文献类型：期刊文献

英文题名：Data-driven online operating performance assessment for multi-datasets multivariable industrial processes

作者：Du, Yupeng[1]; Wang, Zhenlei[1]; Wang, Xin[2]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai, 200237, China; [2] Center of Electrical and Electronic Technology, Shanghai Jiao Tong University, Shanghai, 200240, China

年份：2017

卷号：61

起止页码：1729

外文期刊名：Chemical Engineering Transactions

收录：EI(收录号：20174104261287)

语种：英文

外文关键词：Chemical operations

摘要：In this study, a novel online operating performance assessment method based on multi-sets two-step basis vector extraction artificial neural networks (MTBVE-ANN) strategy is proposed for industrial applications. The MTBVE-ANN method focuses on finding common and specific information involved in multi-datasets, which improves the accuracy of data nonlinear characterization with artificial neural networks introduced. The optimality related variations are extracted from each operating performance grade by analysing the common and unique variations over online steady performance grades. The online operating performance assessment method is performed based on the similarities between the optimality related variations of the test data and that of historical training data. Previously, total projection to latent structures (T-PLS) operating performance assessment method must be performed based on the availability of both input and output data. The proposed method in this paper takes the artificial neural network to assess the operating performance grade of the online test data without output. The validity and precision of the proposed operating performance assessment method is illustrated with the industrial data of multi-datasets multivariable industrial processes. Copyright ? 2017, AIDIC Servizi S.r.l.