Production of ethanol fuel via syngas fermentation: Optimization of economic performance and energy efficiency

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Abstract

In this work, a model was developed to predict the performance of a bubble column reactor for syngas fermentation and the subsequent recovery of anhydrous ethanol. The model was embedded in an optimization framework which employs surrogate models (artificial neural networks) and multi-objective genetic algorithm to optimize different process conditions and design variables with objectives related to investment, minimum selling price, energy efficiency and bioreactor productivity. The results indicate the optimal trade-offs between these objectives while providing a range of solutions such that, if desired, a single solution can be picked, depending on the priority conferred to different process targets. The Pareto-optimal values of the decision variables were discussed for different case studies with and without the recovery unit. It was shown that enhancing the gas-liquid mass transfer coefficient is a key strategy toward sustainability improvement.

Original languageEnglish
Article number100056
Number of pages12
JournalChemical Engineering Science: X
Volume5
DOIs
Publication statusPublished - 2020

Keywords

  • Artificial neural networks
  • Bioethanol production
  • Modeling
  • Multi-objective optimization
  • Renewable energy
  • Syngas fermentation

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