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.