Optimal control to handle variations in moisture content and reactor in-feed rate

Fikri Kucuksayacigil, Mohammad Roni, Sandra D. Eksioglu, Tanveer H. Bhuiyan and Qiushi Chen

Energy, 2022, vol. 248, issue C

Abstract: 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.

Keywords: Biomass pre-processing system; Biomass moisture variability; Optimal process control. mixed-integer linear optimization; Buffer control (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:248:y:2022:i:c:s0360544222005539

DOI: 10.1016/j.energy.2022.123650

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