Process Synthesis for Fuel Ethanol Production from Lignocellulosic Biomass Using an Optimization-Based Strategy
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1 Process Synthesis for Fuel Ethanol Production from Lignocellulosic Biomass Using an Optimization-Based Strategy Óscar J Sánchez 1,2 Eric S Fraga 2 Carlos A Cardona 3 1 Department of Engineering, Universidad de Caldas 2 Department of Chemical Engineering, University College London 3 Department of Chemical Engineering, Universidad Nacional de Colombia, Manizales World Renewable Energy Congress 2006 OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug 22 1 / 17
2 Outline Introduction 1 Introduction 2 Case study 3 Summary OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug 22 2 / 17
3 Introduction Motivation Fuel ethanol demand is on the increase, for reasons this audience is well aware of! Cost-effective process technologies with less expensive feedstocks, such as lignocellulosic biomass, are required. Evaluating alternative designs experimentally is difficult and expensive. Automated tools based on optimisation and simulation can help identify the most cost-effective process alternatives. OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug 22 3 / 17
4 Introduction Lignocellulosic biomass An abundant and cheap feedstock suitable for energy production. Mainly agricultural and forestry residues and agro-industrial wastes. Can be converted to liquid biofuels such as ethanol which can be used directly or as an oxygenate for gasoline. The conversion of lignocellulosic biomass is a complex process: Cellulose and hemicellulose must be transformed into fermentable sugars. Post-fermentation steps include concentration and de-hydration. OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug 22 4 / 17
5 Introduction Automated process design Knowledge based Make use of heuristic rules. Are based on the experience of researchers and engineers. Provide qualitative ranking of design alternatives. Optimisation based Based on a superstructure of design alternatives. Modelled using mixed integer nonlinear programming (MINLP). Provides quantitative ranking. OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug 22 5 / 17
6 Introduction Jacaranda Object oriented framework for process design and optimisation [Fra06]. Extensible and adaptable for a wide range of problems. Can simultaneously solve reaction and separation sections. Able to handle complex models (e.g. physical property estimation methods). Supports both deterministic and stochastic optimisation procedures. Supports multi-criteria optimisation. OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug 22 6 / 17
7 Outline Case study 1 Introduction 2 Case study 3 Summary OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug 22 7 / 17
8 Case study Objective Design and optimise process for the production of ethanol from lignocellulosic biomass. Consider alternative transformation routes. Analyse impact of these alternatives on the separation section. Rank alternatives based on economic criteria. OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug 22 8 / 17
9 Case study Alternative transformation routes Cellulose hydrolysis (CH): (Cellulose) n + n 2 H 2O n 2 Cellobiose (Cellulose) n + n H 2 O n Glucose Cellobiose + 2 H 2 O 2 Glucose Hexose fermentation (HF): Glucose C 2 H 5 OH + 2 CO 2 Glucose NH 3 6 S. cerevisiae H 2 O O 2 OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug 22 9 / 17
10 Case study Alternative transformation routes Simultaneous saccharification and fermentation (SSF): (Cellulose) n + n 2 H 2O n 2 Cellobiose (Cellulose) n + n H 2 O n Glucose Cellobiose + 2 H 2 O 2 Glucose Glucose C 2 H 5 OH + 2 CO 2 Glucose NH 3 6 S. cerevisiae H 2 O O 2 OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug 22 9 / 17
11 Case study Alternative transformation routes Simultaneous saccharification and cofermentation (SSCF): (Cellulose) n + n 2 H 2O n 2 Cellobiose (Cellulose) n + n H 2 O n Glucose Cellobiose + 2 H 2 O 2 Glucose Glucose C 2 H 5 OH + 2 CO 2 Glucose NH 3 6 Z. mobilis H 2 O O 2 3 Xylose 5 C 2 H 5 OH + 5 CO 2 Xylose + NH 3 5 Z. mobilis + 2 H 2 O O 2 OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug 22 9 / 17
12 Process superstructure Case study OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
13 Case study Process superstructure Biological transformations simultaneous saccharification and co-fermentation simultaneous saccharification and fermentation OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process separate design hydrolysis WREC IX 2006 and Aug / 17
