Documents disponibles écrits par cet auteur

Artificial vision system for the automatic measurement of interfiber pore characteristics and fiber diameter distribution in nanofiber assemblies / Emanuele Tomba in Industrial & engineering chemistry research, Vol. 49 N° 6 (Mars 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 6 (Mars 2010) . - pp. 2957–2968 Titre : Artificial vision system for the automatic measurement of interfiber pore characteristics and fiber diameter distribution in nanofiber assemblies Type de document : texte imprimé Auteurs : Emanuele Tomba, Auteur ; Pierantonio Facco, Auteur ; Martina Roso, Auteur Année de publication : 2010 Article en page(s) : pp. 2957–2968 Note générale : Industrial Chemestry Langues : Anglais (eng) Mots-clés : Artificial--Vision--System--Automatic--Measurement--Interfiber--PoreFiber--Diameter--Fiberribution--Nanofiber Résumé : Nanofiber structures are used in several technologies such as membranes, reinforced materials, textiles, catalysts, sensors, and biomedical materials. For all these applications, it is important to know the morphology of the assemblies, in particular their pore and fiber dimension distributions. However, the current methods used to measure pore sizes are all experimental and indirect; furthermore, the fiber diameter distribution is usually determined manually using a digital image of the nanofiber web. In this paper an artificial vision system is proposed to characterize the nanofiber web by automatically measuring several properties related to the interfiber pore distribution and to the nanofiber diameter distribution. The artificial vision system is characterized by a two-section structure: an image processing section and a property measurement section. The image processing section is centered on a multivariate image analysis procedure for the extraction of morphological features from the image. The property measurement section comprises an algorithm for interfiber pore area and pore morphology evaluation and one for fiber diameter distribution measurement that also accounts for the effect of perspective on the lower-level fiber diameters. Because the proposed artificial vision system is completely automatic, measurements can be taken without the need of any experimental setup and with no human intervention. Therefore, besides being fast and accurate, measurements do not suffer from repeatability issues. The ability of the proposed automatic system in characterizing the morphology of a thin nonwoven nanofiber fabric is demonstrated by application to polymer nanofiber membranes obtained by electrospinning. Note de contenu : Bbibiogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901179m [article] Artificial vision system for the automatic measurement of interfiber pore characteristics and fiber diameter distribution in nanofiber assemblies [texte imprimé] / Emanuele Tomba, Auteur ; Pierantonio Facco, Auteur ; Martina Roso, Auteur . - 2010 . - pp. 2957–2968. Industrial Chemestry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 6 (Mars 2010) . - pp. 2957–2968 Mots-clés : Artificial--Vision--System--Automatic--Measurement--Interfiber--PoreFiber--Diameter--Fiberribution--Nanofiber Résumé : Nanofiber structures are used in several technologies such as membranes, reinforced materials, textiles, catalysts, sensors, and biomedical materials. For all these applications, it is important to know the morphology of the assemblies, in particular their pore and fiber dimension distributions. However, the current methods used to measure pore sizes are all experimental and indirect; furthermore, the fiber diameter distribution is usually determined manually using a digital image of the nanofiber web. In this paper an artificial vision system is proposed to characterize the nanofiber web by automatically measuring several properties related to the interfiber pore distribution and to the nanofiber diameter distribution. The artificial vision system is characterized by a two-section structure: an image processing section and a property measurement section. The image processing section is centered on a multivariate image analysis procedure for the extraction of morphological features from the image. The property measurement section comprises an algorithm for interfiber pore area and pore morphology evaluation and one for fiber diameter distribution measurement that also accounts for the effect of perspective on the lower-level fiber diameters. Because the proposed artificial vision system is completely automatic, measurements can be taken without the need of any experimental setup and with no human intervention. Therefore, besides being fast and accurate, measurements do not suffer from repeatability issues. The ability of the proposed automatic system in characterizing the morphology of a thin nonwoven nanofiber fabric is demonstrated by application to polymer nanofiber membranes obtained by electrospinning. Note de contenu : Bbibiogr. ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901179m

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

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