Predicting biomass transportation costs: A machine learning approach for enhanced biofuel competitiveness

Abstract

The escalating depletion of hydrocarbon reserves and the escalating global climate crisis have catalyzed a significant shift towards biofuels as a viable alternative to fossil fuels. However, the substantial cost disparity between biofuels and conventional fossil fuels presents a formidable obstacle to their widespread adoption. A pivotal component within the biofuel supply chain is the substantial financial burden associated with transporting biomass feedstocks to biorefineries for subsequent fuel production. For many low-cost or residue-based biomass feedstocks, the transportation cost represents a substantial portion of the total delivered price, often dominating the overall feedstock cost—especially when sourced from widely distributed or small-scale suppliers. Despite extensive scholarly inquiry, a comprehensive and accurate predictive model for biomass road transport costs remains elusive. This study endeavors to address this critical knowledge gap by conducting an in-depth analysis of global biomass road transport data to meticulously identify the key parameters that exert a significant influence on transportation costs. Through rigorous correlation analysis, fifteen independent variables were identified as having a discernible impact on the final transportation cost. Departing from the prevalent reliance on regression analysis in previous studies, this research demonstrates the limitations of multiple linear regression for accurately predicting transportation costs. Consequently, this study explores the predictive capabilities of two alternative machine learning algorithms: random forests and artificial neural networks. Comparative analysis unequivocally demonstrates the superior predictive performance of the random forest model, achieving a remarkable R-squared value of 97.4 % and a root mean square error of 165. Furthermore, this study delves into the relative importance of each independent variable in determining the overall transportation cost. In the multiple linear regression model, load factor and vehicle type emerged as the most influential factors, contributing 37 % and 31 % to the total cost variation, respectively. Conversely, the impact of distance on transportation costs was found to be minimal. In the more robust random forest model, vehicle type, distance, and load factor were identified as the most significant predictors, contributing 31 %, 25 %, and 12 % to the overall cost variation, respectively. The predictive model developed in this study offers valuable insights into the cost dynamics of biomass transportation. By facilitating precise predictions of transportation costs, stakeholders are empowered to streamline logistical operations, augment operational efficiency, and consequently, curtail overall biofuel production expenses. The resultant enhancement in the price competitiveness of biofuels relative to fossil fuels is poised to stimulate broader utilization of these renewable resources. Furthermore, the transportation sector, a primary consumer of fuel, stands to gain substantially, as the adoption of cost-effective, cleaner-burning fuels fosters a transition towards more sustainable logistics practices.