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Dynamic modeling of syngas fermentation in a continuous stirred‐tank reactor: Multi‐response parameter estimation and process optimization

Syngas fermentation is one of the bets for the future sustainable biobased economies due to its potential as an intermediate step in the conversion of waste carbon to ethanol fuel and other chemicals. Integrated with gasification and suitable downstream processing, it may constitute an efficient and...

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Autores principales: de Medeiros, Elisa M., Posada, John A., Noorman, Henk, Filho, Rubens Maciel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328424/
https://www.ncbi.nlm.nih.gov/pubmed/31286472
http://dx.doi.org/10.1002/bit.27108
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author de Medeiros, Elisa M.
Posada, John A.
Noorman, Henk
Filho, Rubens Maciel
author_facet de Medeiros, Elisa M.
Posada, John A.
Noorman, Henk
Filho, Rubens Maciel
author_sort de Medeiros, Elisa M.
collection PubMed
description Syngas fermentation is one of the bets for the future sustainable biobased economies due to its potential as an intermediate step in the conversion of waste carbon to ethanol fuel and other chemicals. Integrated with gasification and suitable downstream processing, it may constitute an efficient and competitive route for the valorization of various waste materials, especially if systems engineering principles are employed targeting process optimization. In this study, a dynamic multi‐response model is presented for syngas fermentation with acetogenic bacteria in a continuous stirred‐tank reactor, accounting for gas–liquid mass transfer, substrate (CO, H(2)) uptake, biomass growth and death, acetic acid reassimilation, and product selectivity. The unknown parameters were estimated from literature data using the maximum likelihood principle with a multi‐response nonlinear modeling framework and metaheuristic optimization, and model adequacy was verified with statistical analysis via generation of confidence intervals as well as parameter significance tests. The model was then used to study the effects of process conditions (gas composition, dilution rate, gas flow rates, and cell recycle) as well as the sensitivity of kinetic parameters, and multiobjective genetic algorithm was used to maximize ethanol productivity and CO conversion. It was observed that these two objectives were clearly conflicting when CO‐rich gas was used, but increasing the content of H(2) favored higher productivities while maintaining 100% CO conversion. The maximum productivity predicted with full conversion was 2 g·L(−1)·hr(−1) with a feed gas composition of 54% CO and 46% H(2) and a dilution rate of 0.06 hr(−1) with roughly 90% of cell recycle.
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spelling pubmed-93284242022-07-30 Dynamic modeling of syngas fermentation in a continuous stirred‐tank reactor: Multi‐response parameter estimation and process optimization de Medeiros, Elisa M. Posada, John A. Noorman, Henk Filho, Rubens Maciel Biotechnol Bioeng ARTICLES Syngas fermentation is one of the bets for the future sustainable biobased economies due to its potential as an intermediate step in the conversion of waste carbon to ethanol fuel and other chemicals. Integrated with gasification and suitable downstream processing, it may constitute an efficient and competitive route for the valorization of various waste materials, especially if systems engineering principles are employed targeting process optimization. In this study, a dynamic multi‐response model is presented for syngas fermentation with acetogenic bacteria in a continuous stirred‐tank reactor, accounting for gas–liquid mass transfer, substrate (CO, H(2)) uptake, biomass growth and death, acetic acid reassimilation, and product selectivity. The unknown parameters were estimated from literature data using the maximum likelihood principle with a multi‐response nonlinear modeling framework and metaheuristic optimization, and model adequacy was verified with statistical analysis via generation of confidence intervals as well as parameter significance tests. The model was then used to study the effects of process conditions (gas composition, dilution rate, gas flow rates, and cell recycle) as well as the sensitivity of kinetic parameters, and multiobjective genetic algorithm was used to maximize ethanol productivity and CO conversion. It was observed that these two objectives were clearly conflicting when CO‐rich gas was used, but increasing the content of H(2) favored higher productivities while maintaining 100% CO conversion. The maximum productivity predicted with full conversion was 2 g·L(−1)·hr(−1) with a feed gas composition of 54% CO and 46% H(2) and a dilution rate of 0.06 hr(−1) with roughly 90% of cell recycle. John Wiley and Sons Inc. 2019-07-24 2019-10 /pmc/articles/PMC9328424/ /pubmed/31286472 http://dx.doi.org/10.1002/bit.27108 Text en © 2019 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle ARTICLES
de Medeiros, Elisa M.
Posada, John A.
Noorman, Henk
Filho, Rubens Maciel
Dynamic modeling of syngas fermentation in a continuous stirred‐tank reactor: Multi‐response parameter estimation and process optimization
title Dynamic modeling of syngas fermentation in a continuous stirred‐tank reactor: Multi‐response parameter estimation and process optimization
title_full Dynamic modeling of syngas fermentation in a continuous stirred‐tank reactor: Multi‐response parameter estimation and process optimization
title_fullStr Dynamic modeling of syngas fermentation in a continuous stirred‐tank reactor: Multi‐response parameter estimation and process optimization
title_full_unstemmed Dynamic modeling of syngas fermentation in a continuous stirred‐tank reactor: Multi‐response parameter estimation and process optimization
title_short Dynamic modeling of syngas fermentation in a continuous stirred‐tank reactor: Multi‐response parameter estimation and process optimization
title_sort dynamic modeling of syngas fermentation in a continuous stirred‐tank reactor: multi‐response parameter estimation and process optimization
topic ARTICLES
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9328424/
https://www.ncbi.nlm.nih.gov/pubmed/31286472
http://dx.doi.org/10.1002/bit.27108
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