Documents disponibles écrits par cet auteur

Discriminating between critical and noncritical disturbances in (Bio)chemical batch processes using multimodel fault detection and end - quality prediction / Geert Gins in Industrial & engineering chemistry research, Vol. 51 N° 38 (Septembre 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 38 (Septembre 2012) . - pp. 12375–12385 Titre : Discriminating between critical and noncritical disturbances in (Bio)chemical batch processes using multimodel fault detection and end - quality prediction Type de document : texte imprimé Auteurs : Geert Gins, Auteur ; Jef Vanlaer, Auteur ; Jan F. M. Van Impe, Auteur Année de publication : 2012 Article en page(s) : pp. 12375–12385 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Biochemical  batch  processes Résumé : This paper proposes a novel multimodel methodology for discriminating between critical and noncritical process disturbances in (bio)chemical batch processes, in addition to providing online prediction of batch-end quality. A multivariate multiway partial least squares (MPLS) or multiway principal component analysis (MPCA) model monitoring all available measurements is coupled with an MPLS or MPCA model monitoring only those measurements influencing the final product quality. Hence, process disturbances are labeled critical or noncritical, depending on whether they impact final quality and require immediate attention. This avoids unnecessary control actions or even early batch terminations for noncritical disturbances. The presented approach is illustrated on a simulated industrial-scale penicillin production process. On the basis of extensive simulation results, it is concluded that the proposed methodology discriminates between critical (according to a hypothesis test with 0.05 significance level) and noncritical disturbances. In addition, accurate online estimations of the batch-end product quality are provided. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie202386p [article] Discriminating between critical and noncritical disturbances in (Bio)chemical batch processes using multimodel fault detection and end - quality prediction [texte imprimé] / Geert Gins, Auteur ; Jef Vanlaer, Auteur ; Jan F. M. Van Impe, Auteur . - 2012 . - pp. 12375–12385. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 38 (Septembre 2012) . - pp. 12375–12385 Mots-clés : Biochemical  batch  processes Résumé : This paper proposes a novel multimodel methodology for discriminating between critical and noncritical process disturbances in (bio)chemical batch processes, in addition to providing online prediction of batch-end quality. A multivariate multiway partial least squares (MPLS) or multiway principal component analysis (MPCA) model monitoring all available measurements is coupled with an MPLS or MPCA model monitoring only those measurements influencing the final product quality. Hence, process disturbances are labeled critical or noncritical, depending on whether they impact final quality and require immediate attention. This avoids unnecessary control actions or even early batch terminations for noncritical disturbances. The presented approach is illustrated on a simulated industrial-scale penicillin production process. On the basis of extensive simulation results, it is concluded that the proposed methodology discriminates between critical (according to a hypothesis test with 0.05 significance level) and noncritical disturbances. In addition, accurate online estimations of the batch-end product quality are provided. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie202386p

Hybrid derivative dynamic time warping for online industrial batch - end quality estimation / Geert Gins in Industrial & engineering chemistry research, Vol. 51 N° 17 (Mai 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 17 (Mai 2012) . - pp. 6071–6084 Titre : Hybrid derivative dynamic time warping for online industrial batch - end quality estimation Type de document : texte imprimé Auteurs : Geert Gins, Auteur ; Pieter Van den Kerkhof, Auteur ; Jan F. M. Van Impe, Auteur Année de publication : 2012 Article en page(s) : pp. 6071–6084 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Hybrid  Industrial  polymerization Résumé : This paper discusses the design of an inferential sensor for the online prediction of the end-quality of an industrial batch polymerization process. Owing to unequal batch speeds, measurement profiles must be synchronized before modeling. This makes profile alignment an integral part of any inferential sensor. In this work, a novel online hybrid derivative dynamic time warping data alignment technique is presented. The proposed technique allows for automatic adjustment of the warping resolution to achieve optimal alignment results for both slowly and rapidly varying parts of the measurement profiles. The proposed online data alignment technique is combined with a multiway partial least-squares black box model to yield online predictions of the final quality of a running batch process. It is demonstrated that this inferential sensor is capable of accurately predicting the quality online for an industrial polymerization process, even when the production process is only halfway, that is, well before lab measurements become available. As a result of this early warning, batches violating the quality specifications can be corrected or even stopped. This leads to fewer off-spec batches, saves production time, lowers operational costs, and reduces waste material and energy. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie2019068 [article] Hybrid derivative dynamic time warping for online industrial batch - end quality estimation [texte imprimé] / Geert Gins, Auteur ; Pieter Van den Kerkhof, Auteur ; Jan F. M. Van Impe, Auteur . - 2012 . - pp. 6071–6084. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 17 (Mai 2012) . - pp. 6071–6084 Mots-clés : Hybrid  Industrial  polymerization Résumé : This paper discusses the design of an inferential sensor for the online prediction of the end-quality of an industrial batch polymerization process. Owing to unequal batch speeds, measurement profiles must be synchronized before modeling. This makes profile alignment an integral part of any inferential sensor. In this work, a novel online hybrid derivative dynamic time warping data alignment technique is presented. The proposed technique allows for automatic adjustment of the warping resolution to achieve optimal alignment results for both slowly and rapidly varying parts of the measurement profiles. The proposed online data alignment technique is combined with a multiway partial least-squares black box model to yield online predictions of the final quality of a running batch process. It is demonstrated that this inferential sensor is capable of accurately predicting the quality online for an industrial polymerization process, even when the production process is only halfway, that is, well before lab measurements become available. As a result of this early warning, batches violating the quality specifications can be corrected or even stopped. This leads to fewer off-spec batches, saves production time, lowers operational costs, and reduces waste material and energy. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie2019068