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Zhao L, Yang J. Batch Process Monitoring Based on Quality-Related Time-Batch 2D Evolution Information. SENSORS (BASEL, SWITZERLAND) 2022; 22:2235. [PMID: 35336405 PMCID: PMC8954576 DOI: 10.3390/s22062235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/10/2022] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
This paper proposed a quality-related online monitoring strategy based on time and batch two-dimensional evolution information for batch processes. In the direction of time, considering the difference between each phase and the steady part and the transition part in the same phase, the change trend of the regression coefficient of the PLS model is used to divide each batch into phases, and each phase into parts. The phases, the steady parts, and the transition parts are finally distinguished and dealt with separately in the subsequent modeling process. In the batch direction, considering the slow time-varying characteristics of batch evolution, sliding windows are used to perform mode division by analyzing the evolution trend of the score matrix T in the PLS model on the base of phase division and within-phase part division. Finally, an online monitoring model that comprehensively considers the evolution information of time and batch is obtained. In a typical batch operation process, injection molding is used as an example for experimental analysis. The results show that the proposed algorithm takes advantage of mixing the time-batch two-dimensional evolution information. Compared with the traditional methods, the proposed method can overcome the shortcomings caused by the single dimension analysis and has better monitoring results.
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Real-time synchronization with expected distribution of synchronized index for on-line monitoring of uneven multiphase batch process. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107490] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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3
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Backstepping Methodology to Troubleshoot Plant-Wide Batch Processes in Data-Rich Industrial Environments. Processes (Basel) 2021. [DOI: 10.3390/pr9061074] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Troubleshooting batch processes at a plant-wide level requires first finding the unit causing the fault, and then understanding why the fault occurs in that unit. Whereas in the literature case studies discussing the latter issue abound, little attention has been given so far to the former, which is complex for several reasons: the processing units are often operated in a non-sequential way, with unusual series-parallel arrangements; holding vessels may be required to compensate for lack of production capacity, and reacting phenomena can occur in these vessels; and the evidence of batch abnormality may be available only from the end unit and at the end of the production cycle. We propose a structured methodology to assist the troubleshooting of plant-wide batch processes in data-rich environments where multivariate statistical techniques can be exploited. Namely, we first analyze the last unit wherein the fault manifests itself, and we then step back across the units through the process flow diagram (according to the manufacturing recipe) until the fault cannot be detected by the available field sensors any more. That enables us to isolate the unit wherefrom the fault originates. Interrogation of multivariate statistical models for that unit coupled to engineering judgement allow identifying the most likely root cause of the fault. We apply the proposed methodology to troubleshoot a complex industrial batch process that manufactures a specialty chemical, where productivity was originally limited by unexplained variability of the final product quality. Correction of the fault allowed for a significant increase in productivity.
