文献类型：期刊文献

英文题名：Interpretable reconstruction of naphtha components using property-based extreme gradient boosting and compositional-weighted Shapley additive explanation values

作者：Shi, Yi[1];Zhong, Weimin[1];Peng, Xin[1];Yang, Minglei[1,2];Du, Wei[1,2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Meilong Rd 130, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Meilong Rd 130, Shanghai 200237, Peoples R China

年份：2024

卷号：284

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20234715094360);WOS:【SCI-EXPANDED(收录号:WOS:001116920200001)】；

基金：This work was supported by National Natural Science Fund for Distinguished Young Scholars (61925305), National Natural Science Foundation of China (Major Program: 61890930-3) , National Natural Science Foundation of China (62173145, 62303186) , Fundamental Research Funds for the Central Universities and Shanghai AI Lab.

语种：英文

外文关键词：Molecular reconstruction; Naphtha; XGBoost; Explainable machine learning; SHAP

摘要：Various methods exist for reconstructing the molecular composition of petroleum feedstocks from their bulk properties. While data-driven approaches are precise and efficient, they often lack mechanistic insight. This paper presents an interpretable, data-driven model for naphtha composition reconstruction. Utilizing a property-based Extreme Gradient Boosting (XGBoost) model, optimized with the Tree Parzen Estimator (TPE) and property mixing rules, we achieve notable accuracy. The model leverages Shapley Additive Explanations (SHAP) to elucidate the influence of each property on specific compositions. Moreover, we introduce a compositional-weighted SHAP metric, revealing overarching molecular distribution patterns. Our analyses show that PIONA values and boiling points have a more pronounced effect on molecular compositions than other examined properties. Finally, the SOL-CNN model is employed for accurate property prediction of predefined components.