Documents disponibles écrits par cet auteur

Nearest-neighbor method for the automatic maintenance of multivariate statistical soft sensors in batch processing / Pierantonio Facco in Industrial & engineering chemistry research, Vol. 49 N° 5 (Mars 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 5 (Mars 2010) . - pp. 2336–2347 Titre : Nearest-neighbor method for the automatic maintenance of multivariate statistical soft sensors in batch processing Type de document : texte imprimé Auteurs : Pierantonio Facco, Auteur ; Fabrizio Bezzo, Auteur ; Massimiliano Barolo, Auteur Année de publication : 2010 Article en page(s) : pp. 2336–2347 Note générale : Industrial Chemistry Langues : Anglais (eng) Mots-clés : Multivariate  Statistical;  Soft  sensors;  Batch  polymerization Résumé : Soft sensors based on multivariate statistical models are used very frequently for the monitoring of batch processes. From the moment of model calibration onward, the model is usually assumed to be time-invariant. Unfortunately, batch process conditions are subject to several events that make the correlation structure between batches change with respect to that of the original model. This can determine a decay of the soft sensor performance, unless periodic maintenance (i.e., updating) of the model is carried out. This article proposes a methodology for the automatic maintenance of PLS soft sensors in batch processing. Whereas the adaptation scheme usually follows chronological order in classical recursive updating, the proposed strategy defines the reference data set for model recalibration as the set of batches (nearest neighbors) that are most similar to the currently running batch. The nearest neighbors to a running batch are identified during the initial evolution of the batch following a concept of proximity in the latent space of principal components. In this way, for any new batch to be run, a model can be tailored on the running batch itself. The effectiveness of the proposed updating methodology is evaluated in two case studies related to the development of adaptive soft sensors for real-time product quality monitoring: a simulated fed-batch process for the production of penicillin and an industrial batch polymerization process for the production of a resin. Note de contenu : Bibliogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9013919 [article] Nearest-neighbor method for the automatic maintenance of multivariate statistical soft sensors in batch processing [texte imprimé] / Pierantonio Facco, Auteur ; Fabrizio Bezzo, Auteur ; Massimiliano Barolo, Auteur . - 2010 . - pp. 2336–2347. Industrial Chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 5 (Mars 2010) . - pp. 2336–2347 Mots-clés : Multivariate  Statistical;  Soft  sensors;  Batch  polymerization Résumé : Soft sensors based on multivariate statistical models are used very frequently for the monitoring of batch processes. From the moment of model calibration onward, the model is usually assumed to be time-invariant. Unfortunately, batch process conditions are subject to several events that make the correlation structure between batches change with respect to that of the original model. This can determine a decay of the soft sensor performance, unless periodic maintenance (i.e., updating) of the model is carried out. This article proposes a methodology for the automatic maintenance of PLS soft sensors in batch processing. Whereas the adaptation scheme usually follows chronological order in classical recursive updating, the proposed strategy defines the reference data set for model recalibration as the set of batches (nearest neighbors) that are most similar to the currently running batch. The nearest neighbors to a running batch are identified during the initial evolution of the batch following a concept of proximity in the latent space of principal components. In this way, for any new batch to be run, a model can be tailored on the running batch itself. The effectiveness of the proposed updating methodology is evaluated in two case studies related to the development of adaptive soft sensors for real-time product quality monitoring: a simulated fed-batch process for the production of penicillin and an industrial batch polymerization process for the production of a resin. Note de contenu : Bibliogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9013919

Online model-based redesign of experiments for parameter estimation in dynamic systems / Federico Galvanin in Industrial & engineering chemistry research, Vol. 48 N° 9 (Mai 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 9 (Mai 2009) . - pp. 4415–4427 Titre : Online model-based redesign of experiments for parameter estimation in dynamic systems Type de document : texte imprimé Auteurs : Federico Galvanin, Auteur ; Massimiliano Barolo, Auteur ; Fabrizio Bezzo, Auteur Année de publication : 2009 Article en page(s) : pp. 4415–4427 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Dynamic  systems  Online  model-based Résumé : The optimal model-based design of experiments aims at designing a set of dynamic experiments yielding the most informative process data to be used for the estimation of the parameters of a first-principles dynamic process model. According to the usual procedure for parameter estimation, the experiment is first designed offline; then, the experiment is carried out in the plant, and process measurements are collected; and finally, parameters are estimated