacarose C5 e C6 O 1 2 - O FermentaçãoH3C C O - H3C C O 1) Fermentação de ácido HC Láctico (ex. Lactobacilli, HC 3 O + - Bacilli Streptokokki). NH3 H3C C O OH 2) Fermentação de ácido L-Alanina Lactato CH2 Propiónico. Propianato 3) Redução Direta (ex. Clostridium propionicum). 4 5 6 4) Desidratação 5) Conversão Química 6)Caminho Oxidativo (ex. O Pseudomonas aeroginosa) - H2C C O CH Ácido Acrílico
  137. 137. ETHANOL R EC 3 R EC 2 D ISTIL 1 STRIPPER EXTR ACT R AF AC ID TOPO AC R YLATE C OOLER FEED C OOL R EAC TOR D ISTIL 2 R EC 1 EXT WASTE WATERConceptual Plant design for Green Ethyl Acrylate
  138. 138. Reactor Mathematical Model Equações adimensionalizadas Balanço de Massa para o Ácido Acrílico ∂G  ∂G ∂ 2G  = B1 .  ∂u + u. ∂u 2  + B2 .rA  ∂z ad   Balanço de Energia no Tubo ∂θ l  ∂θ l ∂ 2θ l  = B3 .  ∂u + u. ∂u 2  + B4 .rA  ∂z ad   Balanço de Energia do Fluido Térmico = B5 .(θ NT − θ F ) dQ dz ad Queda de Pressão dP ad = B7 dz ad Solução por Colocação Ortogonal
  139. 139. Reactor simulationConversion for several temperatures Tubular reactor 5,0 meters long 0,7 0,6 0,5 Conversão 0,4 0,3 0,2 Conversão @ 75 C Conversão @ 80 C 0,1 Conversão @ 85 C 0,0 0,0 0,2 0,4 0,6 0,8 1,0 Coordenada Axial

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