From Feedstock Variability to Biorefinery Performance: A Review of Modeling and Optimization Approaches for Biomass-to-Bioenergy Supply Chains

Abstract

The industrial scalability and economic competitiveness of biomass-to-bioenergy and biorefinery systems depend on reliable feedstock supply, consistent biomass quality, and efficient logistics. An aspect that remains underexplored in biomass-to-biorefinery supply chain optimization is the incorporation of biomass quality uncertainty into decision-making models. Biomass quality characteristics, including ash content, moisture, chemical composition, and dry matter loss, can influence storage, preprocessing, transportation, conversion efficiency, biorefinery yields, process reliability, and overall energy utilization. Although these characteristics are difficult to model due to their spatial, temporal, and operational variability, ignoring their effects can lead to suboptimal supply-chain designs, inaccurate cost estimates, and unrealistic assessments of biorefinery performance. This paper reviews the treatment of biomass quality characteristics in the literature on quantitative modeling and analysis of biomass-to-biorefinery supply chains. Positioned from an Operational Research (OR) perspective, this review emphasizes mathematical modeling, computer simulation, optimization, and decision-support approaches for biomass-to-bioenergy systems. A total of 71 English-language published articles are reviewed and classified according to modeling approach and quality characteristic(s) considered. Across the selected literature that quantified biomass quality effects, cost reductions along supply chain operations ranging from 6% to 31% were reported when quality-aware models were compared with approaches that ignored quality or assumed unrealistic biomass quality characteristics. Despite these findings, biomass quality remains underrepresented in current analytical models; ash content, dry matter loss, and chemical composition were considered in only 10.4%, 4.3%, and 0.9% of the reviewed literature, respectively. This review summarizes the current state of research and outlines a future research agenda for integrating biomass quality control, uncertainty modeling, and optimization into scalable bioenergy and biorefinery systems.