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

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.

Garlic Origin Traceability and Identification Based on Fusion of Multi-Source Heterogeneous Spectral Information

“…The feature variables extracted by GA were combined with four classification models to assess their classification performance. Zheng et al [29] proposed a BP neural network based on GA optimization for coal mine dust wettability identification. They compared it with a particle swarm optimization (PSO) extreme learning machine (ELM) algorithm.…”

Application research on the prediction of tar yield of deep coal seam mining areas based on PSO-BPNN machine learning algorithm

“…Thus, a machine learning method is suitable for investigating the relationship between tar yield and other geological evaluation indices of coal. With the progress of science and technology, the technique of machine learning is more and more applied to the geological coal industry such as gas outburst prediction (Wu et al, 2020;Gao et al, 2023;Zhu et al, 2023), coal ash softening temperature prediction (Liang et al, 2020), coal gangue identification (Wang et al, 2022), coal dust wettable identification (Zheng et al, 2023), coal seam impact risk assessment (Zhang et al, 2022), etc. It has become a new research hotspot in coal geological engineering practice to mine the relationship between various nonlinear big data through machine learning algorithms to realize data prediction.…”