Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining, Anhui University of Science and Technology, Huainan 232001, China.
School of Safety Science and Engineering, Anhui University of Science and Technology, Huainan 232001, China.
Int J Environ Res Public Health. 2022 Dec 29;20(1):624. doi: 10.3390/ijerph20010624.
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.
针对煤矿粉尘润湿性影响因素不明确、识别过程复杂的问题，提出了一种基于遗传算法（GA）优化反向传播（BP）神经网络的煤矿粉尘润湿性识别方法。首先，确定了 13 个影响粉尘润湿性的煤尘物理化学性质参数，在此基础上，结合遗传算法的并行性和鲁棒性等，优化 BP 神经网络的初始权值和阈值，构建了能够合理识别煤尘润湿性的自适应 GA-BP 模型。极限学习机（ELM）算法是一种单隐层神经网络，训练速度快于传统神经网络。粒子群优化（PSO）算法优化 ELM 的权值和阈值，因此 PSO-ELM 也可以实现煤尘润湿性的识别。结果表明，通过比较 4 种不同模型，煤尘润湿性识别的准确率由高到低依次为 GA-BP＞PSO-ELM＞ELM＞BP。当 PSO 算法和 GA 算法的最大迭代次数和种群规模相同时，不同模型的运行时间也不同，时间消耗由低到高依次为 ELM＜BP＜PSO-ELM＜GA-BP。GA-BP 模型对煤矿粉尘润湿性的判别准确率最高，为 96.6%。丰富了煤矿粉尘润湿性识别的理论和方法，对煤矿粉尘的高效防治以及职业安全健康发展具有重要意义。
