A multi-stage stochastic programming model for adaptive biomass processing operation under uncertainty

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 bi...

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Bibliographic Details
Published in:Energy systems (Berlin. Periodical) 2024-08, Vol.15 (3), p.1197-1232
Main Authors: Gulcan, Berkay, Song, Yongjia, Eksioglu, Sandra D., Roni, Mohammad
Format: Article
Language:English
Subjects:
Biomass
Economics and Management
Energy
Energy Policy
Energy Systems
Feeding rates
Flexibility
Inventory
Operating costs
Operations Research/Decision Theory
Optimization
Original Paper
Reactors
Sensitivity analysis
Sequences
Stochastic models
Stochastic programming
Uncertainty
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Summary: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 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.
ISSN:1868-3967
1868-3975
DOI:10.1007/s12667-022-00544-1