Self-heating and spontaneous ignition of biomass storage piles: Towards a reliable prediction tool

Large-scale biomass storage for modern bioenergy introduces potential safety concerns due to the intrinsic self-heating of biomass. Despite this, very limited research has been conducted in this area. This project fills a critical gap by developing a comprehensive modelling framework for self-heating in biomass piles and conducting a series of experimental studies to explore the intricate sub-processes within these piles. This paper presents only a small portion of the modelling and testing work. It successfully demonstrates the model's usefulness in predicting self-heating in coal piles, guiding safety measures for coal storage. Among various storage parameters, the pile height, particle size and ambient wind velocity have been identified as having substantial impacts on self-heating and self-ignition within the coal pile. This paper also illustrates the pronounced effects of initial biomass moisture content on microbial reactivity and oxygen consumption rate. An increase in initial moisture content significantly enhances the overall microbial reactivity and oxygen consumption rate. Wheat straw, compared to rice straw, is more prone to self-heating under identical storage conditions, as evidenced by higher heat generation, faster oxygen consumption, and a shorter time to peak temperature. Moreover, microbial activity is found to play a critical role in biomass self-heating, particularly in the temperature range of 0–75 °C, during the initial stages of heat accumulation. For the modelling discussed here, the flow within the porous pile must be considered laminar, despite the highly turbulent flow around the pile. This is based on the Reynolds number, calculated from the in-pile volume average of the velocity and the mean diameter of the fuel particles, which is significantly below the critical threshold (Re_cr=200). The insights and sub-models to be derived from the relevant sub-processes in biomass pile can be integrated into the model framework. This integration will create a more comprehensive and robust model for predicting self-heating and self-ignition in biomass storage piles, enhancing both theoretical and practical management of these phenomena.