A generalized disjunctive programming framework for the optimal synthesis and analysis of processes for ethanol production from corn stover.

The aim of this study is to analyze the techno-economic performance of process configurations for ethanol production involving solid-liquid separators and reactors in the saccharification and fermentation stage, a family of process configurations where few alternatives have been proposed. Since including these process alternatives creates a large number of possible process configurations, a framework for process synthesis and optimization is proposed. This approach is supported on kinetic models fed with experimental data and a plant-wide techno-economic model. Among 150 process configurations, 40 show an improved MESP compared to a well-documented base case (BC), almost all include solid separators and some show energy retrieved in products 32% higher compared to the BC. Moreover, 16 of them also show a lower capital investment per unit of ethanol produced per year. Several of the process configurations found in this work have not been reported in the literature.

[1]  Germán Aroca,et al.  Attainable region analysis for continuous production of second generation bioethanol , 2013, Biotechnology for Biofuels.

[2]  Aidong Yang,et al.  A decision support environment for the high-throughput model-based screening and integration of biomass processing paths , 2015 .

[3]  M. Galbe,et al.  Pretreatment of lignocellulosic materials for efficient bioethanol production. , 2007, Advances in biochemical engineering/biotechnology.

[4]  P. Rogers,et al.  Mathematical modelling of ethanol production from glucose/xylose mixtures by recombinant Zymomonas mobilis , 2001, Biotechnology Letters.

[5]  John N. Saddler,et al.  A comparison between simultaneous saccharification and fermentation and separate hydrolysis and fermentation using steam-pretreated corn stover , 2007 .

[6]  Ling Tao,et al.  Performance and techno-economic assessment of several solid-liquid separation technologies for processing dilute-acid pretreated corn stover. , 2014, Bioresource technology.

[7]  Bruce E. Dale,et al.  Take a closer look: biofuels can support environmental, economic and social goals. , 2014, Environmental science & technology.

[8]  M. Studer,et al.  Consolidated bioprocessing of lignocellulose by a microbial consortium , 2014 .

[9]  Ingwald Obernberger,et al.  Wood pellet production costs under Austrian and in comparison to Swedish framework conditions , 2004 .

[10]  I. Grossmann,et al.  Logic-based MINLP algorithms for the optimal synthesis of process networks , 1996 .

[11]  Min Zhang,et al.  Improved ethanol yield and reduced minimum ethanol selling price (MESP) by modifying low severity dilute acid pretreatment with deacetylation and mechanical refining: 2) Techno-economic analysis , 2012, Biotechnology for Biofuels.

[12]  F. You,et al.  Optimal design of sustainable cellulosic biofuel supply chains: Multiobjective optimization coupled with life cycle assessment and input–output analysis , 2012 .

[13]  Mariano Martín,et al.  Methodology for solar and wind energy chemical storage facilities design under uncertainty: Methanol production from CO2 and hydrogen , 2016, Comput. Chem. Eng..

[14]  A. Aden,et al.  An economic comparison of different fermentation configurations to convert corn stover to ethanol using Z. mobilis and Saccharomyces , 2009, Biotechnology progress.

[15]  D. Klingenberg,et al.  Rheology measurements of a biomass slurry: an inter-laboratory study , 2009 .

[16]  Ling Tao,et al.  Techno-economic analysis of the deacetylation and disk refining process: characterizing the effect of refining energy and enzyme usage on minimum sugar selling price and minimum ethanol selling price , 2015, Biotechnology for Biofuels.

[17]  Daniel J Schell,et al.  Soluble and insoluble solids contributions to high-solids enzymatic hydrolysis of lignocellulose. , 2008, Bioresource technology.

[18]  N. Qureshi,et al.  Comparison of separate hydrolysis and fermentation and simultaneous saccharification and fermentation processes for ethanol production from wheat straw by recombinant Escherichia coli strain FBR5 , 2011, Applied Microbiology and Biotechnology.

[19]  P. Väljamäe,et al.  Product inhibition of cellulases studied with 14C-labeled cellulose substrates , 2013, Biotechnology for Biofuels.

