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Ali H, Maulud AS, Zabiri H, Nawaz M, Suleman H, Taqvi SAA. Multiscale Principal Component Analysis-Signed Directed Graph Based Process Monitoring and Fault Diagnosis. ACS OMEGA 2022; 7:9496-9512. [PMID: 35350317 PMCID: PMC8945140 DOI: 10.1021/acsomega.1c06839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/10/2022] [Indexed: 06/14/2023]
Abstract
The chemical process industry has become the backbone of the global economy. The complexities of chemical process systems have been increased in the last two decades due to online sensor technology, plant-wide automation, and computerized measurement devices. Principal component analysis (PCA) and signed directed graph (SDG) are some of the quantitative and qualitative monitoring techniques that have been widely applied for chemical fault detection and diagnosis (FDD). The conventional PCA-SDG algorithm is a single-scale FDD representation origin, which cannot effectively solve multiple FDD representation origins. The multiscale PCA-SDG wavelet-based monitoring technique has potential because it easily distinguishes between deterministic and stochastic characteristics. This study uses multiscale PCA-SDG to detect, diagnose the root cause and identify the fault propagation path. The proposed method is applied to a continuous stirred tank reactor system to validate its effectiveness. The propagation route of most process failures is detected, identified, and diagnosed, which is well-aligned with the fault description, demonstrating a satisfactory performance of the suggested technique for monitoring the process failures.
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Affiliation(s)
- Husnain Ali
- Chemical
Engineering Department, Universiti Teknologi
PETRONAS, Bandar
Seri Iskandar, Perak Darul Ridzuan 32610, Malaysia
| | - Abdulhalim Shah Maulud
- Chemical
Engineering Department, Universiti Teknologi
PETRONAS, Bandar
Seri Iskandar, Perak Darul Ridzuan 32610, Malaysia
- Centre
of Contaminant Control and Utilisation (CenCoU), Universiti Teknologi PETRONAS, Bandar Seri Iskandar, Perak Darul Ridzuan 32610, Malaysia
| | - Haslinda Zabiri
- Chemical
Engineering Department, Universiti Teknologi
PETRONAS, Bandar
Seri Iskandar, Perak Darul Ridzuan 32610, Malaysia
| | - Muhammad Nawaz
- Chemical
Engineering Department, Universiti Teknologi
PETRONAS, Bandar
Seri Iskandar, Perak Darul Ridzuan 32610, Malaysia
| | - Humbul Suleman
- School
of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough TS1 3BX, United Kingdom
| | - Syed Ali Ammar Taqvi
- Department
of Chemical Engineering, NED University
of Engineering & Technology, Karachi 75270, Pakistan
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2
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Hassanpour H, Corbett B, Mhaskar P. Artificial Neural Network-Based Model Predictive Control Using Correlated Data. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04339] [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)
- Hesam Hassanpour
- Department of Chemical Engineering, McMaster University, Hamilton, Ontario L8S 4L7, Canada
| | - Brandon Corbett
- Department of Chemical Engineering, McMaster University, Hamilton, Ontario L8S 4L7, Canada
| | - Prashant Mhaskar
- Department of Chemical Engineering, McMaster University, Hamilton, Ontario L8S 4L7, Canada
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3
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Chen B, Luo XL, Wan X. The abnormal situation with reversal characteristic in chemical processes: Local monitoring and self-recovery. J Taiwan Inst Chem Eng 2021. [DOI: 10.1016/j.jtice.2021.09.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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4
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Hassanpour H, Corbett B, Mhaskar P. Artificial neural network based model predictive control: Implementing achievable set‐points. AIChE J 2021. [DOI: 10.1002/aic.17436] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Hesam Hassanpour
- Department of Chemical Engineering McMaster University Hamilton Ontario Canada
| | - Brandon Corbett
- Department of Chemical Engineering McMaster University Hamilton Ontario Canada
| | - Prashant Mhaskar
- Department of Chemical Engineering McMaster University Hamilton Ontario Canada
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5
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Yellapu VS, Zhang W, Vajpayee V, Xu X. A multiscale data reconciliation approach for sensor fault detection. PROGRESS IN NUCLEAR ENERGY 2021. [DOI: 10.1016/j.pnucene.2021.103707] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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6
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Improved process monitoring using the CUSUM and EWMA-based multiscale PCA fault detection framework. Chin J Chem Eng 2021. [DOI: 10.1016/j.cjche.2020.08.035] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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7
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Nawaz M, Maulud AS, Zabiri H, Suleman H, Tufa LD. Multiscale Framework for Real-Time Process Monitoring of Nonlinear Chemical Process Systems. Ind Eng Chem Res 2020. [DOI: 10.1021/acs.iecr.0c02288] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Muhammad Nawaz
- Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia
| | - Abdulhalim Shah Maulud
- Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia
