城市供水管网爆管事件频发,基于爆管主影响因素的风险评估研究亟待深入开展。主影响因素识别是爆管风险评估准确性的重要基础,现有爆管风险评估模型研究缺乏对爆管主影响因素的分析,导致模型精度偏低。本文提出基于信息增益率权值的改进...城市供水管网爆管事件频发,基于爆管主影响因素的风险评估研究亟待深入开展。主影响因素识别是爆管风险评估准确性的重要基础,现有爆管风险评估模型研究缺乏对爆管主影响因素的分析,导致模型精度偏低。本文提出基于信息增益率权值的改进SHAP法(SHapley Additive ex Planation),改善了多影响因素共同作用时各个特征的shapley值区分度较小、误差较大的问题。使得特征区分度较好、误差降低,有利于影响爆管的主影响因素更容易被自来水公司发觉。对爆管评估模型进行主影响因素分析结果表明,基于改进SHAP法获得的爆管主影响因素有利于提高供水管道爆管风险评估的准确率。展开更多
Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the“selection of the optimal processing route”strategy.With the development of data predic...Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the“selection of the optimal processing route”strategy.With the development of data prediction systems represented by machine learning,it has become possible for real-time prediction systems of petroleum fraction molecular information to replace analyses such as gas chromatography and mass spectrometry.However,the biggest difficulty lies in acquiring the data required for training the neural network.To address these issues,this work proposes an innovative method that utilizes the Aspen HYSYS and full two-dimensional gas chromatography-time-of-flight mass spectrometry to establish a comprehensive training database.Subsequently,a deep neural network prediction model is developed for heavy distillate oil to predict its composition in terms of molecular structure.After training,the model accurately predicts the molecular composition of catalytically cracked raw oil in a refinery.The validation and test sets exhibit R2 values of 0.99769 and 0.99807,respectively,and the average relative error of molecular composition prediction for raw materials of the catalytic cracking unit is less than 7%.Finally,the SHAP(SHapley Additive ExPlanation)interpretation method is used to disclose the relationship among different variables by performing global and local weight comparisons and correlation analyses.展开更多
文摘城市供水管网爆管事件频发,基于爆管主影响因素的风险评估研究亟待深入开展。主影响因素识别是爆管风险评估准确性的重要基础,现有爆管风险评估模型研究缺乏对爆管主影响因素的分析,导致模型精度偏低。本文提出基于信息增益率权值的改进SHAP法(SHapley Additive ex Planation),改善了多影响因素共同作用时各个特征的shapley值区分度较小、误差较大的问题。使得特征区分度较好、误差降低,有利于影响爆管的主影响因素更容易被自来水公司发觉。对爆管评估模型进行主影响因素分析结果表明,基于改进SHAP法获得的爆管主影响因素有利于提高供水管道爆管风险评估的准确率。
基金the National Natural Science Foundation of China(22108307)the Natural Science Foundation of Shandong Province(ZR2020KB006)the Outstanding Youth Fund of Shandong Provincial Natural Science Foundation(ZR2020YQ17).
文摘Acquiring accurate molecular-level information about petroleum is crucial for refining and chemical enterprises to implement the“selection of the optimal processing route”strategy.With the development of data prediction systems represented by machine learning,it has become possible for real-time prediction systems of petroleum fraction molecular information to replace analyses such as gas chromatography and mass spectrometry.However,the biggest difficulty lies in acquiring the data required for training the neural network.To address these issues,this work proposes an innovative method that utilizes the Aspen HYSYS and full two-dimensional gas chromatography-time-of-flight mass spectrometry to establish a comprehensive training database.Subsequently,a deep neural network prediction model is developed for heavy distillate oil to predict its composition in terms of molecular structure.After training,the model accurately predicts the molecular composition of catalytically cracked raw oil in a refinery.The validation and test sets exhibit R2 values of 0.99769 and 0.99807,respectively,and the average relative error of molecular composition prediction for raw materials of the catalytic cracking unit is less than 7%.Finally,the SHAP(SHapley Additive ExPlanation)interpretation method is used to disclose the relationship among different variables by performing global and local weight comparisons and correlation analyses.
