文献类型：期刊文献

英文题名：Novel automatic model construction method for the rapid characterization of petroleum properties from near-infrared spectroscopy

作者：Yu, Huijing[1];Wang, Xinjie[1];Shen, Feifei[1];Long, Jian[1];Du, Wenli[1]

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

年份：2022

卷号：316

外文期刊名：FUEL

收录：;EI(收录号：20220511575308);WOS:【SCI-EXPANDED(收录号:WOS:000783173000001)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101) , International (Re-gional) Cooperation and Exchange Project, China (61720106008) , the National Natural Science Foundation of China (61973124) , National Natural Science Fund for Distinguished Young Scholars (61725301) and the Shanghai Sailing Program (21YF1409900) .

语种：英文

外文关键词：Petroleum; Chemometrics; Near-infrared spectroscopy; Automatic model construction; Genetic programming; Nonlinearity

摘要：Petroleum fuels play an important role in economic society, and near-infrared analysis has been widely used in the characterization of petroleum fuels due to its effectiveness and efficiency. However, near-infrared spectra are high dimensional data that require high computational cost, thereby complicating the practical application. Previous model construction methods are also restricted to pre-designed model structure, thus limiting the model construction ability. To solve these problems, this paper proposes an automatic model construction algorithm that increases the diversity of model structure with linear-in-parameter representation and optimizes the model structure with genetic programming. The proposed automatic construction algorithm is verified on datasets from literature and experiments with real petroleum products compared with Partial Least Square (PLS) and Support Vector Machine Regression (SVR). The proposed method outperforms PLS and SVR in overall predictive accuracy and reduces the involved variable number by at least 70% during the model construction process. Good performance in prediction accuracy, model complexity, and interpretability on the rapid characterization of different petroleum products can assist refinery plant in product control and management during production.