- Centre of Contaminant Control & Utilization (CenCoU), Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia
| | - Haslinda Zabiri
- Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Bandar Seri Iskandar, Perak, Malaysia
| | - Humbul Suleman
- School of Computing, Engineering and Digital Technologies, Teesside University, TS1 3BX Middlesbrough, U. K
| | - Lemma Dendena Tufa
- School of Chemical and Bioengineering, Addis Ababa Institute of Technology, Addis Ababa University, King George VI St Addis Ababa 1000 Addis Ababa, Ethiopia
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8
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Multichannel one-dimensional convolutional neural network-based feature learning for fault diagnosis of industrial processes. Neural Comput Appl 2020. [DOI: 10.1007/s00521-020-05171-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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9
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Visualization analysis for fault diagnosis in chemical processes using recurrent neural networks. J Taiwan Inst Chem Eng 2020. [DOI: 10.1016/j.jtice.2020.06.016] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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10
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Abstract
As Industry 4.0 makes its course into the Chemical Processing Industry (CPI), new challenges emerge that require an adaptation of the Process Analytics toolkit. In particular, two recurring classes of problems arise, motivated by the growing complexity of systems on one hand, and increasing data throughput (i.e., the product of two well-known “V’s” from Big Data: Volume × Velocity) on the other. More specifically, as enabling IT technologies (IoT, smart sensors, etc.) enlarge the focus of analysis from the unit level to the entire plant or even to the supply chain level, the existence of relevant dynamics at multiple scales becomes a common pattern; therefore, multiscale methods are called for and must be applied in order to avoid biased analysis towards a certain scale, compromising the benefits from the balanced exploitation of the information content at all scales. Also, these same enabling technologies currently collect large volumes of data at high-sampling rates, creating a flood of digital information that needs to be properly handled; optimal data aggregation provides an efficient solution to this challenge, leading to the emergence of multi-granularity frameworks. In this article, an overview is presented on multiscale and multi-granularity methods that are likely to play an important role in the future of Process Analytics with respect to several common activities, such as data integration/fusion, de-noising, process monitoring and predictive modelling, among others.
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11
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Ahmed U, Ha D, Shin S, Shaukat N, Zahid U, Han C. Estimation of Disturbance Propagation Path Using Principal Component Analysis (PCA) and Multivariate Granger Causality (MVGC) Techniques. Ind Eng Chem Res 2017. [DOI: 10.1021/acs.iecr.6b02763] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Usama Ahmed
- School
of Chemical and Biological Engineering, Seoul National University, Seoul 151-744, Republic of Korea
| | - Daegeun Ha
- School
of Chemical and Biological Engineering, Seoul National University, Seoul 151-744, Republic of Korea
| | - Seolin Shin
- School
of Chemical and Biological Engineering, Seoul National University, Seoul 151-744, Republic of Korea
| | - Nadeem Shaukat
- Department
of Nuclear Engineering, Seoul National University, Seoul 151-744, Republic of Korea
| | - Umer Zahid
- Chemical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran, Saudi Arabia
| | - Chonghun Han
- School
of Chemical and Biological Engineering, Seoul National University, Seoul 151-744, Republic of Korea
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12
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Tong C, Shi X, Lan T. Statistical process monitoring based on orthogonal multi-manifold projections and a novel variable contribution analysis. ISA TRANSACTIONS 2016; 65:407-417. [PMID: 27435000 DOI: 10.1016/j.isatra.2016.06.017] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/27/2015] [Revised: 06/02/2016] [Accepted: 06/30/2016] [Indexed: 06/06/2023]
Abstract
Multivariate statistical methods have been widely applied to develop data-based process monitoring models. Recently, a multi-manifold projections (MMP) algorithm was proposed for modeling and monitoring chemical industrial processes, the MMP is an effective tool for preserving the global and local geometric structure of the original data space in the reduced feature subspace, but it does not provide orthogonal basis functions for data reconstruction. Recognition of this issue, an improved version of MMP algorithm named orthogonal MMP (OMMP) is formulated. Based on the OMMP model, a further processing step and a different monitoring index are proposed to model and monitor the variation in the residual subspace. Additionally, a novel variable contribution analysis is presented for fault diagnosis by integrating the nearest in-control neighbor calculation and reconstruction-based contribution analysis. The validity and superiority of the proposed fault detection and diagnosis strategy are then validated through case studies on the Tennessee Eastman benchmark process.
