TY - JOUR
T1 - Benchmarking real-time monitoring strategies for ethanol production from lignocellulosic biomass
AU - Cabaneros Lopez, Pau
AU - Feldman, Hannah
AU - Mauricio-Iglesias, Miguel
AU - Junicke, Helena
AU - Huusom, Jakob Kjøbsted
AU - Gernaey, Krist V.
PY - 2019
Y1 - 2019
N2 - The goal of this paper is to review and critically assess different methods to monitor key process variables for ethanol production from lignocellulosic biomass. Because cellulose-based biofuels cannot yet compete with non-cellulosic biofuels, process control and optimization are of importance to lower the production costs. This study reviews different monitoring schemes, to indicate what the added value of real-time monitoring is for process control. Furthermore, a comparison is made on different monitoring techniques to measure the off-gas, the concentrations of dissolved components in the inlet to the process, the concentrations of dissolved components in the reactor, and the biomass concentration. Finally, soft sensor techniques and available models are discussed, to give an overview of modeling techniques that analyze data, with the aim of coupling the soft sensor predictions to the control and optimization of cellulose to ethanol fermentation. The paper ends with a discussion of future needs and developments.
AB - The goal of this paper is to review and critically assess different methods to monitor key process variables for ethanol production from lignocellulosic biomass. Because cellulose-based biofuels cannot yet compete with non-cellulosic biofuels, process control and optimization are of importance to lower the production costs. This study reviews different monitoring schemes, to indicate what the added value of real-time monitoring is for process control. Furthermore, a comparison is made on different monitoring techniques to measure the off-gas, the concentrations of dissolved components in the inlet to the process, the concentrations of dissolved components in the reactor, and the biomass concentration. Finally, soft sensor techniques and available models are discussed, to give an overview of modeling techniques that analyze data, with the aim of coupling the soft sensor predictions to the control and optimization of cellulose to ethanol fermentation. The paper ends with a discussion of future needs and developments.
KW - Cellulosic ethanol
KW - Fermentation
KW - Models
KW - Monitoring devices
KW - Real-time monitoring
KW - Soft sensors
U2 - 10.1016/j.biombioe.2019.105296
DO - 10.1016/j.biombioe.2019.105296
M3 - Review
AN - SCOPUS:85068925791
SN - 0961-9534
VL - 127
JO - Biomass and Bioenergy
JF - Biomass and Bioenergy
M1 - 105296
ER -