Research on Coal Dust Wettability Identification Based on GA–BP Model

Haotian Zheng, Shulei Shi (), Bingyou Jiang (), Yuannan Zheng, Shanshan Li and Haoyu Wang
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Haotian Zheng: Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining, Anhui University of Science and Technology, Huainan 232001, China
Shulei Shi: Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining, Anhui University of Science and Technology, Huainan 232001, China
Bingyou Jiang: Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining, Anhui University of Science and Technology, Huainan 232001, China
Yuannan Zheng: Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining, Anhui University of Science and Technology, Huainan 232001, China
Shanshan Li: School of Economics and Management, Anhui University of Science and Technology, Huainan 232001, China
Haoyu Wang: School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China

IJERPH, 2022, vol. 20, issue 1, 1-18

Abstract: Aiming at the problems of the influencing factors of coal mine dust wettability not being clear and the identification process being complicated, this study proposed a coal mine dust wettability identification method based on a back propagation (BP) neural network optimized by a genetic algorithm (GA). Firstly, 13 parameters of the physical and chemical properties of coal dust, which affect the wettability of coal dust, were determined, and on this basis, the initial weight and threshold of the BP neural network were optimized by combining the parallelism and robustness of the genetic algorithm, etc., and an adaptive GA–BP model, which could reasonably identify the wettability of coal dust was constructed. The extreme learning machine (ELM) algorithm is a single hidden layer neural network, and the training speed is faster than traditional neural networks. The particle swarm optimization (PSO) algorithm optimizes the weight and threshold of the ELM, so PSO–ELM could also realize the identification of coal dust wettability. The results showed that by comparing the four different models, the accuracy of coal dust wettability identification was ranked as GA–BP > PSO–ELM > ELM > BP. When the maximum iteration times and population size of the PSO algorithm and the GA algorithm were the same, the running time of the different models was also different, and the time consumption was ranked as ELM < BP < PSO–ELM < GA–BP. The GA–BP model had the highest discrimination accuracy for coal mine dust wettability with an accuracy of 96.6%. This study enriched the theory and method of coal mine dust wettability identification and has important significance for the efficient prevention and control of coal mine dust as well as occupational safety and health development.

Keywords: coal dust wettability; BP neural network; GA–BP model; identification (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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