[20]  Arne Drud,et al.  CONOPT: A GRG code for large sparse dynamic nonlinear optimization problems , 1985, Math. Program..

[21]  Ryan Davis,et al.  Process Design and Economics for Biochemical Conversion of Lignocellulosic Biomass to Ethanol: Dilute-Acid Pretreatment and Enzymatic Hydrolysis of Corn Stover , 2011 .

[22]  Felipe Scott,et al.  Corn stover semi-mechanistic enzymatic hydrolysis model with tight parameter confidence intervals for model-based process design and optimization. , 2015, Bioresource technology.

[23]  Wyatt Thompson,et al.  Interactions between California's Low Carbon Fuel Standard and the National Renewable Fuel Standard , 2017 .

[24]  Onur Onel,et al.  Biomass to Liquid Transportation Fuels via Biological and Thermochemical Conversion: Process Synthesis and Global Optimization Strategies , 2016 .

[25]  Mary Biddy,et al.  A low-cost solid-liquid separation process for enzymatically hydrolyzed corn stover slurries. , 2015, Bioresource technology.

[26]  A. Osmani,et al.  Economic and environmental optimization of a large scale sustainable dual feedstock lignocellulosic-based bioethanol supply chain in a stochastic environment. , 2014 .

[27]  Nikolaos V. Sahinidis,et al.  BARON: A general purpose global optimization software package , 1996, J. Glob. Optim..

[28]  Venkatesh Balan,et al.  Continuous SSCF of AFEX™ pretreated corn stover for enhanced ethanol productivity using commercial enzymes and Saccharomyces cerevisiae 424A (LNH‐ST) , 2013, Biotechnology and bioengineering.

[29]  Sujit Banerjee,et al.  Enhancement of cellulase catalysis of wood pulp fiber by cationic polyelectrolytes , 2011 .

[30]  Rubén Ruiz-Femenia,et al.  Integration of modular process simulators under the Generalized Disjunctive Programming framework for the structural flowsheet optimization , 2014, Comput. Chem. Eng..

[31]  J. R. Hess,et al.  Process Design and Economics for Conversion of Lignocellulosic Biomass to Ethanol , 2011 .

[32]  Juan P. Ruiz,et al.  Generalized Disjunctive Programming: A Framework for Formulation and Alternative Algorithms for MINLP Optimization , 2012 .

[33]  Christodoulos A. Floudas,et al.  Biomass to liquid transportation fuels (BTL) systems: process synthesis and global optimization framework , 2013 .

[34]  R. Raman,et al.  RELATION BETWEEN MILP MODELLING AND LOGICAL INFERENCE FOR CHEMICAL PROCESS SYNTHESIS , 1991 .

[35]  Jonathan Currie,et al.  Opti: Lowering the Barrier Between Open Source Optimizers and the Industrial MATLAB User , 2012 .

[36]  Mahmoud M. El-Halwagi,et al.  Modeling and optimization of a bioethanol production facility , 2013, Clean Technologies and Environmental Policy.

[37]  Matthew W Liberatore,et al.  Particle concentration and yield stress of biomass slurries during enzymatic hydrolysis at high‐solids loadings , 2009, Biotechnology and bioengineering.

[38]  Elmer Ccopa Rivera,et al.  Evaluation of optimization techniques for parameter estimation: Application to ethanol fermentation considering the effect of temperature , 2006 .

[39]  N. Shah,et al.  A comprehensive approach to the design of ethanol supply chains including carbon trading effects. , 2012, Bioresource technology.

[40]  Bruce E. Dale A New Industry Has Been Launched: The Cellulosic Biofuels Ship (Finally) Sails , 2015 .

[41]  Steven R. Thomas,et al.  Process and technoeconomic analysis of leading pretreatment technologies for lignocellulosic ethanol production using switchgrass. , 2011, Bioresource technology.

[42]  Lorenz T. Biegler,et al.  On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming , 2006, Math. Program..