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。

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。

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 utilization of tar-rich coals.

To achieve more scientific and accurate fine-scale tar-rich coal assessments, this study examined 1073 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 algorithm, 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 accuracy and rationality of the results of both prediction models.

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 goodness of fit and generalization performance. (2) The importance of 20 lithotype and coal quality indices in the first prediction model was further quantitatively analyzed using the Shapley additive explanation (SHAP) algorithm. The results reveal 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 content, and SiO₂ content serve as negative factors influencing the tar yield. The intrinsic relationships between both the lithotype 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.