after completion of the experiment. Therefore, the information gathered during the evolution of the experiment is analyzed only at the end of the experiment itself. Since the experiment is designed on the basis of the parameter estimates available before the experiment is started, the progressive increase of the information resulting from the progress of the experiment is not exploited by the designer until the end of that experiment. In this paper, a strategy for the online model-based redesign of experiments is proposed to exploit the information as soon as it is generated from the execution of an experiment, and its performance is compared to that of a standard optimal experiment design approach. Intermediate parameter estimations are carried out while the experiment is running, and by exploiting the information obtained, the experiment is partially redesigned before its termination, with the purpose of updating the experimental settings to generate more valuable information for subsequent analysis. This enables us to reduce the number of experimental trials that are needed to reach a statistically sound estimation of the model parameters and results in a reduction of experimental time, raw materials needs, number of samples to be analyzed, control effort, and labor. Two simulated case studies of increasing level of complexity are used to demonstrate the benefits of the proposed approach with respect to a state-of-the-art sequential model-based experiment design. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8018356 [article] Online model-based redesign of experiments for parameter estimation in dynamic systems [texte imprimé] / Federico Galvanin, Auteur ; Massimiliano Barolo, Auteur ; Fabrizio Bezzo, Auteur . - 2009 . - pp. 4415–4427. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 9 (Mai 2009) . - pp. 4415–4427 Mots-clés : Dynamic  systems  Online  model-based Résumé : The optimal model-based design of experiments aims at designing a set of dynamic experiments yielding the most informative process data to be used for the estimation of the parameters of a first-principles dynamic process model. According to the usual procedure for parameter estimation, the experiment is first designed offline; then, the experiment is carried out in the plant, and process measurements are collected; and finally, parameters are estimated after completion of the experiment. Therefore, the information gathered during the evolution of the experiment is analyzed only at the end of the experiment itself. Since the experiment is designed on the basis of the parameter estimates available before the experiment is started, the progressive increase of the information resulting from the progress of the experiment is not exploited by the designer until the end of that experiment. In this paper, a strategy for the online model-based redesign of experiments is proposed to exploit the information as soon as it is generated from the execution of an experiment, and its performance is compared to that of a standard optimal experiment design approach. Intermediate parameter estimations are carried out while the experiment is running, and by exploiting the information obtained, the experiment is partially redesigned before its termination, with the purpose of updating the experimental settings to generate more valuable information for subsequent analysis. This enables us to reduce the number of experimental trials that are needed to reach a statistically sound estimation of the model parameters and results in a reduction of experimental time, raw materials needs, number of samples to be analyzed, control effort, and labor. Two simulated case studies of increasing level of complexity are used to demonstrate the benefits of the proposed approach with respect to a state-of-the-art sequential model-based experiment design. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie8018356

Optimization-based approaches for bioethanol supply chains / Ozlem Akgul in Industrial & engineering chemistry research, Vol. 50 N° 9 (Mai 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 9 (Mai 2011) . - pp. 4927-4938 Titre : Optimization-based approaches for bioethanol supply chains Type de document : texte imprimé Auteurs : Ozlem Akgul, Auteur ; Andrea Zamboni, Auteur ; Fabrizio Bezzo, Auteur Année de publication : 2011 Article en page(s) : pp. 4927-4938 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Optimization Résumé : The E.U. has adopted a target of 10% of energy for transportation coming from renewable sources, including biofuels, by 2020 to tackle the increasing greenhouse gas emissions problem and reduce dependency on fossil fuels. In this paper, mixed integer linear programing (MILP) models are presented for the optimal design of a bioethanol supply chain with the objective of minimizing the total supply chain cost. The models aim to optimize the locations and scales of the bioethanol production plants, biomass and bioethanol flows between regions, and the number of transport units required for the transfer of these products between regions as well as for local delivery. The optimal bioethanol production and biomass cultivation rates are also determined by the model. The applicability of the proposed models is demonstrated with a case study for Northern Italy. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24128625 [article] Optimization-based approaches for bioethanol supply chains [texte imprimé] / Ozlem Akgul, Auteur ; Andrea Zamboni, Auteur ; Fabrizio Bezzo, Auteur . - 2011 . - pp. 4927-4938. