1
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Amin MT, Khan F. Dynamic Process Safety Assessment Using Adaptive Bayesian Network with Loss Function. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.2c03080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Md. Tanjin Amin
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas77843-3122, United States
| | - Faisal Khan
- Mary Kay O’Connor Process Safety Center, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, Texas77843-3122, United States
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2
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Chen H, Jiao L, Li S. A soft sensor regression model for complex chemical process based on generative adversarial nets and vine copula. J Taiwan Inst Chem Eng 2022. [DOI: 10.1016/j.jtice.2022.104483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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3
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Liu S, Li S. Multi-model D-vine copula regression model with vine copula-based dependence description. Comput Chem Eng 2022. [DOI: 10.1016/j.compchemeng.2022.107788] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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4
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Jia Q, Li S. Process Monitoring Based on the Multiblock Rolling Pin Vine Copula. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c02989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Qiong Jia
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
| | - Shaojun Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
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5
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Zhou Y, Ren X, Li S. Nonlinear Non-Gaussian and Multimode Process Monitoring-Based Multi-Subspace Vine Copula and Deep Neural Network. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01594] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Yang Zhou
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Xiang Ren
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Shaojun Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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6
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Jia Q, Li S. Process Monitoring and Fault Diagnosis Based on a Regular Vine and Bayesian Network. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c01474] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Qiong Jia
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Shaojun Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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7
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Cui Q, Li S. Process monitoring method based on correlation variable classification and vine copula. CAN J CHEM ENG 2020. [DOI: 10.1002/cjce.23702] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Affiliation(s)
- Qun Cui
- Key Laboratory of Advanced Control and Optimization for Chemical ProcessesMinistry of Education, East China University of Science and Technology Shanghai China
| | - Shaojun Li
- Key Laboratory of Advanced Control and Optimization for Chemical ProcessesMinistry of Education, East China University of Science and Technology Shanghai China
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8
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Ni J, Zhou Y, Li S. Hamiltonian Monte Carlo-Based D-Vine Copula Regression Model for Soft Sensor Modeling of Complex Chemical Processes. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.9b05370] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jianeng Ni
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Yang Zhou
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Shaojun Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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9
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Tian Y, Yao H, Li Z. Plant-wide process monitoring by using weighted copula-correlation based multiblock principal component analysis approach and online-horizon Bayesian method. ISA TRANSACTIONS 2020; 96:24-36. [PMID: 31350045 DOI: 10.1016/j.isatra.2019.06.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Revised: 05/29/2019] [Accepted: 06/01/2019] [Indexed: 06/10/2023]
Abstract
Multiblock methods have been proposed to capture the complex characteristics of plant-wide monitoring due to the enlargement of process industries. These methods based on automatic sub-block division and copula-correlation, which simultaneously describe the correlation degree and correlation patterns, are designed for sub-block partition. However, the selection of variables for each sub-block through copula-correlation analysis requires a pre-defined cutoff parameter which is difficult to be determined without sufficient prior knowledge, and a "bad" parameter leads to a degraded performance. Therefore, a weighted copula-correlation-based multiblock principal component analysis (WCMBPCA) is proposed. First, the variables in each sub-block are obtained through the copula-correlation analysis-based weighted strategy rather than the cutoff parameter, which highly avoids information loss and prevents "noisy" information. Second, a PCA model is established in each sub-block. Third, a Bayesian inference strategy is used to merge the monitoring results of all sub-blocks. Finally, an online-horizon Bayesian fault diagnosis system is established to identify the fault type of the system based on the statistics of each sub-block. The average detection rate and the average diagnosis rate for numerical example are 77.85% and 98.95%, and that for TE example are 80.63% and 89.50%. Comparing with other candidate methods, the proposed method achieves excellent detection and diagnostic performance.
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Affiliation(s)
- Ying Tian
- Shanghai Key Lab of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| | - Heng Yao
- Shanghai Key Lab of Modern Optical System, School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Zeqiu Li
- School of Energy and Power Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
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10
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Jia Q, Deng H, Ren X, Li S. Process Monitoring Method Based on Double-Model and Multi-Subspace Vine Copula. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b01781] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Qiong Jia
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
| | - Hongtao Deng
- Department of Information Science and Technology, Shihezi University, Shihezi 82000, China
| | - Xiang Ren
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Shaojun Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
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11
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Zhou Y, Li S, Xiong N. Improved Vine Copula-Based Dependence Description for Multivariate Process Monitoring Based on Ensemble Learning. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b04081] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yang Zhou
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
| | - Shaojun Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
| | - Ning Xiong
- School of Innovation, Design and Engineering, Mälardalen University, SE-72123 Västeras, Sweden
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12
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Zhou Y, Ren X, Li S. Enhancing Quality of Multivariate Process Monitoring Based on Vine Copula and Active Learning Strategy. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.7b05128] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Yang Zhou
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
| | - Xiang Ren
- Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Shaojun Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
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13
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Zhou N, Li S. Nonlinear and Non-Gaussian Process Monitoring Based on Simplified R-Vine Copula. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.8b00701] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Nan Zhou
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
| | - Shaojun Li
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Ministry of Education, Shanghai 200237, China
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14
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Ren X, Zhu K, Cai T, Li S. Fault Detection and Diagnosis for Nonlinear and Non-Gaussian Processes Based on Copula Subspace Division. Ind Eng Chem Res 2017. [DOI: 10.1021/acs.iecr.7b02419] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Xiang Ren
- Key Laboratory
of Advanced Control and Optimization for Chemical
Processes, Ministry of Education, and §Department of Mathematics, East China University of Science and Technology, Shanghai, 200237, China
- Department of Chemical and Biochemical Engineering and ∥Department of Environmental
Sciences, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Kunping Zhu
- Key Laboratory
of Advanced Control and Optimization for Chemical
Processes, Ministry of Education, and §Department of Mathematics, East China University of Science and Technology, Shanghai, 200237, China
- Department of Chemical and Biochemical Engineering and ∥Department of Environmental
Sciences, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Ting Cai
- Key Laboratory
of Advanced Control and Optimization for Chemical
Processes, Ministry of Education, and §Department of Mathematics, East China University of Science and Technology, Shanghai, 200237, China
- Department of Chemical and Biochemical Engineering and ∥Department of Environmental
Sciences, Rutgers University, Piscataway, New Jersey 08854, United States
| | - Shaojun Li
- Key Laboratory
of Advanced Control and Optimization for Chemical
Processes, Ministry of Education, and §Department of Mathematics, East China University of Science and Technology, Shanghai, 200237, China
- Department of Chemical and Biochemical Engineering and ∥Department of Environmental
Sciences, Rutgers University, Piscataway, New Jersey 08854, United States
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