School of Safety Science and Engineering, Xi'an University of Science and Technology, 58, Yanta Mid. Rd., Xi'an, Shaanxi, 710054, People's Republic of China.
Shaanxi Key Laboratory of Prevention and Control of Coal Fire, Xi'an University of Science and Technology, Xi'an, 710054, People's Republic of China.
Environ Sci Pollut Res Int. 2022 Apr;29(19):28070-28082. doi: 10.1007/s11356-021-18387-1. Epub 2022 Jan 5.
The harmful gases produced from coal spontaneous combustion (CSC) can cause the environmental pollution. Being able to predict the experimental minimum period of CSC (EMPCSC) is essential in controlling CSC and effectively reducing harmful gas emissions. To obtain high prediction accuracy, we used three optimization algorithms, namely the genetic algorithm (GA), ant colony algorithm (ACO), and particle swarm optimization algorithm (PSO), to optimize the backpropagation neural network (BPNN). R, MSE, RMSE, and MAPE were used as evaluation indexes to determine the most accurate prediction model for EMPCSC. Data of 424 coal samples from 15 regions in China were analyzed, with 207 and 217 samples having a spontaneous combustion period of less than 40 days (W) and more than 40 days (V), respectively. The two groups were further distributed between low-temperature slow oxidation (W and V) and low-temperature fast oxidation (W and V). The results indicated that the prediction performance of the BPNN model optimized using PSO (PSO-BPNN) was better than that of the GA-BPNN and ACO-BPNN models. After optimization through PSO, the goodness of fit (R) of groups W, W, V, and V increased from 0.9180, 0.8746, 0.9987, and 0.9782 to 0.9857, 0.9639, 0.9997, and 0.9994, respectively. Therefore, the results can provide a theoretical reference for selecting the optimal neural network model to predict EMPCSC with high accuracy.
煤自燃（CSC）产生的有害气体可能会造成环境污染。预测煤自燃实验最短周期（EMPCSC）对于控制煤自燃和有效减少有害气体排放至关重要。为了获得较高的预测精度，我们使用了三种优化算法，即遗传算法（GA）、蚁群算法（ACO）和粒子群优化算法（PSO），对反向传播神经网络（BPNN）进行了优化。采用 R、MSE、RMSE 和 MAPE 作为评价指标，确定了 EMPCSC 最准确的预测模型。分析了来自中国 15 个地区的 424 个煤样数据，其中 207 个和 217 个煤样的自燃周期小于 40 天（W）和大于 40 天（V）。这两组煤样进一步分为低温缓慢氧化（W 和 V）和低温快速氧化（W 和 V）。结果表明，经 PSO 优化后的 BPNN 模型（PSO-BPNN）的预测性能优于 GA-BPNN 和 ACO-BPNN 模型。经 PSO 优化后，W、W、V 和 V 组的拟合优度（R）从 0.9180、0.8746、0.9987 和 0.9782 分别提高到 0.9857、0.9639、0.9997 和 0.9994。因此，结果可为选择最佳神经网络模型来准确预测 EMPCSC 提供理论参考。
