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S. VV, Mohanta HK, Pani AK. Adaptive non-linear soft sensor for quality monitoring in refineries using Just-in-Time Learning—Generalized regression neural network approach. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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2
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Qi L, Liu H, Xiong Q, Chen Z. Just-in-time-learning based prediction model of BOF endpoint carbon content and temperature via vMF mixture model and weighted extreme learning machine. Comput Chem Eng 2021. [DOI: 10.1016/j.compchemeng.2021.107488] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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3
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Shao W, Ge Z, Song Z. Semisupervised Bayesian Gaussian Mixture Models for Non-Gaussian Soft Sensor. IEEE TRANSACTIONS ON CYBERNETICS 2021; 51:3455-3468. [PMID: 31722504 DOI: 10.1109/tcyb.2019.2947622] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Soft sensors have been widely accepted for online estimating key quality-related variables in industrial processes. The Gaussian mixture models (GMM) is one of the most popular soft sensing methods for the non-Gaussian industrial processes. However, in industrial applications, the quantity of samples with known labels is usually quite limited because of the technical limitations or economical reasons. Traditional GMM-based soft sensor models solely depending on labeled samples may easily suffer from singular covariances, overfitting, and difficulties in model selection, which results in the performance deterioration. To tackle these issues, we propose a semisupervised Bayesian GMM (S2BGMM). In the S2BGMM, we first propose a semisupervised fully Bayesian model, which enables learning from both the labeled and unlabeled datasets for remedying the deficiency of infrequent labeled samples. Subsequently, a general framework of weighted variational inference is developed to train the S2BGMM, such that the rate of learning from unlabeled samples can be controlled by penalizing the unlabeled dataset. Case studies are carried out to evaluate the performance of the S2BGMM through a numerical example and two real-world industrial processes, which demonstrate the effectiveness and reliability of the proposed approach.
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4
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Weighted similarity based just-in-time model predictive control for batch trajectory tracking. Chem Eng Res Des 2020. [DOI: 10.1016/j.cherd.2020.07.028] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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5
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Integrating adaptive moving window and just-in-time learning paradigms for soft-sensor design. Neurocomputing 2020. [DOI: 10.1016/j.neucom.2020.01.083] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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6
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Guo J, Du W, Nascu I. Adaptive Modeling of Fixed-Bed Reactors with Multicycle and Multimode Characteristics Based on Transfer Learning and Just-In-Time Learning. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.9b06668] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jingjing Guo
- Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Wenli Du
- Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
| | - Ioana Nascu
- Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
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7
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Yeo WS, Saptoro A, Kumar P. Missing data treatment for locally weighted partial least square‐based modelling: A comparative study. ASIA-PAC J CHEM ENG 2020. [DOI: 10.1002/apj.2422] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Wan Sieng Yeo
- Department of Chemical EngineeringCurtin University Malaysia Miri Malaysia
| | - Agus Saptoro
- Department of Chemical EngineeringCurtin University Malaysia Miri Malaysia
| | - Perumal Kumar
- Department of Chemical EngineeringCurtin University Malaysia Miri Malaysia
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8
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Wang J, Shao W, Song Z. Robust inferential sensor development based on variational Bayesian Student’s-t mixture regression. Neurocomputing 2019. [DOI: 10.1016/j.neucom.2019.08.039] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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9
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Yeo WS, Saptoro A, Kumar P. Adaptive Soft Sensor Development for Non-Gaussian and Nonlinear Processes. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b03821] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Wan Sieng Yeo
- Chemical Engineering Department, Curtin University Malaysia, CDT 250, Miri 98009, Sarawak, Malaysia
| | - Agus Saptoro
- Chemical Engineering Department, Curtin University Malaysia, CDT 250, Miri 98009, Sarawak, Malaysia
| | - Perumal Kumar
- Chemical Engineering Department, Curtin University Malaysia, CDT 250, Miri 98009, Sarawak, Malaysia
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Pan B, Jin H, Yang B, Qian B, Zhao Z. Soft Sensor Development for Nonlinear Industrial Processes Based on Ensemble Just-in-Time Extreme Learning Machine through Triple-Modal Perturbation and Evolutionary Multiobjective Optimization. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b03702] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Bei Pan
- Department of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
- Department of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Huaiping Jin
- Department of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
| | - Biao Yang
- Department of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
| | - Bin Qian
- Department of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
| | - Zhengang Zhao
- Department of Automation, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
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Pan B, Jin H, Wang L, Qian B, Chen X, Huang S, Li J. Just-in-time learning based soft sensor with variable selection and weighting optimized by evolutionary optimization for quality prediction of nonlinear processes. Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2019.02.004] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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12
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Performance monitoring of non-gaussian chemical processes with modes-switching using globality-locality preserving projection. Front Chem Sci Eng 2017. [DOI: 10.1007/s11705-017-1675-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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13
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Kaneko H, Funatsu K. Smoothing-Combined Soft Sensors for Noise Reduction and Improvement of Predictive Ability. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b03054] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Hiromasa Kaneko
- Department
of Chemical System
Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan
| | - Kimito Funatsu
- Department
of Chemical System
Engineering, The University of Tokyo, Hongo 7-3-1, Bunkyo-ku, Tokyo 113-8656, Japan
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14
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Kaneko H, Funatsu K. Ensemble locally weighted partial least squares as a just‐in‐time modeling method. AIChE J 2015. [DOI: 10.1002/aic.15090] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Hiromasa Kaneko
- Dept. of Chemical System EngineeringUniversity of TokyoHongo 7‐3‐1Bunkyo‐ku Tokyo113‐8656 Japan
| | - Kimito Funatsu
- Dept. of Chemical System EngineeringUniversity of TokyoHongo 7‐3‐1Bunkyo‐ku Tokyo113‐8656 Japan
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15
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Zhang X, Li Y, Kano M. Quality Prediction in Complex Batch Processes with Just-in-Time Learning Model Based on Non-Gaussian Dissimilarity Measure. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b01425] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Xinmin Zhang
- Information
Engineering School, Shenyang University of Chemical Technology, ShenYang 110142, P. R. China
| | - Yuan Li
- Information
Engineering School, Shenyang University of Chemical Technology, ShenYang 110142, P. R. China
| | - Manabu Kano
- Department
of Systems Science, Kyoto University, Yoshida-Honmachi, Sakyo-ku, Kyoto 606-8501, Japan
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Jin H, Chen X, Wang L, Yang K, Wu L. Adaptive Soft Sensor Development Based on Online Ensemble Gaussian Process Regression for Nonlinear Time-Varying Batch Processes. Ind Eng Chem Res 2015. [DOI: 10.1021/acs.iecr.5b01495] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Huaiping Jin
- Department of Chemical Engineering, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
| | - Xiangguang Chen
- Department of Chemical Engineering, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
| | - Li Wang
- Department of Chemical Engineering, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
| | - Kai Yang
- Department of Chemical Engineering, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
| | - Lei Wu
- Department of Chemical Engineering, Beijing Institute of Technology, Beijing 100081, People’s Republic of China
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