THERMAL SCIENCE

International Scientific Journal

Thermal Science - Online First

Authors of this Paper

External Links

Xiang Wang

,

Shanhui Zhao

online first only

PSO/GA/ANN modeling and prediction for the higher heating values (HHVS) of solid fuels: The machine learning approach

ABSTRACT
The quick evaluation for the higher heating value (HHV) is crucial for thermochemical 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. 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 hidden layer number on the prediction performance was studied. Results show that single hidden layer with logsig function using 8 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, accounting for 21.73% and 22.91% respectively. Particle swarm optimization (PSO) and genetic algorithm (GA) were further used to optimize the artificial neural network 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 PSOANN and GA-ANN decreased to 1.15 % and 1.72 % respectively.
KEYWORDS
solid fuel, Higher heating value, machine learning, Artificial Neural Network
PAPER SUBMITTED: 2024-09-22
PAPER REVISED: 2024-12-23
PAPER ACCEPTED: 2025-01-09
PUBLISHED ONLINE: 2025-02-16
DOI REFERENCE: https://doi.org/10.2298/TSCI240922012W
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PSO/GA/ANN modeling and prediction for the higher heating values (HHVS) of solid fuels: The machine learning approach