Decarbonizing ethanol production via gas fermentation: Impact of the CO/H&lt;sub&gt;2&lt;/sub&gt;/CO&lt;sub&gt;2&lt;/sub&gt; mix source on greenhouse gas emissions and production costs

Almeida Benalcazar, E.F.; Noorman, H.J.; Maciel Filho, Rubens; Posada Duque, J.A.

doi:10.1016/j.compchemeng.2022.107670

Decarbonizing ethanol production via gas fermentation

Title

Decarbonizing ethanol production via gas fermentation: Impact of the CO/H₂/CO₂ mix source on greenhouse gas emissions and production costs

Author

Almeida Benalcazar, E.F. (TU Delft BT/Biotechnology and Society; University of Campinas)
Noorman, H.J. (TU Delft BT/Bioprocess Engineering; DSM)
Maciel Filho, Rubens (University of Campinas)
Posada Duque, J.A. (TU Delft BT/Biotechnology and Society)

Date

2022

Abstract

This study explores key success factors for ethanol production via fermentation of gas streams, by assessing the effects of eight process variables driving the fermentation performance on the production costs and greenhouse gas emissions. Three fermentation feedstocks are assessed: off-gases from the steel industry, lignocellulosic biomass-derived syngas and a mixture of H₂ and CO₂. The analysis is done through a sequence of (i) sensitivity analyses based on stochastic simulations and (ii) multi-objective optimizations. In economic terms, the use of steel off-gas leads to the best performance and the highest robustness to low mass transfer coefficients, low microbial tolerance to ethanol, acetic-acid co-production and to dilution of the gas feed with CO₂, due to the relatively high temperature at which the gas feedstock is available. The ethanol produced from the three feedstocks lead to lower greenhouse gas emissions than fossil-based gasoline and compete with first and second generation ethanol.

Subject

Bubble column
Ethanol
Gas fermentation
Process optimization
Sensitivity analysis
Syngas

To reference this document use:

http://resolver.tudelft.nl/uuid:b8505ad5-6693-4bbe-a857-f711176d2107

DOI

https://doi.org/10.1016/j.compchemeng.2022.107670

ISSN

0098-1354

Source

Computers & Chemical Engineering, 159

Part of collection

Institutional Repository