Oscar J. Sánchez, M.Sc. Luis F. Gutiérrez, M.Sc. Carlos A. Cardona, Ph.D. Eric S. Fraga, Prof., Ph.D. - PDF

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Computer Aided Process Engineering Group University College London ANALYSIS OF EXTRACTIVE FERMENTATION PROCESS FOR ETHANOL PRODUCTION USING A RIGOROUS MODEL AND A SHORT-CUT METHOD Oscar J. Sánchez, M.Sc.

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Computer Aided Process Engineering Group University College London ANALYSIS OF EXTRACTIVE FERMENTATION PROCESS FOR ETHANOL PRODUCTION USING A RIGOROUS MODEL AND A SHORT-CUT METHOD Oscar J. Sánchez, M.Sc. Department of Chemical Engineering, University College London Department 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 INTRODUCTION WHY FUEL ETHANOL? Progressive exhaustion of world energetic resources based on non-renewable oil fuels Dark panorama in the oil market Generation of huge amounts of pollution gases released into the atmosphere Ethanol can be utilized directly as fuel or as an oxygenate of gasoline for elevating its oxygen content BIOETHANOL PRODUCTION Catalytic synthesis From bioenergy crops Sugar cane (juice or molasses) Starch from grains (corn, wheat) From biomass Agriculture residues (grass) Forestry wastes (wood chips, sawdust) Industrial wastes Food processing wastes Municipal solid waste FUEL ETHANOL PRODUCTION FROM LIGNOCELLULOSIC BIOMASS Not implemented yet at industrial scale Higher 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 PROCESS INTEGRATION AS A TOOL FOR PROCESS DESIGN Process intensification by: Reaction-reaction integration Reaction-separation integration Separation-separation integration Continuous Fermentation F F S 10, S 10 S 1, S 1, X, P FX + V r X = 0 F( S S1) VrS 1 10 = F( S S2) VrS 2 20 = D = F / V 0 0 Most industrial fermentations are carried out in batch regime Continuous fermentation offers higher productivities Implemented processes: Ethanol production Single-cell protein production Inhibition of growth and product formation rates by: Product Substrate Simultaneous Extractive Fermentation Removal of the compound causing the inhibition through an extractive biocompatible agent (solvent) Solvent favours the migration of ethanol to solvent phase Proposed solvents for alcoholic fermentation: n-dodecanol oleyl alcohol oleyl alcohol + 4-heptanone Ways of improving: Appropriate solvent selection Analysis and optimal design prio experimentation SCOPE AND OBJECTIVE OF THE RESEARCH Model the extractive fermentation process for ethanol production from lignocellulosic biomass utilizing a rigorous mathematical description Propose a short-cut approach for analyzing this process Formulate an overall strategy of optimization odelling of Continuous xtractive Fermentation F E E 0 Q E E, P* F A S1, S2, QA Q A X + V A r X = 0 S 10, S 20 X, P F A S 10 QAS1 VArS 1 = 0 r S r 1, S 2 F F F A E A S P 20 QAS2 VArS 2 = 0 * * 0 QAP QE P + VArP = + FE QA QE = 0 0 r [ = α r + ( 1 α ) r ]X X X, 1 X, 2 r [ = α r + ( 1 α ) r ]X P P, 1 P, 2 Taken from Leksawadi et al., 200 P * = k EtOH P Coupled algorithm for the calculation of extractive fermentation process Overall algorithm Liquid-liquid equilibrium algorithm Fermentation profiles in dependence on aqueous dilution rate X, P, P* [g.l -1 ] D Ai [h -1 ] X P P* S1 S2 (a) S1, S2 [g.l -1 ] Productivity [g.l -1.h -1 ] D Ai [h -1 ] PrA PrE PrT Effect of inlet aqueous dilution rate (D Ai ) on: (a) effluent concentrations of glucose (S 1 ), xylose (S 2 ), 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) (b) S 10 = 100 g.l -1 ; S 20 = 50 g.l -1 GAMS Optimal D Ai = h -1 Effect of R = F E / F A Effect of solvent feed flow rate/aqueous feed flow rate ratio (R) on performance of continuous extractive fermentation using n-dodecanol at D ai = h-1, S 10 = 100 g.l -1 ; S 20 = 50 g.l -1. Total ethanol productivity (PrT), and productivity for ethanol recovered from solvent phase (PrE). 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. S 10 = 100 g.l -1 ; S 20 = 50 g.l -1 Influence of D Ai, R, and initial concentration of sugars SHORT-CUT APPROACH Stoichiometric relationships were considered: Thermodynamic-topological approach: Representation of extractive fermentation in a ternary diagram Representation when initial concentration of sugars changes Zone of feasible steady-states OPTIMIZATION STRATEGY OF EXTRACTIVE FERMENTATION From short-cut approach is determined the zone of feasible operating conditions: R, S 10, S 20, D ai Liquid-liquid model was simplified assuming k EtOH to linearly dependent of sugar concentration Simplified LLE model and mass balances were introduced into GAMS code CONCLUSIONS Removal of valuable products from culture broths is a promising technology for the intensification of fermentation processes 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 transformations proposed short-cut method based on the principles of thermodynamic-topological analysis allows getting a preliminary idea for approaching to the rigorous simulation Presented 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 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 British Council Department of Chemical Engineering, University College London Colombian Institute for the Development of Science and Technology (Colciencias) Department of Chemical Engineering, National University of Colombia at Manizales Acknowledm.
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