【摘要】 为了提高焦油产率预测的精度和效率，鉴于煤岩煤质指标之间的多元非线性复杂关系，通过筛选两淮煤田以往煤岩煤质指标较为齐全的129组钻孔数据，利用Pearson相关系数法相关性分析确定了氢碳比、氢元素、镜质组、挥发分与焦油产率相关性最强，氧化钙、氧化镁、氧化铁、氧化硅、氧化铝、固定碳与焦油产率相关性中等，其他指标相关性较弱，并将影响焦油产率的特征参数划分为“强、强+中、强+中+弱”三种指标参数组合，建立了基于PSO-GA-BP的组合优化算法预测模型，通过对不同参数组合进行机器学习训练，对比分析了不同预测模型实际应用效果。结果表明：“强+中”特征参数组合样本数据在训练过程中，性能和训练状态较优，其最佳适应度最大，绝对系数R~2、均方根误差RMSE、平均绝对误差MAE均好于其他特征参数组合。通过与BP、GA-BP、PSO-BP算法模型进行对比，PSO-GA-BP组合优化算法模型误差最小，在提高焦油产率预测精度和数据拟合效果方面更具优势。将本次利用钻孔建立的PSO-GA-BP组合算法模型应用到巷道采集扩展样品焦油产率预测中，预测模型表现出较好的泛化能力。建立完整和全面的煤岩煤质数据库，利用先进智能算法，可进一步提高模型学习能力和预测效果。更多还原

【Abstract】 In order to improve the accuracy and efficiency of prediction of tar yield, in view of the multivariate nonlinear complex relationship between coal, rock and coal quality indexes, the characteristic parameters affecting tar yield are divided into three combinations of strong, strong + medium, strong + medium + weak by screening 129 groups of borehole data with complete coal, rock and coal quality indexes in Huainan-Huaibei coalfield through correlation analysis.The correlation analysis was conducted to determine the hydrogen-to-carbon ratio, hydrogen content, vitrinite reflectance, volatile matter, and tar yield were most strongly correlated, while the oxides of calcium, magnesium, iron, silicon, aluminum, and fixed carbon were moderately correlated with tar yield.Other indicators had weaker correlations through Pearson correlation coefficient method.The characteristic parameters affect tar yield were divided into three parameter combinations: strong, strong+medium, and strong+medium+weak, and a combination optimization algorithm prediction model based on PSO-GA-BP.Different parameter combinations were trained using machine learning, and the actual application effects of different prediction models were compared and analyzed.The results show that the best fitness of the sample data of the strong + medium feature parameter combination is the largest in the training process, and the absolute coefficient R~2,root mean square error RMSE and mean absolute error MAE are better than other feature parameter combinations with better performance and training status.The PSO-GA-BP combined optimization algorithm has the smallest error, and has more advantages in improving the prediction accuracy and data fitting effect of tar yield compared with BP,GA-BP and PSO-BP algorithm.The PSO-GA-BP is applied to predict the tar yield of extended sample collected in roadway, and the prediction model shows good generalization ability combined algorithm model established by drilling.The establishment of complete and comprehensive coal rock and coal quality database is the basis and prerequisite for further improving the model learning ability and forecasting effect through advanced intelligent algorithms.更多还原