emso: environment for modeling, simulation and ... r. secchi - emso... · 13 environment for...

EMSO: Environment for Modeling, Simulation and Optimization of

Biorefineries

Solutions for Process Control and Optimization

COPPE/UFRJ

11th World Bioenergy Symposium – WBS 2018

Argimiro R. Secchi

Chemical Engineering Program – COPPE

Universidade Federal do Rio de Janeiro

Technological Center, Rio de Janeiro – RJ, Brazil

Rio de Janeiro, June 19th, 2018

http://www.ibeas.org.br/ufscar.gif

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Chemical Engineering Program

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Founded in 1963

First department of COPPE, origin of the Institute

First to offer Graduate Courses in Engineering in Brazil


COPPE - UFRJ

2018

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Kinetics and Catalysis

Modeling, Simulation and Control of Chemical Processes

Biotechnology and Environmental Processes

Membrane Separation Processes

Polymer Engineering

Applied Thermodynamic

Thermofluidynamics

Interfacial Phenomena


Research Areas

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Solutions for Process Control and OptimizationCOPPE/UFRJ


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OutlineOutline

• Motivation

• Environment for Modeling, Simulation and Optimization

• Biorefinery Modeling

• Biorefinery Simulation

• Biorefinery Optimization for 1G+2G Bioethanol Production

• Final Remarks

EMSO: Environment for Modeling, Simulation and Optimization of Biorefineries

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High dependence on fossil fuels

Source: IEA, 2013

Growing energy needs

World total final consumption by

fuel (2011)

World total final consumption by

fuel (2011)

Energy consumption by sector

Energy consumption by sector

Source: BP, 2014

Energy demand increases 41% between 2012 and 2035

Increasing GHG emissions Opportunities for renewable resource

Waste and wastewater,

3%Energy supply,

26% Transport, 13%

Residential & Commercial

buildings , 8%

Industry, 19%

Agriculture, 14%

Forestry, 17%

Global GHG emissions by source (2004)

Global GHG emissions by source (2004)

Source: IPCC, 2007; EPA, 2012

U.S., 13300,

57%

Brazil, 6267, 27%

Europe; 1371; 6%

China; 696; 3%

India; 545; 2%

Canada; 523; 2%

ROW; 727; 3%

Source: USDA-FAZ, 2014

Global ethanol production 2013

(million gallons)

Global ethanol production 2013

(million gallons)

Total Emissions in 2012 = 6,526Million Metric Tons of CO2 equivalent

MotivationMotivation

• Angarita et al. (2015), Biochemical Engineering Journal, 104, 10-19.

8• DOE (2016). Bioenergy Technologies Office: Multi-year program plan – Energy Dept., USA.

Global production of biofuels Industrial routes for conversion of biomass


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SHF: Separate Hydrolysis and fermentation. Low efficiency. Older technology.SSF: Prevents enzyme inhibition by product. Disadvantages in operating conditions (≠ optimal cond.)SSCF: Only one bioreactor.CBP: Only one microorganism and one bioreactor.

• Cardona et al., (2010). Bioresource Technology, 101, 4751-4766.

Many process synthesis and design alternatives


Ionic liquid Extrusion Milling

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batch continuous

FinFout

fedbatch

Fin

continuous with product separation

Fin

Fout

FinFout

continuous tubular ... and many others!

fluidized bed


Several equipment types and operating modes

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Opportunities for process optimization

• Furlan et al. (2015), Computer Aided Chemical Engineering, 37, 1349-1354.

Need Computer Aided Process Engineering (CAPE) Tools!

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tools

applications

Environment for Modeling, Simulation and OptimizationEnvironment for Modeling,

Simulation and Optimization

PSE CAPE

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Environment for Modeling, Simulation and OptimizationEnvironment for Modeling,

Simulation and Optimization

EMSO stands for “Environment for Modeling, Simulation, and Optimization”

Development started in 2001 (by Rafael P. Soares), written in C++ language

Available in Windows and Linux

Models are written in an object-oriented modeling language

Equation-oriented simulator and optimizer

Computationally efficient for dynamic and steady-state simulations

Continuous improvements through ALSOC project:

http://www.enq.ufrgs.br/alsoc

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Thermodynamic andPhysical Properties – Plugin

Thermodynamic andPhysical Properties – Plugin

Data bank with about 2000 pure compounds

Mixture properties calculation

Ideal GasRK

SRKPR

APRASRKCPA

GERG2008Ideal Liquid

WilsonNRTL

UNIFACUNIQUAQ

F-SAC. . .

