TY - JOUR
TI - The PSO/GA/ANN modeling and prediction for the higher heating values of solid fuels: The machine learning approach
AU - Wang Xiang
AU - Zhao Shanhui
JN - Thermal Science
PY - 2025
VL - 29
IS - 4
SP - 2881
EP - 2897
PT - Article
AB - The quick evaluation for the higher heating value (HHV) is crucial for thermo-chemical conversion of solid fuels. In this work, machine learning method based on artificial neural networks (ANN) was used to predict the HHV of solid fuel. The 205 groups of different kinds of solid fuels collected from publications were used. The proximate analysis, ultimate analysis and the combination of two were used as input parameters. The influence of activation function, neuron number, and hid-den layer number on the prediction performance was studied. Results show that single hidden layer with logsig function using eight neurons was an optimized condition for HHV prediction. The combination of two composition analyses could achieve much higher accuracy, with the average relative error of 2.57%. Impact analysis indicated that the non-combustible components, namely ash content and oxygen content showed the largest influencing weight for HHV prediction, ac-counting for 21.73% and 22.91%, respectively. Particle swarm optimization (PSO) and genetic algorithm (GA) were further used to optimize the ANN model. Results show that PSO and GA both improved the prediction performance of ANN model by optimizing the initial weight and threshold values. The average relative errors for PSO-ANN and GA-ANN decreased to 1.15 % and 1.72 %, respectively.