摘要
[目的]焦油产率是煤低温干馏利用最重要的煤质参数,决定着富油煤的清洁利用方向.但由于多方面的原因,在煤炭地质勘查阶段对煤焦油产率的测试数据十分有限,极大地制约了富油煤的精细评价和高效利用.[方法]为了提高富油煤精细评价的科学性和准确性,以陕北侏罗纪煤田以往测试 1 073组煤岩煤质数据为基础,并筛选出显微组分、工业分析、元素分析、灰成分分析等20项煤岩煤质参数齐全的 141组数据,利用BP神经网络算法分别建立了 20项煤岩煤质指标的焦油产率预测模型和以 4项工业分析为基础的焦油产率预测模型,并对预测模型的准确性和合理性进行分析评价.[结果和结论]结果表明:以 20项煤岩煤质指标为特征建立的预测模型最终训练均方误差为 0.30,测试集数据预测结果平均绝对误差为 0.65;以 4项工业分析指标为特征建立的预测模型最终训练均方误差为 1.07,测试集数据预测结果平均绝对误差为 1.35;扩展集数据在两个模型中预测结果平均绝对误差分别为 0.84和 1.34,显示出 20项煤岩煤质指标比 4项工业分析煤质指标建立的预测模型具有更高的拟合优度和泛化性能.利用SHAP算法进一步对预测模型中 20项煤岩煤质指标的重要性进行量化分析,显示出镜质组、氢元素、三氧化二铁、水分、挥发分、碳元素、壳质组、氧元素含量是焦油产率的正向影响因素,三氧化二铝、惰质组、固定碳、灰分、二氧化硅含量是焦油产率的负向影响因素,模型中煤岩煤质与焦油产率之间的内在联系很好地契合了地质上对焦油产率影响因素的基本认识,该焦油产率预测模型可以很好地应用于陕北侏罗纪煤田的焦油产率预测,为陕北地区富油煤的清洁高效利用提供支撑.
Abstract
[Objective]Tar yield,the most important coal quality parameter for coal utilization through low-temperature pyrolysis,determines the clean utilization of tar-rich coals.However,various constraints result in limited test data on tar yield in the geological exploration stage of coals,substantially restricting the fine-scale assessment and efficient utiliza-tion of tar-rich coals.[Methods]To achieve more scientific and accurate fine-scale tar-rich coal assessments,this study examined 1 073 sets of lithotype and coal quality data obtained previously from a Jurassic coalfield in northern Shaanxi.From these data,141 sets with 20 lithotype and coal quality parameters regarding macerals,proximate analysis,ultimate analysis,and ash composition analysis were selected.Then,employing the back propagation(BP)neural network al-gorithm,this study constructed two tar yield prediction models based on 20 lithotype and coal quality indices and four proximate analysis indices each(also referred to as the first and second models,respectively).Finally,it assessed the accu-racy and rationality of the results of both prediction models.[Results and Conclusions]The results are as follows:(1)The first model exhibited a mean square error(MSE)of 0.30 in the final training and a mean absolute error(MAE)of 0.65 for the prediction results of the test set data.In contrast,the second model yielded a MSE of 1.07 in the final training,with a MAE of 1.35 for the prediction results of the test set data.For the prediction results of the superset data,the first and second models yielded MAEs of 0.84 and 1.34,respectively,suggesting that the first model features higher good-ness of fit and generalization performance.(2)The importance of 20 lithotype and coal quality indices in the first predic-tion model was further quantitatively analyzed using the Shapley additive explanation(SHAP)algorithm.The results re-veal that factors including vitrinite,hydrogen and carbon elements,Fe2O3,moisture,volatile constituents,exinite,and oxygen content prove to be the positive factors influencing the tar yield,whereas Al2O3,inertinite,fixed carbon,ash con-tent,and SiO2 content serve as negative factors influencing the tar yield.The intrinsic relationships between both the li-thotype and coal quality and the tar yield,derived from the first model,align well with the general understanding of the geological factors influencing the tar yield.Therefore,the first prediction model can effectively predict the tar yield of the Jurassic coalfield in northern Shaanxi,providing support for the clean and efficient utilization of tar-rich coals in northern Shaanxi.
基金项目
国家自然科学基金青年基金项目(42002194)
国家自然科学基金重点项目(42330808)
陕西省自然科学基础研究计划企业联合基金项目(2019JL-01)
陕煤化工集团科学技术研究计划项目(2021SMHKJ-A-J-07-02)
国家科技期刊平台
NSTL
万方数据