Thermodynamic models:

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All equipments or modules are simultaneously evaluated(Block decomposition can be used to explore sequential solution)

Open-source Modeling Equipment contain only chemistry

and physics of the model

Equation-Oriented SimulatorEquation-Oriented Simulator

ex: EMSO, Ascend, Jacobian, gPROMS, AspenDynamics, EcosimPro

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A process flowsheet model can be hierarchically decomposed:

Plant

Separa

tion

Sys

tem

Pretreat. System

Reaction System

Separation System

Colu

mn 1

Colu

mn

2

Colu

mn

3

Column

Feed Tray

Linked Trays

Linked Trays

Condenser

Splitter

Pump

Rebolier

Linked Trays

Tray

Tray

Tray

Tray

Object-Oriented ModelingObject-Oriented Modeling

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Tray

mass balance

energy balance

thermodynamic equilibrium

mol fraction normalization

ab

stra

ct m

od

el

liquid flow model

vapor flow model con

cre

te m

od

el (

ide

al t

ray)

efficiency model

con

cre

te m

od

el (

rea

l tra

y)


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• ABACUSS / JACOBIAN (Barton, 1999)

• ASCEND (Piela, 1989)

• Dymola (Elmqvist, 1978)

• EcosimPro (EA Int. & ESA, 1999)

• EMSO (Soares and Secchi, 2003)

• gPROMS/Speedup (Barton and Pantelides, 1994)

• Modelica (Modelica Association, 1996)

• ModKit (Bogusch et al., 2001)

• MPROSIM (Rao et al., 2004)

• Omola (Andersson, 1994)

• ProMoT (Tränkle et al., 1997)

Examples of general-purpose object-oriented modeling languages:


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• Separation Systems– Dynamic Flash

– Steady-State Flash

– Dynamic Condenser

– Steady-State Condenser

– Dynamic Reboiler

– Steady-State Reboiler

– Partial Reboiler

– Equilibrium Stage - Tray

– Splitter

– Mixer

– Cylindrical Tank

– Horizontal Cylindrical

– Column Section

– Distillation Column with Dynamic Condenser and Reboiler

– Distillation Column with Thermosyphon Reboiler and Sub-cooling

– Distillation Column with Thermosyphon Reboiler and Dynamic Condenser

– Distillation Column with Kettle Reboiler and Sub-cooling

– Rectifier Column

– Rectifier Column with Sub-cooling

– Stripping Column with Reflux

– Stripping Column with Sub-cooled Reflux

– Absorption Column with Reflux

– Absorption Column with Sub-cooled Reflux

– Stripping Column with Kettle Reboiler

– Stripping Column with Thermosyphon Reboiler

– Absorption Column with Kettle Reboiler

– Absorption Column with Thermosyphon Reboiler

• Controllers –PID Controllers (series, parallel, AW, AWBT)–Incremental PID Controllers (series, parallel, AW, AWBT)–Lead-Lag, Lag–Comparator, Sum, Ratio, Multiply, HiLoSelect–IAE –ISE

• Heat Exchangers –Simplified Shell-Tube Heat Exchanger –Rigorous Shell-Tube Heat Exchanger –Discretized Shell-Tube Heat Exchanger –Multi-Streams Heat Exchanger - MHeatex–Heat Exchanger – Heater and Cooler –Double Pipe Heat Exchanger–Plate Heat Exchanger

• Reactors–CSTR –PFR–Gibbs–Equilibrium–Batch–Fed Batch

• Pressure Changers –Pumps–Turbines–Compressors–Valves

• Didactic Models –Fogler’s book Exercises

EML – OO Model LibraryEML – OO Model Library

Petrochemical-oriented model library (2001 – 2012):

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Dynamic and steady-state simulation

Steady-state optimization (NLP, MINLP)

Dynamic and steady-state parameter estimation

Steady-state data reconciliation

Process monitoring and inferences with OPC communication

Build bifurcation diagram (interface with AUTO for DAEs)

Sensitivity analysis and case study (surface response)

Linearization of nonlinear dynamic system

State estimation and model updating (EMSO-CEKF)

Dynamic simulation with SIMULINK/SCICOS (interface with MATLAB/SCILAB)

Add new solvers (DAE, NLA, NLP)

Add external routines using the Plugins resource

What can I do with EMSO?What can I do with EMSO?