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Gao Z, Jia M, Mao Z, Zhao L. Transitional phase modeling and monitoring with respect to the effect of its neighboring phases. Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2019.03.023] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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5
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Zhao H. Order-Information-Based Phase Partition and Fault Detection for Batch Processes. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.7b03646] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Haitao Zhao
- Automation Department, East China University of Science and Technology, Shanghai 200237, P.R. China
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Multiphase batch process with transitions monitoring based on global preserving statistics slow feature analysis. Neurocomputing 2018. [DOI: 10.1016/j.neucom.2018.02.091] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Liu J, Liu T, Zhang J. Window-Based Stepwise Sequential Phase Partition for Nonlinear Batch Process Monitoring. Ind Eng Chem Res 2016. [DOI: 10.1021/acs.iecr.6b01257] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Jingxiang Liu
- Institute
of Advanced Control Technology, Dalian University of Technology, Dalian, 116024, People’s Republic of China
| | - Tao Liu
- Institute
of Advanced Control Technology, Dalian University of Technology, Dalian, 116024, People’s Republic of China
| | - Jie Zhang
- School
of Chemical Engineering and Advanced Materials, Newcastle University, Newcastle
upon Tyne NE1 7RU, United Kingdom
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8
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Luo L, Bao S, Mao J, Tang D. Phase Partition and Phase-Based Process Monitoring Methods for Multiphase Batch Processes with Uneven Durations. Ind Eng Chem Res 2016. [DOI: 10.1021/acs.iecr.5b03993] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lijia Luo
- College of Mechanical Engineering, Zhejiang University of Technology, Engineering Research Center of Process Equipment and Remanufacturing, Ministry of Education, Hangzhou, China, 310014
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Shiyi Bao
- College of Mechanical Engineering, Zhejiang University of Technology, Engineering Research Center of Process Equipment and Remanufacturing, Ministry of Education, Hangzhou, China, 310014
| | - Jianfeng Mao
- College of Mechanical Engineering, Zhejiang University of Technology, Engineering Research Center of Process Equipment and Remanufacturing, Ministry of Education, Hangzhou, China, 310014
| | - Di Tang
- College of Mechanical Engineering, Zhejiang University of Technology, Engineering Research Center of Process Equipment and Remanufacturing, Ministry of Education, Hangzhou, China, 310014
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Li W, Zhao C, Gao F. Sequential Time Slice Alignment Based Unequal-Length Phase Identification and Modeling for Fault Detection of Irregular Batches. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b01405] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Wenqing Li
- State
Key Laboratory of Industrial Control Technology, Department of Control
Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Chunhui Zhao
- State
Key Laboratory of Industrial Control Technology, Department of Control
Science and Engineering, Zhejiang University, Hangzhou, 310027, China
- Key Laboratory of Advanced Control and Optimization for Chemical Processes, Shanghai, 200237 China
| | - Furong Gao
- Department
of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR
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Yan Z, Huang BL, Yao Y. Multivariate statistical process monitoring of batch-to-batch startups. AIChE J 2015. [DOI: 10.1002/aic.14939] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Zhengbing Yan
- Department of Electrical Engineering and Automation, College of Physics and Electronic Information Engineering; Wenzhou University; Wenzhou 325035 China
| | - Bi-Ling Huang
- Dept. of Chemical Engineering; National Tsing Hua University; Hsinchu 30013 Taiwan
| | - Yuan Yao
- Dept. of Chemical Engineering; National Tsing Hua University; Hsinchu 30013 Taiwan
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Shen F, Ge Z, Song Z. Multivariate Trajectory-Based Local Monitoring Method for Multiphase Batch Processes. Ind Eng Chem Res 2015. [DOI: 10.1021/ie503921t] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Feifan Shen
- State Key
Laboratory of Industrial
Control Technology, Institute of Industrial Process Control, Department
of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, People’s Republic of China
| | - Zhiqiang Ge
- State Key
Laboratory of Industrial
Control Technology, Institute of Industrial Process Control, Department
of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, People’s Republic of China
| | - Zhihuan Song
- State Key
Laboratory of Industrial
Control Technology, Institute of Industrial Process Control, Department
of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, People’s Republic of China
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12
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Zhao L, Zhao C, Gao F. Between-Mode Quality Analysis Based Multimode Batch Process Quality Prediction. Ind Eng Chem Res 2014. [DOI: 10.1021/ie500548a] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Luping Zhao
- Department
of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Chunhui Zhao
- State
Key Laboratory of Industrial Control Technology, Department of Control
Science and Engineering, Zhejiang University, Hangzhou, 310027, China
| | - Furong Gao
- Department
of Chemical and Biomolecular Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
- Fok
Ying Tung Graduate School, The Hong Kong University of Science and Technology, Hong Kong, China
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Affiliation(s)
- Lijia Luo
- College
of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Shiyi Bao
- College
of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Zengliang Gao
- College
of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Jingqi Yuan
- Department
of Automation, Shanghai Jiao Tong University, Shanghai, China
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