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Batch-to-batch steady state identification based on variable correlation and mahalanobis distance / Yuan Yao in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11060–11070 Titre : Batch-to-batch steady state identification based on variable correlation and mahalanobis distance Type de document : texte imprimé Auteurs : Yuan Yao, Auteur ; Zhao, Chunhui, Auteur ; Gao, Furong, Auteur Année de publication : 2010 Article en page(s) : pp. 11060–11070 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Batch-to-Batch--Steady  State--Identification--Variable--Correlation--Mahalanobis--Distance Résumé : Online steady state identification (SSID) is an important task to ensure the quality consistence of final products in batch processes. Additionally, it is also critical for satisfactory control of many batch processes. The existing approach for batch process SSID is based on the multiway principal component analysis (MPCA) method, which requires history data from dozens of batches for process modeling. Consequently, this limits its online applications. In this work, principal component analysis (PCA) models are built for each batch from which the variable correlation information is extracted. The changes in variable correlation structures are then quantified with a PCA similarity factor. At the same time, the Mahalanobis distances between batch trajectories are also calculated to indicate the changes in variable trajectory magnitudes. These two types of information are then used for online SSID in batch processes. This method is more suitable for online applications and can solve the problems of uneven operation durations. Additionally, it can be easily extended to deal with non-Gaussian information and multiphase batch process characteristics. Application examples show the effectiveness of the proposed method. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901107h [article] Batch-to-batch steady state identification based on variable correlation and mahalanobis distance [texte imprimé] / Yuan Yao, Auteur ; Zhao, Chunhui, Auteur ; Gao, Furong, Auteur . - 2010 . - pp. 11060–11070. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11060–11070 Mots-clés : Batch-to-Batch--Steady  State--Identification--Variable--Correlation--Mahalanobis--Distance Résumé : Online steady state identification (SSID) is an important task to ensure the quality consistence of final products in batch processes. Additionally, it is also critical for satisfactory control of many batch processes. The existing approach for batch process SSID is based on the multiway principal component analysis (MPCA) method, which requires history data from dozens of batches for process modeling. Consequently, this limits its online applications. In this work, principal component analysis (PCA) models are built for each batch from which the variable correlation information is extracted. The changes in variable correlation structures are then quantified with a PCA similarity factor. At the same time, the Mahalanobis distances between batch trajectories are also calculated to indicate the changes in variable trajectory magnitudes. These two types of information are then used for online SSID in batch processes. This method is more suitable for online applications and can solve the problems of uneven operation durations. Additionally, it can be easily extended to deal with non-Gaussian information and multiphase batch process characteristics. Application examples show the effectiveness of the proposed method. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901107h

Batch-to-batch steady state identification based on variable correlation and mahalanobis distance / Yuan Yao in Industrial & engineering chemistry research, Vol. 48 N° 24 (Décembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11060–11070 Titre : Batch-to-batch steady state identification based on variable correlation and mahalanobis distance Type de document : texte imprimé Auteurs : Yuan Yao, Auteur ; Zhao, Chunhui, Auteur ; Gao, Furong, Auteur Année de publication : 2010 Article en page(s) : pp. 11060–11070 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Online  steady  state  identification  Multiway  principal  component  analysis Résumé : Online steady state identification (SSID) is an important task to ensure the quality consistence of final products in batch processes. Additionally, it is also critical for satisfactory control of many batch processes. The existing approach for batch process SSID is based on the multiway principal component analysis (MPCA) method, which requires history data from dozens of batches for process modeling. Consequently, this limits its online applications. In this work, principal component analysis (PCA) models are built for each batch from which the variable correlation information is extracted. The changes in variable correlation structures are then quantified with a PCA similarity factor. At the same time, the Mahalanobis distances between batch trajectories are also calculated to indicate the changes in variable trajectory magnitudes. These two types of information are then used for online SSID in batch processes. This method is more suitable for online applications and can solve the problems of uneven operation durations. Additionally, it can be easily extended to deal with non-Gaussian information and multiphase batch process characteristics. Application examples show the effectiveness of the proposed method. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901107h [article] Batch-to-batch steady state identification based on variable correlation and mahalanobis distance [texte imprimé] / Yuan Yao, Auteur ; Zhao, Chunhui, Auteur ; Gao, Furong, Auteur . - 2010 . - pp. 11060–11070. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 24 (Décembre 2009) . - pp. 11060–11070 Mots-clés : Online  steady  state  identification  Multiway  principal  component  analysis Résumé : Online steady state identification (SSID) is an important task to ensure the quality consistence of final products in batch processes. Additionally, it is also critical for satisfactory control of many batch processes. The existing approach for batch process SSID is based on the multiway principal component analysis (MPCA) method, which requires history data from dozens of batches for process modeling. Consequently, this limits its online applications. In this work, principal component analysis (PCA) models are built for each batch from which the variable correlation information is extracted. The changes in variable correlation structures are then quantified with a PCA similarity factor. At the same time, the Mahalanobis distances between batch trajectories are also calculated to indicate the changes in variable trajectory magnitudes. These two types of information are then used for online SSID in batch processes. This method is more suitable for online applications and can solve the problems of uneven operation durations. Additionally, it can be easily extended to deal with non-Gaussian information and multiphase batch process characteristics. Application examples show the effectiveness of the proposed method. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901107h

