  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.
