TY - JOUR
T1 - Optimal control to handle variations in moisture content and reactor in-feed rate
AU - Kucuksayacigil, Fikri
AU - Roni, Mohammad
AU - Eksioglu, Sandra D.
AU - Bhuiyan, Tanveer H.
AU - Chen, Qiushi
N1 - Funding Information:
The funding of this work was provided by the U.S. Department of Energy , Office of Energy Efficiency and Renewable Energy , Bioenergy Technologies Office under award Number DE- EE0008255 and Department of Energy Idaho Operations Office Contract No. DE-AC07-05ID14517 . The authors greatly thank INL staff in the DOE Biomass Feedstock National User Facility for providing technical data for this study. Specifically, we acknowledge Neal A. Yancey and Jaya S. Tumuluru, who are the individual INL contributors to this research and provided helpful comments, data, and other forms of support for this analysis. The views expressed herein are those of the author only, and do not necessarily represent the views of DOE or the U.S. Government.
Funding Information:
The funding of this work was provided by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office under award Number DE- EE0008255 and Department of Energy Idaho Operations Office Contract No. DE-AC07-05ID14517. The authors greatly thank INL staff in the DOE Biomass Feedstock National User Facility for providing technical data for this study. Specifically, we acknowledge Neal A. Yancey and Jaya S. Tumuluru, who are the individual INL contributors to this research and provided helpful comments, data, and other forms of support for this analysis. The views expressed herein are those of the author only, and do not necessarily represent the views of DOE or the U.S. Government.
Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/6/1
Y1 - 2022/6/1
N2 - The variations in feedstock characteristics, such as moisture and particle size distribution, lead to an inconsistent flow of feedstock from the biomass pre-processing system to the reactor in-feed system. These inconsistencies result in low on-stream times at the reactor in-feed equipment. This research develops an optimal process control method for a biomass pre-processing system comprised of milling and densification operations to provide the consistent flow of feedstock to a reactor's throat. This method uses a mixed-integer optimization model to identify optimal bale sequencing, equipment in-feed rate, and buffer location and size in the biomass pre-processing system. This method, referred to as the hybrid process control (HPC), aims to maximize throughput over time. We compare HPC with a baseline feed forward process control. Our case study based on switchgrass finds that HPC reduces the variation of a reactor's feeding rate by up to 100% without increasing the operating cost of the biomass pre-processing system for biomass with moisture ranging from 10 to 25%. Additionally, HPC reduces the cost of processing biomass by 0.36%–2.22%, and reduces processing time by 0.35%–2.24%. A biorefinery can adapt HPC to achieve its design capacity.
AB - The variations in feedstock characteristics, such as moisture and particle size distribution, lead to an inconsistent flow of feedstock from the biomass pre-processing system to the reactor in-feed system. These inconsistencies result in low on-stream times at the reactor in-feed equipment. This research develops an optimal process control method for a biomass pre-processing system comprised of milling and densification operations to provide the consistent flow of feedstock to a reactor's throat. This method uses a mixed-integer optimization model to identify optimal bale sequencing, equipment in-feed rate, and buffer location and size in the biomass pre-processing system. This method, referred to as the hybrid process control (HPC), aims to maximize throughput over time. We compare HPC with a baseline feed forward process control. Our case study based on switchgrass finds that HPC reduces the variation of a reactor's feeding rate by up to 100% without increasing the operating cost of the biomass pre-processing system for biomass with moisture ranging from 10 to 25%. Additionally, HPC reduces the cost of processing biomass by 0.36%–2.22%, and reduces processing time by 0.35%–2.24%. A biorefinery can adapt HPC to achieve its design capacity.
KW - Biomass moisture variability
KW - Biomass pre-processing system
KW - Buffer control
KW - Optimal process control. mixed-integer linear optimization
UR - https://www.scopus.com/pages/publications/85126056965
UR - https://www.mendeley.com/catalogue/31bd05f2-d84b-3054-868c-8fdbb77c1e3b/
U2 - 10.1016/j.energy.2022.123650
DO - 10.1016/j.energy.2022.123650
M3 - Article
AN - SCOPUS:85126056965
SN - 0360-5442
VL - 248
JO - Energy
JF - Energy
M1 - 123650
ER -