Documents disponibles écrits par cet auteur

General framework for latent variable model inversion for the design and manufacturing of new products / Emanuele Tomba in Industrial & engineering chemistry research, Vol. 51 N° 39 (Octobre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 39 (Octobre 2012) . - pp. 12886-12900 Titre : General framework for latent variable model inversion for the design and manufacturing of new products Type de document : texte imprimé Auteurs : Emanuele Tomba, Auteur ; Massimiliano Barolo, Auteur ; Salvador García-Muñoz, Auteur Année de publication : 2012 Article en page(s) : pp. 12886-12900 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Manufacturing  Design  Modeling Résumé : Latent variable regression model (LVRM) inversion is a useful tool to support the development of new products and their manufacturing conditions. The objective of the model inversion exercise is that ot finding the best combination of regressors (e.g., raw material properties, process parameters) that are needed to obtain a desired response (e.g., product quality) from the model. Each of the published applications where model inversion has been applied utilizes a tailored approach to achieve the inversion, given the specific objectives and needs. These approaches range from the direct inversion of the LVRM to the formulation of an objective function that is optimized using nonlinear programming. In this paper we present a framework that aims to give a holistic view of the optimization formulations that can arise from the need to invert an LVRM. The different sets of equations that become relevant (either as a term within the objective function or as a constraint) are discussed, and an example of these scenarios is also provided. Additional to the formulation of the different scenarios and their objective functions, this work proposes a new metric (the P2 statistic) to cross-validate the ability of the model to reconstruct the regressor vector (analogous to the Q2 statistic aimed at the predictability of the response). This new metric comes from the need to not only predict the response from the regressor, but to also reconstruct the regressors from the scores values. In this context, a discussion is provided on the effect of uncertainty in the reconstruction of the regressor (the actual design) as these values are normally given upstream as targets to the supplier of materials, or as set points to the process. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26419245 [article] General framework for latent variable model inversion for the design and manufacturing of new products [texte imprimé] / Emanuele Tomba, Auteur ; Massimiliano Barolo, Auteur ; Salvador García-Muñoz, Auteur . - 2012 . - pp. 12886-12900. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 39 (Octobre 2012) . - pp. 12886-12900 Mots-clés : Manufacturing  Design  Modeling Résumé : Latent variable regression model (LVRM) inversion is a useful tool to support the development of new products and their manufacturing conditions. The objective of the model inversion exercise is that ot finding the best combination of regressors (e.g., raw material properties, process parameters) that are needed to obtain a desired response (e.g., product quality) from the model. Each of the published applications where model inversion has been applied utilizes a tailored approach to achieve the inversion, given the specific objectives and needs. These approaches range from the direct inversion of the LVRM to the formulation of an objective function that is optimized using nonlinear programming. In this paper we present a framework that aims to give a holistic view of the optimization formulations that can arise from the need to invert an LVRM. The different sets of equations that become relevant (either as a term within the objective function or as a constraint) are discussed, and an example of these scenarios is also provided. Additional to the formulation of the different scenarios and their objective functions, this work proposes a new metric (the P2 statistic) to cross-validate the ability of the model to reconstruct the regressor vector (analogous to the Q2 statistic aimed at the predictability of the response). This new metric comes from the need to not only predict the response from the regressor, but to also reconstruct the regressors from the scores values. In this context, a discussion is provided on the effect of uncertainty in the reconstruction of the regressor (the actual design) as these values are normally given upstream as targets to the supplier of materials, or as set points to the process. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=26419245

Optimization of batch operating policies. Part II. incorporating process constraints and industrial applications / Salvador García-Muñoz in Industrial & engineering chemistry research, Vol. 47 n°12 (Juin 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 n°12 (Juin 2008) . - p. 4202–4208 Titre : Optimization of batch operating policies. Part II. incorporating process constraints and industrial applications Type de document : texte imprimé Auteurs : Salvador García-Muñoz, Auteur ; John F. MacGregor, Auteur ; Debashis Neogi, Auteur ; Bruce E. Latshaw, Auteur Année de publication : 2008 Article en page(s) : p. 4202–4208 Note générale : Bibliogr. p. 4208 Langues : Anglais (eng) Mots-clés : Partial  least  squares;  Batch  data Résumé : In the first part of this series [Ind. Eng. Chem. Res. 2006, 45, 7856−7866], data-driven approaches, based on partial least squares (PLS) models built from historical batch data, were used to find optimal batch operating trajectories that would yield a desired vector of final product quality attributes. The method allowed for the inclusion of univariate and multivariate constraints on the set of desired final product quality attributes and presented approaches for handling multiple solutions. In this paper, the technology is further extended to include constraints in the process operating trajectories themselves. The methodology is successfully applied to an industrial batch polymerization process where the batch trajectories are designed to achieve specific properties of the final polymer while consuming the minimal amount of time for the batch run. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie071437j [article] Optimization of batch operating policies. Part II. incorporating process constraints and industrial applications [texte imprimé] / Salvador García-Muñoz, Auteur ; John F. MacGregor, Auteur ; Debashis Neogi, Auteur ; Bruce E. Latshaw, Auteur . - 2008 . - p. 4202–4208. Bibliogr. p. 4208 Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°12 (Juin 2008) . - p. 4202–4208 Mots-clés : Partial  least  squares;  Batch  data Résumé : In the first part of this series [Ind. Eng. Chem. Res. 2006, 45, 7856−7866], data-driven approaches, based on partial least squares (PLS) models built from historical batch data, were used to find optimal batch operating trajectories that would yield a desired vector of final product quality attributes. The method allowed for the inclusion of univariate and multivariate constraints on the set of desired final product quality attributes and presented approaches for handling multiple solutions. In this paper, the technology is further extended to include constraints in the process operating trajectories themselves. The methodology is successfully applied to an industrial batch polymerization process where the batch trajectories are designed to achieve specific properties of the final polymer while consuming the minimal amount of time for the batch run. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie071437j