文献类型：期刊文献

英文题名：Semi-supervised learning for predicting multivariate attributes of process units from small labeled and large unlabeled data sets with application to detect properties of crude feed distillation unit

作者：Zhu, Jiannan[1,2];Fan, Chen[1];Yang, Minglei[1,3];Qian, Feng[1,3];Mahalec, Vladimir[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai, Peoples R China;[2]McMaster Univ, Dept Chem Engn, Hamilton, ON L8S 4L8, Canada;[3]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai, Peoples R China

年份：2024

卷号：298

外文期刊名：CHEMICAL ENGINEERING SCIENCE

收录：;EI(收录号：20242616422863);WOS:【SCI-EXPANDED(收录号:WOS:001261491500001)】；

基金：This work was supported by National Natural Science Foundation of China (Basic Science Center Program: 61988101), National Natural Science Foundation of China (62293501), the Shanghai Committee of Science and Technology, China (Grant No. 22DZ1101500), the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017, Fundamental Research Funds for the Central Universities and Shanghai AI Lab.

语种：英文

外文关键词：Crude feed properties estimation; Semi-supervised learning; Pseudo-labeling; Clustering co-training semi-supervised learning (CCoT-SSL); Stacked co-training semi-supervised learning (SCoT-SSL)

摘要：In a refinery, the accurate estimation of feed properties is crucial for precise real-time optimization (RTO), and thus, developing models for real-time estimation of crude feed properties from plant measurements remains a challenge. The unbalanced dataset arising from varying variable collection frequencies hinders estimation from plant data. To address these challenges, this study initially proposes two novel algorithms, the clustering cotraining semi-supervised learning (CCoT-SSL) and stacked co-training semi-supervised learning (SCoT-SSL), to solve the unbalanced dataset problem. In contrast to prior work, this study tackles the estimation of multivariate attributes when available data comprises small labeled and large unlabeled data with a "cascade shape" structure and illustrates the proposed methods by estimating Crude Distillation Units (CDU). Results indicate that CCoTSSL excels when the base learners are judiciously selected, which is essential for the real-time optimization of CDU and refinery operations.