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Biorefinery ModelingBiorefinery Modeling

EMSO as platform for Sugarcane Virtual Biorefinery (since 2012)

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# Component # Component

1 water 17 HMF (hydroxymethylfurfural)

2 sucrose 18 glycerol

3 glucose 19 unknown sugars

4 xylose 20 MEG (ethylene glycol)

5 ethanol 21 sulfuric acid

6 CO2 22 phosphoric acid

7 CO 23 impurities

9 oxygen 24 cellobiose

9 nitrogen 25 ammonium hydroxide

10 hydrogen 26 cellulose

11 methane 27 hemicellulose

12 ammonia 28 ash

13 lignin 29 enzyme

14 xylan 30 yeast

15 acetic acid 31 calcium hydroxide

16 furfural 32 calcium phosphate

Physico-chemical properties

Many chemical species not present in petrochemical-oriented database

VRTherm

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Biomodel

Accessories

Heat exchangers

Mixers and splitters

Pressure changers

Reactors

Separators

Flowsheets

1. assumptions2. energy stream3. main stream4. water stream

1. duplicator and selector2. tank3. valve

1. heater2. water heater3. heat exchanger

4. hybrid heat exchanger5. water heat exchanger

1. mixer and splitter2. water mixer and splitter3. hybrid mixer

4. splitter heat5. splitter power

1. compressor2. isenthalpic valve3. pump

4. water pump5. turbine6. steam turbine

1. boiler2. digester3. enz. hydrolysis

4. fermenter5. liming tank6. phosphate tank

7. pre-treatment8. stoic. reactor

1. centrifuge2. cleaning3. column4. decanter5. dry cleaning

6. evaporator7. filter8. flash9. water flash10. mill

11. separator12. sieve13. absorption tower

1. Examples of flowsheets


Biorefinery Model Library

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Block Diagrams


25• Furlan et al. (2012), Computers and Chemical Engineering, 43 , 1–9.

Biorefinery SimulationBiorefinery Simulation

1G + 2G ethanol-from-sugarcane production plant

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Co-generation plant using biomass or lignin

Biorefinery SimulationBiorefinery Simulation

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Biorefinery OptimizationBiorefinery Optimization

• Carpio et al. (2017), Computer Aided Chemical Engineering, 40, 2065-2070.

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Biorefinery OptimizationBiorefinery Optimization

Retro-Techno Economic Analysis

Rea

ctio

n yi

eld

iso-economics NPV = 0

Bio

cata

lyst

yie

ld

iso-economics NPV = 0

• Furlan et al. (2016), Industrial & Engineering Chemistry Research, 55 (37), 9865–9872.

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• CAPE tools are available for bioprocess synthesis and design;

• Simulation and optimization of biorefineries illustrate the potential of the developed model library;

• Physico-chemical properties database need to be enlarged;

• The power and availability of computer hardware and software have increased our ability to model complex phenomena in biochemical processes. In fact, we are probably limited now more by what can or cannot be measured experimentally, than by techniques for solving equations;

• Particular importance are model-based state estimation techniques which compensate for the scarcity of online sensors for bioprocesses.

Final RemarksFinal Remarks

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TeamTeam

ProfessorsAntônio J.G. Cruz (UFSCar)Argimiro R. SecchiCaliane B.B. Costa (UEM)Elba P.S. Bon (Bioetanol)Felipe F. Furlan (UFSCar)Frederico W. TavaresKese P.F. Alberton, EQRoberto C. Giordano (UFSCar)Maurício B. de Souza Jr.Príamo A. Melo Jr.Tito L.A. Moitinho

Engineers:Bruno L. NogueiraDasciana Rodrigues (Embrapa)Jurgen L. BregadoRicardo S. Teixeira (Bioetanol)Rodrigo R.O. Barros (Bioetanol)Rossano Gambetta (Embrapa)

Secretariat: Rosemary Cezar

PhD students:Alex F. TeixeiraAtaíde S. AndradeCaio F. C. MarcellosDaniel M. ThomazEliza H. C. ItoFelipe C. CunhaJavier A. AngaritaJeiveison G. S. S. MaiaLeonardo D. RibeiroMaria Rosa T. GoesRafael B. DemunerReinaldo C. SpelanoRoymel R. CarpioSergio A. C. GiraldoThamires A. L. Guedes

Post-Docs:José Mauel G. T. PerezLeonardo S. SouzaSimone C. Miyoshi

MSc students:Allyne M. dos SantosAndré F. F. SouzaMaría Jimena F. QuagliataMariana CarvalhoMariana K. MoroMario G. Neves Nt.Otávio F. IvoPedro C. N. FerreiraThiago C. d’ÁvilaVitor P. Paixão

Undergrad. students:Bruno BezCarlos M. M. FonsecaIsabella Q. SouzaLucas F. BernardinoLucas MarquesPedro DelouSilvio Cisneiros Nt.Thales S. M. GamaVictor C. Gomes

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You are very welcome to visit us

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... thank you for your attention!

Process Modeling, Simulation and Control Lab

• Prof. Argimiro R. Secchi, D.Sc.

