Documents disponibles écrits par cet auteur

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

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

Model migration with inclusive similarity for development of a new process model / Junde Lu in Industrial & engineering chemistry research, Vol. 47 N° 23 (Décembre 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 N° 23 (Décembre 2008) . - p. 9508–9516 Titre : Model migration with inclusive similarity for development of a new process model Type de document : texte imprimé Auteurs : Junde Lu, Auteur ; Gao, Furong, Auteur Année de publication : 2009 Article en page(s) : p. 9508–9516 Note générale : Chemistry engineering Langues : Anglais (eng) Mots-clés : Model  migration Résumé : In the processing industries, operating conditions change to meet the requirements of the market and customers. Under different operating conditions, data-based process modeling must be repeated for the development of a new process model. Obviously, this is inefficient and uneconomical. Effective use and adaptation of the existing process model can reduce the number of experiments in the development of a new process model, resulting in savings of time, cost, and effort. In this paper, a particular process similarity, inclusive similarity, is discussed in detail. A model migration strategy for processes with this type of similarity is developed to model a new process by taking advantage of existing models and data from the new process. The new model is built by aggregating the existing models using a bagging algorithm. As an illustrated example, the development of a new soft-sensor model for online prediction of melt-flow length for new mold geometry for an injection molding process is demonstrated by taking advantage of existing models for different molds. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800595a [article] Model migration with inclusive similarity for development of a new process model [texte imprimé] / Junde Lu, Auteur ; Gao, Furong, Auteur . - 2009 . - p. 9508–9516. Chemistry engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 N° 23 (Décembre 2008) . - p. 9508–9516 Mots-clés : Model  migration Résumé : In the processing industries, operating conditions change to meet the requirements of the market and customers. Under different operating conditions, data-based process modeling must be repeated for the development of a new process model. Obviously, this is inefficient and uneconomical. Effective use and adaptation of the existing process model can reduce the number of experiments in the development of a new process model, resulting in savings of time, cost, and effort. In this paper, a particular process similarity, inclusive similarity, is discussed in detail. A model migration strategy for processes with this type of similarity is developed to model a new process by taking advantage of existing models and data from the new process. The new model is built by aggregating the existing models using a bagging algorithm. As an illustrated example, the development of a new soft-sensor model for online prediction of melt-flow length for new mold geometry for an injection molding process is demonstrated by taking advantage of existing models for different molds. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800595a

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

Permalink

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) 

Permalink

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) 

Permalink

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

Permalink

Statistical modeling and online monitoring based on between - set regression analysis / Chunhui Zhao in Industrial & engineering chemistry research, Vol. 51 N° 25 (Juin 2012) 

Permalink

Statistical prediction of product quality in batch processes with limited batch - cycle data / Ge, Zhiqiang in Industrial & engineering chemistry research, Vol. 51 N° 35 (Septembre 2012) 

Permalink

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

Permalink