文献类型：期刊文献

英文题名：Soft-Sensor Model for Chemical Processes Based on D-Vine Copula with Rolling Pin Transformation

作者：Cai, Jun[1]; Zhou, Yang[1]; Zhang, Zhihua[2]; Li, Shaojun[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai, 200237, China; [2] School of Electronics and Information Engineering, University of Science and Technology Liaoning, Anshan, 114051, China

年份：2019

卷号：58

期号：41

起止页码：18965

外文期刊名：Industrial and Engineering Chemistry Research

收录：EI(收录号：20194207534688)

语种：英文

外文关键词：Ethylene - Distribution functions - Metadata

摘要：Soft-sensing methods have been widely used in recent years to predict key variables that are difficult to measure or involve costly and time-consuming in chemical processes. Owing to the increasing complexity of industrial processes, industrial data often exhibit strong nonlinearities and self-correlation, and the data distribution fails to satisfy the Gaussian assumption. To address these problems, a vine copula-based soft-sensor model combined with the rolling pin method is proposed. This approach uses a D-vine copula to model a joint probability distribution of auxiliary variables and a key variable. In accordance with the joint distribution, the predicted value of the key variable can be determined by weighting the training samples. During modeling, the Bayesian information criterion is adopted to select the best-fitted copula pairs. Given the nonmonotonicity between variables of actual industrial data, the rolling pin monotonic transformation is simultaneously introduced to improve the suitability of transformed data for vine copula modeling. The efficiency of the proposed method was verified using a numerical example and ethylene cracking. Copyright ? 2019 American Chemical Society.