Optimization Models for Integrated Biorefinery Operations

TL;DR

This paper develops a stochastic programming model to optimize biorefinery operations under biomass variability, improving equipment utilization and reducing costs through strategic inventory and process management.

Contribution

It introduces a novel stochastic programming approach with chance constraints for biorefinery process optimization considering biomass uncertainties.

Findings

Sequencing biomass bales by moisture level improves reactor utilization.

Increasing storage capacity reduces operational costs.

Managing particle size distribution enhances process efficiency.

Abstract

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.