Lignocellulosic biomass-to-biofuel supply chain optimization with mobile densification and farmers’ choices

Abstract

This dissertation focuses on logistics challenges arising in the biofuels industry. Studies have found that logistics costs in the biomass-to-biofuel supply chain (BBSC) account for 35%-65% of total biofuel production cost. This is mainly due to the low density of biomass that results in high costs associated with biomass transportation, storage, and handling in the biomass-to-biofuel supply chain. Densification provides an as-yet-unexplored opportunity to reduce logistic costs associated with biomass-to-biofuel supply chains. This research advances understanding about biomass-to-biofuel supply chain management through new optimization models. As a first step, the author presents an extensive overview of densification techniques and BBSC optimization models that account for biomass densification. This literature review helps the author to recognize the gaps and future research areas in BBSC studies. These gaps direct the author toward the remaining components of the dissertation. In particular, the literature review highlights two research gaps. First, the review indicates that mobile pelleting holds promise for improved BBSC management, but that there is no mathematical optimization model that addresses this opportunity. Second, currently, there does not exist a model that explicitly accounts for farmers’ objectives and their probability to sell biomass to the bioenergy plant in BBSC optimization. To fill the first gap, the author focuses on managing the BBSC considering mobile densification units to account for chances to minimize logistics costs. A mixed integer linear programming model is proposed to manage the BBSC with different types and forms of biomass feedstock and mobile densification units. Sensitivity analysis and scenario analysis are presented to quantify conditions that make mobile densification an attractive choice. The author conducts a case study to demonstrate model applicability and type of analysis that can be drawn from this type of models. The result indicates that mobile pelleting is not an attractive choice under the current economic status. However, modest changes in pelleting cost, satellite storage location fixed cost, and/or travel distances are enough to make mobile pelleting an attractive choice. To fill the second gap, the author introduces a model that explicitly accounts for mobile densification and farmers’ probability to supply a bioenergy plant with biomass feedstock. Farmers’ probability to provide biomass to the bioenergy plant depends on contract attributes, including expected net return and services provided by the bioenergy plant. The proposed model helps the bioenergy plant to meet biofuel demand while considering farmers’ choices that satisfy their own objectives and preferences. The model makes it possible to determine most important factors that influence type of contract offered to each supplier and optimal BBSC design. A case study based on the state of Kansas is conducted to demonstrate how bioenergy plant can benefit from this type of model.