Assessing the Sensitivity of Technical Performance of three Ethanol Production Processes based on the Fermentation of Steel Manufacturing Offgas, Syngas and a 3:1 Mixture Between H2 and CO2

Book Chapter (2020)
Author(s)

E.F. Almeida Benalcazar (University of Campinas, TU Delft - BT/Biotechnology and Society)

HJ Noorman (TU Delft - BT/Bioprocess Engineering, DSM)

Rubens Maciel Maciel Filho (University of Campinas)

J. Posada Duque (TU Delft - BT/Biotechnology and Society)

Research Group
BT/Biotechnology and Society
DOI related publication
https://doi.org/10.1016/B978-0-12-823377-1.50099-9
More Info
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Publication Year
2020
Language
English
Research Group
BT/Biotechnology and Society
Pages (from-to)
589-594

Abstract

This study assesses the sensitivity of the technical, environmental and economic performance of three ethanol production process based on the fermentation of three gas mixtures: i) CO-rich flue gas from steel manufacturing, ii) biomass-based syngas with a H2/CO ratio of 2 and iii) a 3:1 combination between H2 and CO2. The sensitivity analysis is based on stochastic bioreactor simulations constructed by randomly generated combinations of eight parameters that command the fermentation process i.e., temperature, pressure, gas feed dilution with an inert components, ethanol concentration, height of the liquid column, mass transfer coefficients, superficial gas velocity and, acetic acid co-production. The sensitivity analysis identified that the bioreactor technical performance is highly sensitive to variations on pressure, liquid column height and the mass transfer coefficients. The pressure mainly improves mass transfer and consequently ethanol productivity whereas liquid column height improves the gas residence time and consequently the efficiency in the gas utilization. The trend was common for the three gas supply options. The results suggested that in order to produce an optimal bioreactor design, there are options to optimize the productivity and the gas utilization simultaneously. The results from the sensitivity analysis may help guiding a subsequent multi-objective process optimization study.

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