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Multi-objective optimization of simultaneous saccharification and fermentation for cellulosic ethanol production

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  • Shadbahr, Jalil
  • Zhang, Yan
  • Khan, Faisal
  • Hawboldt, Kelly

Abstract

A multi-objective optimization of simultaneous saccharification and fermentation process for cellulosic ethanol production was carried out to simultaneously maximize the ethanol yield/cellulose conversion and minimize the enzyme consumption by manipulating the initial sugar concentrations, and cellulose and enzyme loadings. The study was based on an experimentally verified kinetic model. Several bi-objective optimization problems with different combinations of objectives and constraints were solved by a controlled elitist genetic algorithm, a variant of the non-dominated sorting genetic algorithm II (NSGA-II). The optimum operating conditions were verified by experiments. There was significant performance improvement in terms of ethanol yield, cellulose conversion and enzyme loading. An overall 40% reduction of enzyme consumption per ethanol produced was attained at the same ethanol yield (32%) of a non-optimized process. However, the optimum conditions are highly sensitive to the selected kinetic model and associated kinetic parameters therefore, selection of the appropriate kinetic model is critical.

Suggested Citation

  • Shadbahr, Jalil & Zhang, Yan & Khan, Faisal & Hawboldt, Kelly, 2018. "Multi-objective optimization of simultaneous saccharification and fermentation for cellulosic ethanol production," Renewable Energy, Elsevier, vol. 125(C), pages 100-107.
    • Handle: RePEc:eee:renene:v:125:y:2018:i:c:p:100-107
    DOI: 10.1016/j.renene.2018.02.106
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    References listed on IDEAS

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    1. David Zilberman & Gal Hochman & Deepak Rajagopal & Steve Sexton & Govinda Timilsina, 2013. "The Impact of Biofuels on Commodity Food Prices: Assessment of Findings," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(2), pages 275-281.
    2. Jack P. C. Kleijnen, 2015. "Response Surface Methodology," International Series in Operations Research & Management Science, in: Michael C Fu (ed.), Handbook of Simulation Optimization, edition 127, chapter 0, pages 81-104, Springer.
    3. Betiku, Eriola & Taiwo, Abiola Ezekiel, 2015. "Modeling and optimization of bioethanol production from breadfruit starch hydrolyzate vis-à-vis response surface methodology and artificial neural network," Renewable Energy, Elsevier, vol. 74(C), pages 87-94.
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    Cited by:

    1. He, Ke & Zhang, Junbiao & Zeng, Yangmei, 2018. "Rural households' willingness to accept compensation for energy utilization of crop straw in China," Energy, Elsevier, vol. 165(PA), pages 562-571.

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