Computer Aided Process Engineering GroupUniversity College London

ANALYSIS OF EXTRACTIVE ANALYSIS OF EXTRACTIVE FERMENTATION PROCESS FOR ETHANOL FERMENTATION PROCESS FOR ETHANOL

PRODUCTION USING A RIGOROUS MODEL PRODUCTION USING A RIGOROUS MODEL AND A SHORTAND A SHORT--CUT METHODCUT METHOD

Oscar J. Sánchez, M.Sc.Department of Chemical Engineering, University College LondonDepartment of Chemical Engineering, National University of Colombia at Manizales

Luis F. Gutiérrez, M.Sc.Department of Chemical Engineering, National University of Colombia at Manizales

Carlos A. Cardona, Ph.D.Department of Chemical Engineering, National University of Colombia at Manizales

Eric S. Fraga, Prof., Ph.D.Department of Chemical Engineering, University College London

Computer Process Engineering Group – University College London

INTRODUCTION

BIOETHANOL PRODUCTIONCatalytic synthesisFrom bioenergy crops

Sugar cane (juice or molasses)Starch from grains (corn, wheat)

From biomassAgriculture residues (grass)Forestry wastes (wood chips, sawdust)Industrial wastesFood processing wastesMunicipal solid waste

WHY FUEL ETHANOL?

Progressive exhaustion of world energetic resources based on non-renewable oil fuelsDark panorama in the oil marketGeneration of huge amounts of pollution gases released into the atmosphereEthanol can be utilized directly as fuel or as an oxygenate of gasoline for elevating its oxygen content

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

FUEL ETHANOL PRODUCTION FROM LIGNOCELLULOSIC BIOMASS

Not implemented yet at industrial scaleHigher costs: US$1.50 vs. US$0.88 from corn (McAloon et al., 2000)Hexose- and pentose-assimilating microorganisms recombinant Zymomonas mobilis (Leksawasdi et al., 2001; Wooley et al., 1999)Process intensification is required Reaction-separation integration

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

Process intensification by:Reaction-reaction integrationReaction-separation integrationSeparation-separation integration

PROCESS INTEGRATION AS A TOOL FOR PROCESS DESIGN

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

Continuous Fermentation

0=+− XrVFX

0)( 1110 =−− SVrSSF

VFD /=

0)( 2220 =−− SVrSSF

F F

S10 , S10 S1 , S1 , X, P

Most industrial fermentations are carried out in batch regimeContinuous fermentation offers higher productivitiesImplemented processes:

Ethanol productionSingle-cell protein production

Inhibition of growth and product formation rates by:

ProductSubstrate

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

Simultaneous Extractive Fermentation

Removal of the compound causing the inhibition through an extractive biocompatible agent (solvent)Solvent favours the migration of ethanol to solvent phaseProposed solvents for alcoholic fermentation:

n-dodecanololeyl alcohololeyl alcohol + 4-heptanone

Ways of improving:Appropriate solvent selectionAnalysis and optimal design prior experimentation

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

SCOPE AND OBJECTIVE OF THE RESEARCH

Model the extractive fermentation process for ethanol production from lignocellulosic biomass utilizing a rigorous mathematical descriptionPropose a short-cut approach for analyzing this processFormulate an overall strategy of optimization

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

0=+− XAA rVXQ

01110 =−− SAAA rVSQSF

02220 =−− SAAA rVSQSF

0**0 =+−− PAEAE rVPQPQPF

0=−−+ EAEA QQFF

S10, S20

E0

QA

QEFE

FA

S1, S2, X, P

E, P*

Modelling of Continuous Extractive Fermentation

[ ]Xrrr XXX 2,1, )1( αα −+=

[ ]Xrrr PPP 2,1, )1( αα −+=

PkP EtOH=*

21 , SS rr

Taken from Leksawadi et al., 2001

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

Coupled algorithm for the calculation of extractive fermentation process

Overall algorithm Liquid-liquid equilibrium algorithm

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

Fermentation profiles in dependence on aqueous dilution rate

0

5

10

15

20

25

30

0 0.1 0.2 0.3 0.4D Ai [h-1]

X, P

, P*

[g.L

-1]

0

20

40

60

80

100

120

S 1, S

2 [g

.L-1

]

X P P* S1 S2

024681012141618

0 0.1 0.2 0.3 0.4D Ai [h-1]

Prod

uctiv

ity [g

.L-1

.h-1

]

PrA PrE PrT

Effect of inlet aqueous dilution rate (DAi) on: (a) effluent concentrations of glucose (S1), xylose (S2), ethanol in aqueous phase (P),

ethanol in solvent phase (P*), and effluent cell concentration (X)(b) total ethanol productivity (PrT), productivity for ethanol recovered from aqueous

phase (PrA), and productivity for ethanol recovered from solvent phase (PrE)

S10 = 100 g.L-1; S20 = 50 g.L-1

(a) (b)

GAMS

Optimal DAi = 0.265 h-1

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

Effect of R = FE / FA

Effect of solvent feed flow rate/aqueous feed flow rate ratio (R) on performance of continuous extractive fermentation using n-dodecanol at Dai = 0.265 h-1, S10 = 100 g.L-1; S20 = 50 g.L-1 .

Total ethanol productivity (PrT), and productivity for ethanol recovered from solvent phase (PrE).

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

Reaction trajectories for several steady-states

Representation in the ternary diagram of the steady states achieved during the rigorous simulation of extractive fermentation using n-dodecanol for different operating conditions.S10 = 100 g.L-1; S20 = 50 g.L-1

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

Influence of DAi, R, and initial concentration of sugars

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

SHORT-CUT APPROACH

Stoichiometric relationships were considered:

Thermodynamic-topological approach:

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

Representation of extractive fermentation in a ternary diagram

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

Representation when initial concentration of sugars changes

Zone of feasible steady-states

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

OPTIMIZATION STRATEGY OF EXTRACTIVE FERMENTATION

From short-cut approach is determined the zone of feasible operating conditions: R, S10, S20, Dai

Liquid-liquid model was simplified assuming kEtOH to linearly dependent of sugar concentrationSimplified LLE model and mass balances were introduced into GAMS code

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

CONCLUSIONS

Removal of valuable products from culture broths is a promising technology for the intensification of fermentation processesRigorous analysis of the behaviour of extractive fermentation can provide useful tools for defining the best operating parameters and suitable regimes in order to increase techno-economical indexes of biotechnological transformationsproposed short-cut method based on the principles of thermodynamic-topological analysis allows getting a preliminary idea for approaching to the rigorous simulationPresented methodology makes possible the decrease in calculation time and in the number of experimental runs and helps to determine which data are required and the space of initial conditions where experimental efforts should be focused.Usefulness and advantages of this methodology was demonstrated when multivariate optimization is needed for the determination of the best operating parameters in such a complex process as the extractive fermentation

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

FUTURE WORK

Couple or embed rigorous description of the equilibrium model embedded into the GAMS code

Formulation of an objective function that considers other performance indexes like the conversion of sugars (better utilization of the feedstock) or the amount of generated wastewater (evaluation of environmental impact)

Undertake the needed experimental runs considering the theoretical results

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledgments

Computer Process Engineering Group – University College London

Acknowledgments

British CouncilDepartment of Chemical Engineering, University College LondonColombian Institute for the Development of Science and Technology (Colciencias)Department of Chemical Engineering, National University of Colombia at Manizales

Introduction

Objective

Rigorous Modelling

Short-cut Approach

Optimization Strategy

Conclusions

Future Work

Acknowledm.