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Affiliation(s)
- Chudong Tong
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, P.R. China.
| | - Xuhua Shi
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, P.R. China
| | - Ting Lan
- Faculty of Electrical Engineering & Computer Science, Ningbo University, Ningbo 315211, P.R. China
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13
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Krishnannair S, Aldrich C, Jemwa G. Detecting faults in process systems with singular spectrum analysis. Chem Eng Res Des 2016. [DOI: 10.1016/j.cherd.2016.07.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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14
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Online process monitoring for complex systems with dynamic weighted principal component analysis. Chin J Chem Eng 2016. [DOI: 10.1016/j.cjche.2016.05.038] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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15
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Li S, Zhou X, Shi H, Qiao Z, Zheng Z. Monitoring of Multimode Processes Based on Subspace Decomposition. Ind Eng Chem Res 2015. [DOI: 10.1021/ie504730x] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | | | | | - Zhi Qiao
- NUS
Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore, 117456, Republic of Singapore
- Department
of Physics and Centre for Computational Science and Engineering, National University of Singapore, Singapore, 117542, Republic of Singapore
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16
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Luo L, Bao S, Gao Z, Yuan J. Tensor Global-Local Preserving Projections for Batch Process Monitoring. Ind Eng Chem Res 2014. [DOI: 10.1021/ie403973w] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Lijia Luo
- College
of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Shiyi Bao
- College
of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Zengliang Gao
- College
of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Jingqi Yuan
- Department
of Automation, Shanghai Jiao Tong University, Shanghai, China
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17
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Alawi A, Zhang J, Morris J. Multiscale Multiblock Batch Monitoring: Sensor and Process Drift and Degradation. Org Process Res Dev 2014. [DOI: 10.1021/op400337x] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Affiliation(s)
- Ahmed Alawi
- School
of Chemical Engineering and Advanced Materials and ‡Centre for Process
Analytics and Control Technology, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K
| | - Jie Zhang
- School
of Chemical Engineering and Advanced Materials and ‡Centre for Process
Analytics and Control Technology, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K
| | - Julian Morris
- School
of Chemical Engineering and Advanced Materials and ‡Centre for Process
Analytics and Control Technology, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K
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18
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Luo L, Bao S, Gao Z, Yuan J. Batch Process Monitoring with Tensor Global–Local Structure Analysis. Ind Eng Chem Res 2013. [DOI: 10.1021/ie402355f] [Citation(s) in RCA: 32] [Impact Index Per Article: 2.9] [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, Hangzhou 310014, China
| | - Shiyi Bao
- College
of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Zengliang Gao
- College
of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310014, China
| | - Jingqi Yuan
- Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
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19
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Stubbs S, Zhang J, Morris J. Multiway Interval Partial Least Squares for Batch Process Performance Monitoring. Ind Eng Chem Res 2013. [DOI: 10.1021/ie303562t] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Shallon Stubbs
- Centre for Process
Analytics and Control Technology, School of Chemical Engineering and
Advanced Materials, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K
| | - Jie Zhang
- Centre for Process
Analytics and Control Technology, School of Chemical Engineering and
Advanced Materials, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K
| | - Julian Morris
- Centre for Process
Analytics and Control Technology, School of Chemical Engineering and
Advanced Materials, Newcastle University, Newcastle upon Tyne NE1 7RU, U.K
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20
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Ko YD, Shang H. SAG mill system diagnosis using multivariate process variable analysis. CAN J CHEM ENG 2011. [DOI: 10.1002/cjce.20487] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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21
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Zhang Y, Ma C. Fault diagnosis of nonlinear processes using multiscale KPCA and multiscale KPLS. Chem Eng Sci 2011. [DOI: 10.1016/j.ces.2010.10.008] [Citation(s) in RCA: 92] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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22
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Ren S, Gao L. Improvement of the prediction ability of multivariate calibration by a method based on the combination of data fusion and least squares support vector machines. Analyst 2011; 136:1252-61. [DOI: 10.1039/c0an00433b] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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23
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Gao L, Ren S. Multivariate calibration of spectrophotometric data using a partial least squares with data fusion. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2010; 76:363-368. [PMID: 20434392 DOI: 10.1016/j.saa.2010.03.024] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2009] [Revised: 03/09/2010] [Accepted: 03/16/2010] [Indexed: 05/29/2023]
Abstract
A novel method named DF-PLS based on partial least squares (PLS) regression combined with data fusion (DF) was applied to enhance the ability of extracting characteristic information and the quality of regression for the simultaneous spectrophotometric determination of Cu(II), Ni(II) and Cr(III). Data fusion is a technique that seamlessly integrates information from disparate sources to produce a single model or decision. Wavelet representations of signals provide a local time-frequency description and are multiscale in nature, thus in the wavelet domain, the quality of noise removal is implemented by a scale-dependent threshold method. Information from different wavelet scales is just like different sources of information. Integrating the information from different wavelet scales to obtain a PLS model belongs to the technique of data fusion. PLS was applied for multivariate calibration and noise reduction by eliminating the less important latent variables. In this case, by optimization, wavelet functions, decomposition level and thresholding methods and the number of PLS factors for DF-PLS were selected as Daubechies 4, 7, HYBRID thresholding and 3, respectively. The relative standard errors of prediction (RSEP) for all compounds with DF-PLS and PLS were 3.13% and 10.3%, respectively. Experimental results showed the DF-PLS method to be successful for simultaneous multicomponent determination even when severe overlap of spectra was present and proved it to be better than PLS. The DF-PLS method is a hybrid technique that combines the best attributes of DF and PLS, which makes it a promising and attractive method.
