Optimizing the cost of cellulosic ethanol production using multimodal straw collection and storage

Despite the increasing availability of raw materials, cellulosic ethanol remains costly to produce. With the aim of identifying cost drivers and optimizing logistics strategies, this study presents a quantitative framework integrating multimodal modeling of straw collection and storage with a full-chain cost assessment. Four representative collection modes are evaluated, and a mixed-mode optimization model is proposed to minimize costs by adjusting collection radii and station numbers under varying levels of demand. A case study of a 30,000-ton-per-year plant in Suihua, China, shows that the mechanized–centralized mode achieves the lowest unit cost at 169.59 yuan per ton, while mixed modes offer a flexible balance between cost control and adaptability. Meanwhile, straw density significantly influences logistics costs, especially in manual systems. Full-chain analysis further identifies raw materials as the largest cost component with 60% of total costs, with cellulase use and energy consumption in pretreatment emerging as the other two most critical drivers of cost. This study contributes by introducing an integrated multimodal optimization model for straw logistics, addressing gaps in existing research through the consideration of various combinations of collection modes. It constructs a model that incorporates critical factors like straw density and production scale, enhancing real-world applicability and cost accuracy. It also identifies key cost drivers through empirical analysis, offering strategies for cost reduction and operational optimization.