  Variations of physical and chemical characteristics of biomass reduce
equipment utilization and increase operational costs of biomass processing.
Biomass processing facilities use sensors to monitor the changes in biomass
characteristics. Integrating sensory data into the operational decisions in
biomass processing will increase its flexibility to the changing biomass
conditions. In this paper, we propose a multi-stage stochastic programming
model that minimizes the expected operational costs by identifying the initial
inventory level and creating an operational decision policy for equipment speed
settings. These policies take the sensory information data and the current
biomass inventory level as inputs to dynamically adjust inventory levels and
equipment settings according to the changes in the biomass' characteristics. We
ensure that a prescribed target reactor utilization is consistently achieved by
penalizing the violation of the target reactor feeding rate. A case study is
developed using real-world data collected at Idaho National Laboratory's
biomass processing facility. We show the value of multi-stage stochastic
programming from an extensive computational experiment. Our sensitivity
analysis indicates that updating the infeed rate of the system, the processing
speed of equipment, and bale sequencing based on the moisture level of biomass
improves the processing rate of the reactor and reduces operating costs.