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Affiliation(s)
- Ling Gao
- Department of Chemistry, Inner Mongolia University, West University Road 235, Huhehot, 010021 Inner Mongolia, PR China
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24
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An embedded fault detection, isolation and accommodation system in a model predictive controller for an industrial benchmark process. Comput Chem Eng 2008. [DOI: 10.1016/j.compchemeng.2008.03.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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26
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27
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Chen J, Hsu CJ, Jiang YC, Ming-Wei Lee. Self-Growing Hidden Markov Tree Based Multiway Principle Component Analysis for Enhanced Monitoring of Batch Processes. Ind Eng Chem Res 2007. [DOI: 10.1021/ie0608298] [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)
- Junghui Chen
- R&D Center for Membrane Technology, Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li, Taiwan 32023, Republic of China
| | - Chia-Jung Hsu
- R&D Center for Membrane Technology, Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li, Taiwan 32023, Republic of China
| | - Yan-Cheng Jiang
- R&D Center for Membrane Technology, Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li, Taiwan 32023, Republic of China
| | - Ming-Wei Lee
- Industrial Technology Research Institute, Energy and Environment Research Laboratories, Hsinchu, Taiwan 310, Republic of China
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28
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Chang H, Chen J, Ho YP. Batch Process Monitoring by Wavelet Transform Based Fractal Encoding. Ind Eng Chem Res 2006. [DOI: 10.1021/ie050856i] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Hsuan Chang
- Department of Chemical and Materials Engineering, Tamkang University, 151 Ying-Chuan Road, Tamsui, Taipei, Taiwan 25137, Republic of China
| | - Junghui Chen
- R&D Center for Membrane Technology, Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li, Taiwan 320, Republic of China
| | - Yun-Peng Ho
- R&D Center for Membrane Technology, Department of Chemical Engineering, Chung-Yuan Christian University, Chung-Li, Taiwan 320, Republic of China
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30
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Xie L, Kruger U, Lieftucht D, Littler T, Chen Q, Wang SQ. Statistical Monitoring of Dynamic Multivariate Processes Part 1. Modeling Autocorrelation and Cross-correlation. Ind Eng Chem Res 2006. [DOI: 10.1021/ie050583r] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lei Xie
- Institute of Advanced Process Control, National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, P.R. China, Intelligent Systems and Control Research Group, Queen's University Belfast, BT9 5AH, U.K., Energy Systems Research Group, Queen's University Belfast, BT9 5AH, U.K., College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R. China, and Department of Process Dynamics and Operation, Technical University of Berlin,
| | - Uwe Kruger
- Institute of Advanced Process Control, National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, P.R. China, Intelligent Systems and Control Research Group, Queen's University Belfast, BT9 5AH, U.K., Energy Systems Research Group, Queen's University Belfast, BT9 5AH, U.K., College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R. China, and Department of Process Dynamics and Operation, Technical University of Berlin,
| | - Dirk Lieftucht
- Institute of Advanced Process Control, National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, P.R. China, Intelligent Systems and Control Research Group, Queen's University Belfast, BT9 5AH, U.K., Energy Systems Research Group, Queen's University Belfast, BT9 5AH, U.K., College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R. China, and Department of Process Dynamics and Operation, Technical University of Berlin,
| | - Tim Littler
- Institute of Advanced Process Control, National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, P.R. China, Intelligent Systems and Control Research Group, Queen's University Belfast, BT9 5AH, U.K., Energy Systems Research Group, Queen's University Belfast, BT9 5AH, U.K., College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R. China, and Department of Process Dynamics and Operation, Technical University of Berlin,
| | - Qian Chen
- Institute of Advanced Process Control, National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, P.R. China, Intelligent Systems and Control Research Group, Queen's University Belfast, BT9 5AH, U.K., Energy Systems Research Group, Queen's University Belfast, BT9 5AH, U.K., College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R. China, and Department of Process Dynamics and Operation, Technical University of Berlin,
| | - Shu-Qing Wang
- Institute of Advanced Process Control, National Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, P.R. China, Intelligent Systems and Control Research Group, Queen's University Belfast, BT9 5AH, U.K., Energy Systems Research Group, Queen's University Belfast, BT9 5AH, U.K., College of Aerospace Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P.R. China, and Department of Process Dynamics and Operation, Technical University of Berlin,
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