• Phone: +55-21-3938-8307

• E-mail: [email protected]

Prof. Maurício B. de Souza Jr., D.Sc.

• Phone: + 55-21-3938-7315

• E-mail: [email protected]

• http://portal.peq.coppe.ufrj.br/index.php/areas-de-pesquisa/modelagem-simulacao-e-processos

http://www.enq.ufrgs.br/alsoc

Solutions for Process Control and Optimization

COPPE/UFRJ

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ReferencesReferences• Barton, P.I., Pantelides, C.C., 1994, The modeling of combined discrete-continuous processes, AIChE J., 40, 966–979.

• Bogusch, R., Marquardt, W. 1997. A formal representation of process model equations. Comp. & Chem. Engng. 21 (10) 1105-111.

• Cardona, C.A., Quintero, J. A., Paz, I. C., 2010. Production of Bioethanol from Sugarcane Bagasse: Status and Perspectives, Bioresource Technology, 101, 4754-4766.

• Carpio, R.R., Giordano, R.C., Secchi, A.R., 2017. Enhanced Surrogate Assisted Global Optimization Algorithm Based on Maximizing Probability of Improvement Method, Computer Aided Chemical Engineering, 40, 2065-2070.

• EA International, ESA. 1999. EcosimPro ver. 3.0: Getting started, users manual, modeling language (EL), modeling and simulation guide, and mathematical algorithms. Madrid, Spain: EA International.

• Elmqvist, H., Bruck, D., Otter, M. 1999. Dymola: Dynamic modeling laboratory: User’s manual. Version 4.0. Lund, Sweden: Dynamic AB.

• Elnashaie, S.S.E.H., Chen, Z., Garhyan, P., Prasad, P., Mahecha-Botero, A.. 2006. Practical Implications of Bifurcation and Chaos in Chemical and Biological Reaction Engineering. Int. J. Chemical Reactor Engineering, 4, 1-41.

• Furlan, F.F., Costa, C.B.B., Fonseca, G.C., Soares, R.P., Secchi, A.R., Cruz, A.J.G., Giordano, R.C., 2012. Assessing the Production of First and Second Generation Bioethanol from Sugarcane through the Integration of Global Optimization and Process Detailed Modeling, Computers and Chemical Engineering, 43, 1–9.

• Furlan, F.F., Giordano, R.C., Costa, C.B.B, Secchi, A.R., Woodley, J.M., 2015. Process Alternatives for Second Generation Ethanol Production from Sugarcane Bagasse, Computer Aided Chemical Engineering, 37, 1349-1354.

• Furlan, F.F., Costa, C.B.B., Secchi, A.R., Woodley, J.M., R.C. Giordano, 2016. Retro-Techno-Economic Analysis (RTEA): using (bio)process systems engineering tools to attain process target values, Ind. & Engineering Chemistry Research, 55 (37), 9865–9872.

• Angarita, J.D., Souza, R.B.A., Cruz, A.J.G., Biscaia Jr., E.C., Secchi, A.R., 2015. Kinetic Modeling for Enzymatic Hydrolysis of Pretreated Sugarcane Straw, Biochemical Engineering Journal, 104, 10-19.

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ReferencesReferences

• Modelica Association. 1996. Modelica: A unified object-oriented language for physical systems modeling: Tutorial, rationale and language specification. Retrieved from http://www.modelica.org.

• Piela, P.C. 1989. ASCEND:Anobject-oriented environment for modeling and analysis. Ph.D. diss., Engineering Design Research Center, Carnegie Mellon University, Pittsburgh, PA.

• Rao, R.M., Rengaswamy, R., Suresh, A.K. and Balaraman, K.S. 2004. Industrial Experience with Object-Oriented Modelling FCC Case Study. Chem. Engng. Res. & Des., 82 (A4) 527–552.

• Rodrigues, R., Soares, R.P. and Secchi, A.R. 2010. Teaching Chemical Reaction Engineering Using EMSO Simulator. Computer Applications in Engineering Education, 18 (4) 607-618.

• Soares, R.P. and Secchi, A.R. 2003. EMSO: A New Environment for Modeling, Simulation and Optimization. ESCAPE 13, Lappeenranta, Finlândia, 947 – 952.

• Soares, R.P. and Secchi, A.R. 2005. Direct Initialisation and Solution of High-Index DAE Systems, ESCAPE 15, Barcelona, Spain, 157–162.

• Valle, E.C., Soares, R.P., Finkler, T.F. and Secchi, A.R. 2008. A New Tool Providing an Integrated Framework for Process Optimization, EngOpt 2008 - International Conference on Engineering Optimization, Rio de Janeiro, Brazil.

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