Documents disponibles écrits par cet auteur

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

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