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 9 (Mai 2011) . - pp. 4927-4938 Mots-clés : Optimization Résumé : The E.U. has adopted a target of 10% of energy for transportation coming from renewable sources, including biofuels, by 2020 to tackle the increasing greenhouse gas emissions problem and reduce dependency on fossil fuels. In this paper, mixed integer linear programing (MILP) models are presented for the optimal design of a bioethanol supply chain with the objective of minimizing the total supply chain cost. The models aim to optimize the locations and scales of the bioethanol production plants, biomass and bioethanol flows between regions, and the number of transport units required for the transfer of these products between regions as well as for local delivery. The optimal bioethanol production and biomass cultivation rates are also determined by the model. The applicability of the proposed models is demonstrated with a case study for Northern Italy. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=24128625

Transfer of process monitoring models between different plants using latent variable techniques / Pierantonio Facco in Industrial & engineering chemistry research, Vol. 51 N° 21 (Mai 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 21 (Mai 2012) . - pp. 7327–7339 Titre : Transfer of process monitoring models between different plants using latent variable techniques Type de document : texte imprimé Auteurs : Pierantonio Facco, Auteur ; Emanuele Tomba, Auteur ; Fabrizio Bezzo, Auteur Année de publication : 2012 Article en page(s) : pp. 7327–7339 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Process  monitoring  models Résumé : This paper addresses the scenario where the manufacturing of a product with assigned quality specifications is transferred from a plant A to a plant B, which uses the same manufacturing process as plant A, but may differ for scale, configuration, actual operating conditions, measurement system arrangement, or simply location. The issue arises on whether the process data already available for plant A can be exploited to build a process monitoring model enabling to monitor the operation of plant B until enough data have been collected in this plant to design a monitoring model based entirely on the incoming data. This paper presents a general framework to tackle this problem (which we refer to as a model transfer problem), and three possible latent variable approaches within this framework are proposed and evaluated. One approach makes use of measurements coming from plant A only, whereas the other two integrate plant A data and plant B data into a single adaptive monitoring model. The proposed approaches are tested on an industrial spray-drying process, where plant A is a pilot unit and plant B is a production unit. It is shown that all proposed model transfer approaches guarantee very satisfactory monitoring performance in plant B, with quick fault detection, limited number of false alarms or undetected faults, and limited (or no) need of plant B data to accomplish the model transfer. We believe that these strategies can provide a valuable contribution to the practical implementation of quality-by-design methodologies and continuous quality assurance programs in product manufacturing. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie202974u [article] Transfer of process monitoring models between different plants using latent variable techniques [texte imprimé] / Pierantonio Facco, Auteur ; Emanuele Tomba, Auteur ; Fabrizio Bezzo, Auteur . - 2012 . - pp. 7327–7339. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 21 (Mai 2012) . - pp. 7327–7339 Mots-clés : Process  monitoring  models Résumé : This paper addresses the scenario where the manufacturing of a product with assigned quality specifications is transferred from a plant A to a plant B, which uses the same manufacturing process as plant A, but may differ for scale, configuration, actual operating conditions, measurement system arrangement, or simply location. The issue arises on whether the process data already available for plant A can be exploited to build a process monitoring model enabling to monitor the operation of plant B until enough data have been collected in this plant to design a monitoring model based entirely on the incoming data. This paper presents a general framework to tackle this problem (which we refer to as a model transfer problem), and three possible latent variable approaches within this framework are proposed and evaluated. One approach makes use of measurements coming from plant A only, whereas the other two integrate plant A data and plant B data into a single adaptive monitoring model. The proposed approaches are tested on an industrial spray-drying process, where plant A is a pilot unit and plant B is a production unit. It is shown that all proposed model transfer approaches guarantee very satisfactory monitoring performance in plant B, with quick fault detection, limited number of false alarms or undetected faults, and limited (or no) need of plant B data to accomplish the model transfer. We believe that these strategies can provide a valuable contribution to the practical implementation of quality-by-design methodologies and continuous quality assurance programs in product manufacturing. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie202974u