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Fuentes-Cortés LF, Flores-Tlacuahuac A, Nigam KDP. Machine Learning Algorithms Used in PSE Environments: A Didactic Approach and Critical Perspective. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c00335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Luis Fabián Fuentes-Cortés
- Departamento de Ingeniería Química, Tecnologico Nacional de México - Instituto Tecnológico de Celaya, Celaya, Guanajuato 38010, Mexico
| | - Antonio Flores-Tlacuahuac
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias Ave. Eugenio Garza Sada 2501, Monterrey, N.L. 64849, Mexico
| | - Krishna D. P. Nigam
- Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias Ave. Eugenio Garza Sada 2501, Monterrey, N.L. 64849, Mexico
- Department of Chemical Engineering, Indian Institute of Technology Delhi 600036, India
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Guo L, Shi H, Tan S, Song B, Tao Y. Multiblock Adaptive Convolution Kernel Neural Network for Fault Diagnosis in a Large-Scale Industrial Process. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Lei Guo
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
| | - Hongbo Shi
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
| | - Shuai Tan
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
| | - Bing Song
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
| | - Yang Tao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
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3
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Application of graph theory and filter based variable selection methods in the design of a distributed data-driven monitoring system. Comput Chem Eng 2020. [DOI: 10.1016/j.compchemeng.2020.107098] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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4
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Li Z, Yan X. Performance-driven ensemble ICA chemical process monitoring based on fault-relevant models. Soft comput 2020. [DOI: 10.1007/s00500-020-04673-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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6
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Khatib S, Daoutidis P. Optimal Feature Selection for Distributed Data-Driven Process Monitoring. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.9b03872] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Shaaz Khatib
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Prodromos Daoutidis
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
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7
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Wan X, Tong C, Luo L. Distributed Statistical Process Monitoring Based on Multiblock Canonical Correlation Analysis. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b04971] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Xinchun Wan
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, P. R. China
| | - Chudong Tong
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, P. R. China
| | - Lijia Luo
- Institute of Process Equipment and Control Engineering, Zhejiang University of Technology, Hangzhou 310014, China
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Li Z, Yan X. Complex dynamic process monitoring method based on slow feature analysis model of multi-subspace partitioning. ISA TRANSACTIONS 2019; 95:68-81. [PMID: 31151751 DOI: 10.1016/j.isatra.2019.05.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 05/14/2019] [Accepted: 05/15/2019] [Indexed: 06/09/2023]
Abstract
This study presents an ensemble monitoring strategy based on slow feature analysis (SFA) model of multi-subspace partitioning for dynamic large-scale process. SFA can effectively extract the various dynamics of process data, where the relationship between process data and slow features (SFs) can be revealed by transformation matrix. The similar projecting directions represent similar importance of variables, and corresponding latent variables (LVs) will show similar monitoring behavior. Several LV subspaces are obtained by dividing the transformation vectors with higher similarity into the same sub-block automatically based on the defined process variable related index and hierarchical clustering, which can avoid the problems of information loss and the selection of SFs. Then, the S2 statistics constructed in each subspaces are integrated by support vector data description to show an intuitive detection results. Experiments on Tennessee Eastman benchmark process and wastewater treatment process have validated the proposed strategy's effectiveness and excellence.
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Affiliation(s)
- Zhichao Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China
| | - Xuefeng Yan
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China.
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9
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Tao Y, Shi H, Song B, Tan S. Distributed Supervised Fault Detection and Diagnosis for a Non-Gaussian Process. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b00005] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Yang Tao
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Hongbo Shi
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Bing Song
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
| | - Shuai Tan
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, P. R. China
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Khatib S, Daoutidis P, Almansoori A. System Decomposition for Distributed Multivariate Statistical Process Monitoring by Performance Driven Agglomerative Clustering. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b01708] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Shaaz Khatib
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Prodromos Daoutidis
- Department of Chemical Engineering and Materials Science, University of Minnesota, Minneapolis, Minnesota 55455, United States
| | - Ali Almansoori
- Department of Chemical Engineering, Khalifa University of Science and Technology, Abu Dhabi, UAE
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11
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Li Z, Yan X. Adaptive Selective Ensemble-Independent Component Analysis Models for Process Monitoring. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b00591] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Zhichao Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
| | - Xuefeng Yan
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China
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Huang J, Yan X. Relevant and independent multi-block approach for plant-wide process and quality-related monitoring based on KPCA and SVDD. ISA TRANSACTIONS 2018; 73:257-267. [PMID: 29317086 DOI: 10.1016/j.isatra.2018.01.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 10/09/2017] [Accepted: 01/01/2018] [Indexed: 06/07/2023]
Abstract
Due to prior knowledge being often unavailable in practice, a multi-block strategy totally based on data-driven analytics is an appropriate alternative for plant-wide processes. However, most recent multi-block methods are relatively vague or insufficient for dividing up the process space and lack the comprehensive fault information for quality-related monitoring. This work intends to develop a more reasonable multi-block method and demonstrate the negative impacts of quality-unrelated variables. Both motivations are entirely dependent on the correlation between variables. A major innovation is to determine those independent or related sets of variables, and to provide a more precise indication for those quality-related faults. Sub-blocks with related variables are each modeled by the KPCA, and the rest of the independent variables are treated as an input for a SVDD model. Finally, all of the statistical indicators are aggregated into a single statistic through Bayesian inference. The benefits of the proposed multi-block scheme (MKPCA-SVDD) are elaborated on in detail using numerical simulation, TE benchmark and industrial p-xylene oxidation process.
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Affiliation(s)
- Junping Huang
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China
| | - Xuefeng Yan
- Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai, 200237, PR China.
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Gao H, Xu Y, Zhu Q. Spatial Interpretive Structural Model Identification and AHP-Based Multimodule Fusion for Alarm Root-Cause Diagnosis in Chemical Processes. Ind Eng Chem Res 2016. [DOI: 10.1021/acs.iecr.5b04268] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Huihui Gao
- Engineering
Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
- College
of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Yuan Xu
- Engineering
Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
- College
of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Qunxiong Zhu
- Engineering
Research Center of Intelligent PSE, Ministry of Education of China, Beijing 100029, China
- College
of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
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