LAUSR

This study applied the artificial neural networks (ANNs) model to the thermal data obtained by suspension ignition and combustion experiment of single peanut shells (PS, millimeter scale) pellet under O2/CO2 atmosphere. ANN11 was the best ANN model for predicting the relevant parameters of PS combustion. The coincidence between ANN prediction data and experimental data was over 99%. Two modes of biomass pellet ignition were observed: homogeneous ignition of volatiles and hetero‐homogeneous ignition of volatiles and char simultaneously. The ignition mode was transformed from homogeneous ignition to hetero‐homogeneous ignition when oxygen concentration was 50%. In addition, it was observed that ignition at the bottom emerged first, and then the upper end was ignited, finally generating an envelope flame. This phenomenon occurred when gas flow temperature exceeded 873 K or the oxygen concentration was greater than 50%. The reduction of ignition delay time and internal ignition temperature from 21% to 50% oxygen concentration was more intense than that of 50% to 100% oxygen concentration. Increasing oxygen concentration or temperature resulted in a shorter, brighter, and more stable volatile flame of biomass pellets, which reduced volatile burnout time. Nevertheless, the impact of gas flow rate on biomass combustion was intricate and irregular.