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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

Model migration for development of a new process model / Junde Lu in Industrial & engineering chemistry research, Vol. 48 N° 21 (Novembre 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 21 (Novembre 2009) . - pp. 9603–9610 Titre : Model migration for development of a new process model Type de document : texte imprimé Auteurs : Junde Lu, Auteur ; Yuan Yao, Auteur ; Gao, Furong, Auteur Année de publication : 2010 Article en page(s) : pp. 9603–9610 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Data-based  process  models  Model  migration  method Résumé : Data-based process models are usually developed by fitting input−output data collected on a particular process. The model built on one particular process becomes invalid with another similar process. Traditional data-based modeling methods have to completely rebuild a new process model on a similar process, leading to repetition of a large number of experiments, if process similarities between two similar processes are ignored. Effective use and extraction of these process similarities and migration of the existing process model to the new process can require a fewer number of experiments for the development of a new process model, resulting in savings of time, cost, and effort. In this paper, we present a model migration method that can quickly model a new process based on an existing base model and contrast information between the base model and the new process. The method developed involves a procedure of six steps: information extraction from the base model, initial design of experiments, slope/bias correction (SBC) to the base model, outlier detection and assessment, further design of experiments, and development of the new model by combining local difference models and the corrected base model. An example is provided to illustrate the new model development strategy for predicting injection molded part weight, taking advantage of an existing model. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8013296 [article] Model migration for development of a new process model [texte imprimé] / Junde Lu, Auteur ; Yuan Yao, Auteur ; Gao, Furong, Auteur . - 2010 . - pp. 9603–9610. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 21 (Novembre 2009) . - pp. 9603–9610 Mots-clés : Data-based  process  models  Model  migration  method Résumé : Data-based process models are usually developed by fitting input−output data collected on a particular process. The model built on one particular process becomes invalid with another similar process. Traditional data-based modeling methods have to completely rebuild a new process model on a similar process, leading to repetition of a large number of experiments, if process similarities between two similar processes are ignored. Effective use and extraction of these process similarities and migration of the existing process model to the new process can require a fewer number of experiments for the development of a new process model, resulting in savings of time, cost, and effort. In this paper, we present a model migration method that can quickly model a new process based on an existing base model and contrast information between the base model and the new process. The method developed involves a procedure of six steps: information extraction from the base model, initial design of experiments, slope/bias correction (SBC) to the base model, outlier detection and assessment, further design of experiments, and development of the new model by combining local difference models and the corrected base model. An example is provided to illustrate the new model development strategy for predicting injection molded part weight, taking advantage of an existing model. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8013296

Statistical monitoring and fault diagnosis of batch processes using two - dimensional dynamic information / Yuan Yao in Industrial & engineering chemistry research, Vol. 49 N° 20 (Octobre 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 20 (Octobre 2010) . - pp. 9961–9969 Titre : Statistical monitoring and fault diagnosis of batch processes using two - dimensional dynamic information Type de document : texte imprimé Auteurs : Yuan Yao, Auteur ; Furong Gao, Auteur Année de publication : 2011 Article en page(s) : pp. 9961–9969 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Dynamic Résumé : Two-dimensional (2D) dynamics widely exist in batch processes, which inspirit research efforts to develop corresponding monitoring schemes. Recently, two-dimensional dynamic principal component analysis (2D-DPCA) has been proposed to model and monitor such 2D dynamic batch processes, in which support region (ROS) determination is a key step. A proper ROS ensures modeling accuracy, monitoring efficiency, and reasonable fault diagnosis. The previous ROS determination method is practicable in many situations but still has certain limitations, as discussed in this paper. To overcome these shortcomings, a 2D-DPCA method with an improved ROS determination procedure is developed, by considering variable partial correlations and performing iterative stepwise regressions. Such a procedure expands ROS batch by batch and is a generalization of the autoregressive (AR) model order selection to the 2D batch process cases. Simulations show that the proposed method extracts 2D dynamics more accurately and improves the monitoring and diagnosis performance of the 2D-DPCA model. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100860x [article] Statistical monitoring and fault diagnosis of batch processes using two - dimensional dynamic information [texte imprimé] / Yuan Yao, Auteur ; Furong Gao, Auteur . - 2011 . - pp. 9961–9969. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 20 (Octobre 2010) . - pp. 9961–9969 Mots-clés : Dynamic Résumé : Two-dimensional (2D) dynamics widely exist in batch processes, which inspirit research efforts to develop corresponding monitoring schemes. Recently, two-dimensional dynamic principal component analysis (2D-DPCA) has been proposed to model and monitor such 2D dynamic batch processes, in which support region (ROS) determination is a key step. A proper ROS ensures modeling accuracy, monitoring efficiency, and reasonable fault diagnosis. The previous ROS determination method is practicable in many situations but still has certain limitations, as discussed in this paper. To overcome these shortcomings, a 2D-DPCA method with an improved ROS determination procedure is developed, by considering variable partial correlations and performing iterative stepwise regressions. Such a procedure expands ROS batch by batch and is a generalization of the autoregressive (AR) model order selection to the 2D batch process cases. Simulations show that the proposed method extracts 2D dynamics more accurately and improves the monitoring and diagnosis performance of the 2D-DPCA model. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie100860x

Two-dimensional dynamic principal component analysis with autodetermined support region / Yuan Yao in Industrial & engineering chemistry research, Vol. 48 N°2 (Janvier 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N°2 (Janvier 2009) . - p.837–843 Titre : Two-dimensional dynamic principal component analysis with autodetermined support region Type de document : texte imprimé Auteurs : Yuan Yao, Auteur ; Yinghu Diao, Auteur ; Ningyun Lu, Auteur ; Junde Lu, Auteur ; Gao, Furong, Auteur Année de publication : 2009 Article en page(s) : p.837–843 Note générale : chemical engineering Langues : Anglais (eng) Mots-clés : Dynamics--Principal  Component  Analysis Résumé : Dynamics are inherent characteristics of batch processes. In some cases, such dynamics exist not only within a particular batch, but also from batch to batch. In previous work, two-dimensional dynamic principal component analysis (2-D-DPCA) has been developed to monitor 2-D dynamics. Support region determination is a key step in 2-D-DPCA modeling and monitoring of a batch process. A proper support region can ensure modeling accuracy, monitoring efficiency, and reasonable fault diagnosis. In this work, an automatic method for support region determination is developed. This data-based method can be applied on different batch processes without prior process knowledge. Simulation shows that the developed method has good application potentials for both monitoring and fault diagnosis. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800825m [article] Two-dimensional dynamic principal component analysis with autodetermined support region [texte imprimé] / Yuan Yao, Auteur ; Yinghu Diao, Auteur ; Ningyun Lu, Auteur ; Junde Lu, Auteur ; Gao, Furong, Auteur . - 2009 . - p.837–843. chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N°2 (Janvier 2009) . - p.837–843 Mots-clés : Dynamics--Principal  Component  Analysis Résumé : Dynamics are inherent characteristics of batch processes. In some cases, such dynamics exist not only within a particular batch, but also from batch to batch. In previous work, two-dimensional dynamic principal component analysis (2-D-DPCA) has been developed to monitor 2-D dynamics. Support region determination is a key step in 2-D-DPCA modeling and monitoring of a batch process. A proper support region can ensure modeling accuracy, monitoring efficiency, and reasonable fault diagnosis. In this work, an automatic method for support region determination is developed. This data-based method can be applied on different batch processes without prior process knowledge. Simulation shows that the developed method has good application potentials for both monitoring and fault diagnosis. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800825m