Enhanced process comprehension and statistical analysis for slow-varying batch processes / Zhao, Chunhui ; Wang, Fuli ; Gao, Furong in Industrial & engineering chemistry research, Vol. 47 n°24 (Décembre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 n°24 (Décembre 2008) . - p. 9996–10008 Titre : Enhanced process comprehension and statistical analysis for slow-varying batch processes Type de document : texte imprimé Auteurs : Zhao, Chunhui, Auteur ; Wang, Fuli, Auteur ; Gao, Furong, Auteur Année de publication : 2009 Article en page(s) : p. 9996–10008 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Statistical  analysis Résumé : Under the influence of various exterior factors, batch processes commonly involve normal slow variations over batches, in which the changing underlying behaviors make their modeling and monitoring a greater challenge. Having realized the problems associated with the commonly adopted adaptive methods, in the present work, our biggest concern is how to minimize the efforts for long-term model updating adjustment and simultaneously maintain their validity as permanently as possible once the initial models are built. It is implemented from the viewpoint of between-batch relative changes, which are regular with process evolution and conform to certain evolving rule and statistical characteristics. First, difference subspace is constructed by calculating the between-batch difference trajectories, which represent the batchwise relative changes resulting from the slow-varying behaviors. Then their variability along batch direction is addressed and analyzed in the difference subspace using ICA-PCA two-step feature extraction, which reveals the evolving rule and statistical characteristics of slow variations. In this way, the mode of normal slow changes is extracted, trained, and modeled, which, thus, endows the initial monitoring system with adaptive competency to slow-varying behaviors. Therefore, it is less sensitive to normal slow variations, which eases the excessive dependency of monitoring performance on updating and, thus, decreases the risk of false adaptation to process disturbances. Despite the simplicity of the proposed idea and algorithm, the performance it achieves in the case studies indicates that it is smart and competitive as a feasible solution to analyze and monitor the regular slow-varying characteristics. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800643d [article] Enhanced process comprehension and statistical analysis for slow-varying batch processes [texte imprimé] / Zhao, Chunhui, Auteur ; Wang, Fuli, Auteur ; Gao, Furong, Auteur . - 2009 . - p. 9996–10008. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°24 (Décembre 2008) . - p. 9996–10008 Mots-clés : Statistical  analysis Résumé : Under the influence of various exterior factors, batch processes commonly involve normal slow variations over batches, in which the changing underlying behaviors make their modeling and monitoring a greater challenge. Having realized the problems associated with the commonly adopted adaptive methods, in the present work, our biggest concern is how to minimize the efforts for long-term model updating adjustment and simultaneously maintain their validity as permanently as possible once the initial models are built. It is implemented from the viewpoint of between-batch relative changes, which are regular with process evolution and conform to certain evolving rule and statistical characteristics. First, difference subspace is constructed by calculating the between-batch difference trajectories, which represent the batchwise relative changes resulting from the slow-varying behaviors. Then their variability along batch direction is addressed and analyzed in the difference subspace using ICA-PCA two-step feature extraction, which reveals the evolving rule and statistical characteristics of slow variations. In this way, the mode of normal slow changes is extracted, trained, and modeled, which, thus, endows the initial monitoring system with adaptive competency to slow-varying behaviors. Therefore, it is less sensitive to normal slow variations, which eases the excessive dependency of monitoring performance on updating and, thus, decreases the risk of false adaptation to process disturbances. Despite the simplicity of the proposed idea and algorithm, the performance it achieves in the case studies indicates that it is smart and competitive as a feasible solution to analyze and monitor the regular slow-varying characteristics. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800643d

