Variations of physical and chemical characteristics of biomass lead to an uneven flow of biomass in a biorefinery, which reduces equipment utilization and increases operational costs. Uncertainty of biomass supply and high processing costs increase the risk of investing in the US's cellulosic biofuel industry. We propose a stochastic programming model to streamline processes within a biorefinery. A chance constraint models system's reliability requirement that the reactor is operating at a high utilization rate given uncertain biomass moisture content, particle size distribution, and equipment failure. The model identifies operating conditions of equipment and inventory level to maintain a continuous flow of biomass to the reactor. The Sample Average Approximation method approximates the chance constraint and a bisection search-based heuristic solves this approximation. A case study is developed using real-life data collected at Idaho National Laboratory's pilot biomass processing facility. An extensive computational analysis indicates that sequencing of biomass bales based on moisture level, increasing storage capacity, and managing particle size distribution increase utilization of the reactor and reduce operational costs.
A Joint Chance-Constrained Stochastic Programming Approach for the Integrated Predictive Maintenance and Operations Scheduling Problem in Power Systems
An Integrated Optimization Framework for Smart Charging of Electric Bus Fleets under Dynamic Electricity Prices with On-Site Solar Generation, Energy Storage, and V2G operations
KU Leuven University（荷语鲁汶大学）；Luxembourg Institute of Socio-Economic Research (LISER)（卢森堡社会经济研究所）
A Stochastic Programming Model for Anticipative Planning of Integrated Electricity and Gas Systems with Bidirectional Energy Flows under Fuel and CO2 Price Uncertainty
University of Twente（特文特大学）；University of Exeter（埃克塞特大学）；Maastricht University（马斯特里赫特大学）；Weihenstephan-Triesdorf University of Applied Sciences（魏恩施蒂芬-特里斯多夫应用科学大学）；Technical University of Munich（慕尼黑工业大学）
