Modeling and Multi-Objective Optimization of Syngas Fermentation in a Bubble Column Reactor

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientificpeer-review

Abstract

Ethanol may be produced from waste materials via a thermochemical-biochemical route employing gasification and syngas fermentation by acetogenic bacteria. This process is considered promising, but commercialization might be hindered by sub-optimal choices of design and operating conditions. In the present work, process systems engineering (PSE) techniques were applied for the optimization of a large-scale syngas fermentation bioreactor. Starting with the development of a dynamic model for a bubble column reactor with gas recycle, the multiple system outputs were studied with Principal Component Analysis to assist in the defmition of relevant objective functions, and artificial neural networks were used to approximate the steady-state responses with fast and accurate functions. This framework was then used to conduct a multi-objective optimization aiming at maximizing the ethanol production rate, lower heating value efficiency and ethanol titer, while also minimizing acetic acid titer and reactor volume.

Original languageEnglish
Title of host publicationComputer Aided Chemical Engineering
EditorsAnton A. Kiss, Edwin Zondervan
PublisherElsevier
Pages1531-1536
Volume46
ISBN (Electronic)978-0-12-818634-350256-3
ISBN (Print)978-0-12-819940-4
DOIs
Publication statusPublished - 2019
EventESCAPE-29 : 29th European Symposium on Computer Aided Chemical Engineering - Eindhoven, Netherlands
Duration: 16 Jun 201919 Jun 2019

Publication series

NameComputer Aided Chemical Engineering
Volume46
ISSN (Print)1570-7946

Conference

ConferenceESCAPE-29 : 29th European Symposium on Computer Aided Chemical Engineering
CountryNetherlands
CityEindhoven
Period16/06/1919/06/19

Keywords

  • bioethanol
  • multi-objective optimization
  • neural networks
  • principal component analysis
  • syngas fermentation

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