Multiblock-based qualitative and quantitative spectral calibration analysis / Zhao, Chunhui in Industrial & engineering chemistry research, Vol. 49 N° 18 (Septembre 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 18 (Septembre 2010) . - pp. 8694–8704 Titre : Multiblock-based qualitative and quantitative spectral calibration analysis Type de document : texte imprimé Auteurs : Zhao, Chunhui, Auteur ; Gao, Furong, Auteur Année de publication : 2010 Article en page(s) : pp. 8694–8704 Note générale : Industrial chemistry Langues : Anglais (eng) Résumé : In this article, an improved spectral calibration and statistical analysis approach is proposed. Having realized the multiplicity of underlying spectral characteristics and their different effects on the quality of interpretation and prediction, the major task lies in how to qualify and quantify the descriptor−quality relationship more meaningfully over different wavelength subspaces. The underlying spectral information can be explored more comprehensively by a spectral subspace separation and multiblock modeling strategy. Unlike the specific purpose of quality predictions over different spectral subspaces, systematic information in both descriptor and quality spaces is decomposed into different parts under the control of different between-set relationships. Closely related variations and irrelevant ones are discriminated and evaluated separately, where, in particular, the orthogonal variations, as important systematic information, are quantified in specific model parameters, even though they are not predictive. In this way, the approach well tracks the wavelength-varying underlying characteristics, takes full advantage of underlying variations in both X and Y spaces, and allows for a more meaningful interpretation of the between-set relationship. This article theoretically and experimentally illustrates the efficiency of the proposed method in qualitative and quantitative spectral calibration analysis. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100892y [article] Multiblock-based qualitative and quantitative spectral calibration analysis [texte imprimé] / Zhao, Chunhui, Auteur ; Gao, Furong, Auteur . - 2010 . - pp. 8694–8704. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 18 (Septembre 2010) . - pp. 8694–8704 Résumé : In this article, an improved spectral calibration and statistical analysis approach is proposed. Having realized the multiplicity of underlying spectral characteristics and their different effects on the quality of interpretation and prediction, the major task lies in how to qualify and quantify the descriptor−quality relationship more meaningfully over different wavelength subspaces. The underlying spectral information can be explored more comprehensively by a spectral subspace separation and multiblock modeling strategy. Unlike the specific purpose of quality predictions over different spectral subspaces, systematic information in both descriptor and quality spaces is decomposed into different parts under the control of different between-set relationships. Closely related variations and irrelevant ones are discriminated and evaluated separately, where, in particular, the orthogonal variations, as important systematic information, are quantified in specific model parameters, even though they are not predictive. In this way, the approach well tracks the wavelength-varying underlying characteristics, takes full advantage of underlying variations in both X and Y spaces, and allows for a more meaningful interpretation of the between-set relationship. This article theoretically and experimentally illustrates the efficiency of the proposed method in qualitative and quantitative spectral calibration analysis. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100892y

A Multiple - time - region (MTR) - based fault subspace decomposition and reconstruction modeling strategy for online fault diagnosis / Zhao, Chunhui in Industrial & engineering chemistry research, Vol. 51 N° 34 (Août 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 34 (Août 2012) . - pp. 11207–11217 Titre : A Multiple - time - region (MTR) - based fault subspace decomposition and reconstruction modeling strategy for online fault diagnosis Type de document : texte imprimé Auteurs : Zhao, Chunhui, Auteur ; Youxian Sun, Auteur ; Gao, Furong, Auteur Année de publication : 2012 Article en page(s) : pp. 11207–11217 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Fault  diagnosis Résumé : Time-varying fault characteristics have not yet been addressed by conventional fault-reconstruction-based modeling methods, which could affect fault diagnosis performance. In the present work, the multiple-time-region (MTR) nature, that is, the multiplicity of fault characteristics along with the process evolution, is proposed and efficiently analyzed for fault diagnosis. First, an automatic time-region-division algorithm is developed that can partition the whole fault process into different local regions according to the changes in fault characteristics. Different local fault characteristics are thus analyzed by building different representative fault feature models in multiple time regions. Following the changing relationships between the fault and normal operation statuses, different fault reconstruction actions are finally taken in different time regions. By a proper time-region division, the proposed method can better model the time-varying fault behaviors and capture the different fault-to-normal reconstruction relationships for fault diagnosis. The feasibility and performance of the proposed method are illustrated with the Tennessee Eastman process, revealing enhanced fault understanding and improved fault diagnosis performance. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie301096x [article] A Multiple - time - region (MTR) - based fault subspace decomposition and reconstruction modeling strategy for online fault diagnosis [texte imprimé] / Zhao, Chunhui, Auteur ; Youxian Sun, Auteur ; Gao, Furong, Auteur . - 2012 . - pp. 11207–11217. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 34 (Août 2012) . - pp. 11207–11217 Mots-clés : Fault  diagnosis Résumé : Time-varying fault characteristics have not yet been addressed by conventional fault-reconstruction-based modeling methods, which could affect fault diagnosis performance. In the present work, the multiple-time-region (MTR) nature, that is, the multiplicity of fault characteristics along with the process evolution, is proposed and efficiently analyzed for fault diagnosis. First, an automatic time-region-division algorithm is developed that can partition the whole fault process into different local regions according to the changes in fault characteristics. Different local fault characteristics are thus analyzed by building different representative fault feature models in multiple time regions. Following the changing relationships between the fault and normal operation statuses, different fault reconstruction actions are finally taken in different time regions. By a proper time-region division, the proposed method can better model the time-varying fault behaviors and capture the different fault-to-normal reconstruction relationships for fault diagnosis. The feasibility and performance of the proposed method are illustrated with the Tennessee Eastman process, revealing enhanced fault understanding and improved fault diagnosis performance. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie301096x

Nonlinear batch process monitoring using phase-based kernel-independent component analysis−principal component analysis (KICA−PCA) / Zhao, Chunhui in Industrial & engineering chemistry research, Vol. 48 N° 20 (Octobre 2009) 

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Phase-based joint modeling and spectroscopy analysis for batch processes monitoring / Zhao, Chunhui in Industrial & engineering chemistry research, Vol. 49 N° 2 (Janvier 2010) 

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