14 Case study Process superstructure Feed SSCF SSF SHF Biological transformations simultaneous saccharification and co-fermentation simultaneous saccharification and fermentation OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process separate design hydrolysis WREC IX 2006 and Aug / 17
15 Case study Process superstructure Separation and purification Consider distillation alone but this could be relaxed. Must handle non-ideal mixture behaviour. OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
16 Process superstructure Case study Solvent Ethanol Water Separation and purification Consider distillation alone but this could be Solvent relaxed. Must handle non-ideal mixture behaviour. Waste OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
17 Process superstructure Case study Solvent Ethanol Water Feed SSCF SSF Solvent SHF Waste OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
18 Models Case study Solvent Ethanol Water Feed Feed SSCF The SSF feed to system is the lignocellulosic stream after pre-treatment using dilute Solvent acid. Contains SHF primarily cellulose, pentoses (mainly xylose), glucose, lignin, Wasteand water. OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
19 Case study Models Solvent Ethanol Feed SSCF SSF Products Water The desired final product stream is ethanol at greater than 99.5 wt%. The waste treatment step was not considered in this study. Solvent SHF Waste OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
20 Models Reactor Models Case study Rate based for system of differential equations for each reactor, e.g. [SHL95]: Solvent Ethanol r S = {k (1 x) n + c} ES [ ][ ] k S/C k S/P C Water s C + k S/C P + k S/P Feed Each system SSCF solved using lsode within Octave invoked by Jacaranda. SSF Solvent SHF Waste OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
21 Models Feed SSCF Case study Distillation Models Designs generated using Fenske, Underwood & Solvent Gilliland short-cut Ethanol methodology. Physical properties Water estimated with NRTL activity coefficient model plus ideal gas EOS. SSF Solvent SHF Waste OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
22 Case study Computational aspects Design variables: two binary variables for feed splitting, residence time for each reactor, and recovery of light and heavy keys for each column MINLP with total of 2 binary and = 10 continuous real valued variables. The nonlinear problem is solved using a genetic algorithm (population replacement policy, elite size of 1, mutation rate of 10%, crossover rate of 70% and roulette wheel selection). Jacaranda will calculate the make-up of ethylene glycol required and the solvent recycle stream flow rate. OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
23 Case study Results SSCF configuration best performing Integration gives immediate consumption of glucose formed, avoiding inhibition of cellulose-degrading enzymes (cellulases). The utilization of xylose allows an increase in the content of fermentation sugars increase in ethanol. Enhanced utilisation of the feed-stock is not a characteristic of the SSF process. The SHF requires two bioreactors, increasing capital cost in comparison. OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
24 Outline Summary 1 Introduction 2 Case study 3 Summary OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
25 Summary Summary Results demonstrate that the genetic algorithm used by Jacaranda handles the complexity of the problem design robustly. The solutions obtained show variability in the technological option. From 10 different runs: three of the solutions corresponded to SSCF configurations (two of them with the best values of the objective function), six solutions to the SSF process, and one solution to the SHF configuration. Next steps are to use more rigorous models for distillation for non-ideal behaviour and to include yet more transformation steps. OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
26 Summary Acknowledgements O. Sánchez gratefully acknowledges the provision of computational and office resources by UCL during his visit. The authors also acknowledge the financial support provided by the Colombian Institute for Development of Science and Technology (Colciencias) and by the National University of Colombia at Manizales. OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
27 Summary References Eric S. Fraga. The Jacaranda framework for process design and optimisation C. R. South, D. A. L. Hogsett, and L. R. Lynd. Modeling simultaneous saccharification and fermentation of lignocellulose to ethanol in batch and continuous reactors. Enzyme and Microbial Technology, 17: , OJS, ESF & CAC (UdC, UCL & UNAL) Optimisation for bioethanol process design WREC IX 2006 Aug / 17
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