Zhenhua Yu, Wenjing Wang, Xueting Wang, Qingchao Jiang, Guan Wang
{"title":"基于潜在映射嵌入深度神经网络的非线性动态过程监控","authors":"Zhenhua Yu, Wenjing Wang, Xueting Wang, Qingchao Jiang, Guan Wang","doi":"10.1002/cjce.25461","DOIUrl":null,"url":null,"abstract":"<p>In industrial processes, complex nonlinearity and dynamics generally exist, making it challenging to achieve good results using conventional process monitoring methods. In this paper, a latent mapping embedding neural network method (LMNN) is proposed for efficient monitoring of nonlinear dynamic processes. First, a deep neural network (DNN) is employed to acquire features of state variables from nonlinear process data and expand them along with the input to a new feature subspace. Second, a latent mapping (LM) method is used to map the high-dimensional feature subspace to a low-dimensional subspace that includes the most beneficial time series information. Then the entire neural network and regression parameters are obtained through an end-to-end learning manner, through which the nonlinearity and process dynamics are well characterized. Subsequently, prediction error-based residual is generated and the monitoring model is established. The performance of the proposed method is verified through a simulation of penicillin production process and an actual fermentation process of penicillin. Comparisons with state-of-the-art methods are carried out, and results validate the effectiveness and superiority of the proposed method.</p>","PeriodicalId":9400,"journal":{"name":"Canadian Journal of Chemical Engineering","volume":"103 4","pages":"1802-1812"},"PeriodicalIF":1.6000,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Nonlinear dynamic process monitoring based on latent mapping embedding deep neural networks\",\"authors\":\"Zhenhua Yu, Wenjing Wang, Xueting Wang, Qingchao Jiang, Guan Wang\",\"doi\":\"10.1002/cjce.25461\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>In industrial processes, complex nonlinearity and dynamics generally exist, making it challenging to achieve good results using conventional process monitoring methods. In this paper, a latent mapping embedding neural network method (LMNN) is proposed for efficient monitoring of nonlinear dynamic processes. First, a deep neural network (DNN) is employed to acquire features of state variables from nonlinear process data and expand them along with the input to a new feature subspace. Second, a latent mapping (LM) method is used to map the high-dimensional feature subspace to a low-dimensional subspace that includes the most beneficial time series information. Then the entire neural network and regression parameters are obtained through an end-to-end learning manner, through which the nonlinearity and process dynamics are well characterized. Subsequently, prediction error-based residual is generated and the monitoring model is established. The performance of the proposed method is verified through a simulation of penicillin production process and an actual fermentation process of penicillin. Comparisons with state-of-the-art methods are carried out, and results validate the effectiveness and superiority of the proposed method.</p>\",\"PeriodicalId\":9400,\"journal\":{\"name\":\"Canadian Journal of Chemical Engineering\",\"volume\":\"103 4\",\"pages\":\"1802-1812\"},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-08-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Canadian Journal of Chemical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cjce.25461\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Canadian Journal of Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cjce.25461","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
Nonlinear dynamic process monitoring based on latent mapping embedding deep neural networks
In industrial processes, complex nonlinearity and dynamics generally exist, making it challenging to achieve good results using conventional process monitoring methods. In this paper, a latent mapping embedding neural network method (LMNN) is proposed for efficient monitoring of nonlinear dynamic processes. First, a deep neural network (DNN) is employed to acquire features of state variables from nonlinear process data and expand them along with the input to a new feature subspace. Second, a latent mapping (LM) method is used to map the high-dimensional feature subspace to a low-dimensional subspace that includes the most beneficial time series information. Then the entire neural network and regression parameters are obtained through an end-to-end learning manner, through which the nonlinearity and process dynamics are well characterized. Subsequently, prediction error-based residual is generated and the monitoring model is established. The performance of the proposed method is verified through a simulation of penicillin production process and an actual fermentation process of penicillin. Comparisons with state-of-the-art methods are carried out, and results validate the effectiveness and superiority of the proposed method.
期刊介绍:
The Canadian Journal of Chemical Engineering (CJChE) publishes original research articles, new theoretical interpretation or experimental findings and critical reviews in the science or industrial practice of chemical and biochemical processes. Preference is given to papers having a clearly indicated scope and applicability in any of the following areas: Fluid mechanics, heat and mass transfer, multiphase flows, separations processes, thermodynamics, process systems engineering, reactors and reaction kinetics, catalysis, interfacial phenomena, electrochemical phenomena, bioengineering, minerals processing and natural products and environmental and energy engineering. Papers that merely describe or present a conventional or routine analysis of existing processes will not be considered.