Documents disponibles écrits par cet auteur

Online data reconciliation with poor redundancy systems / Flavio Manenti in Industrial & engineering chemistry research, Vol. 50 N° 24 (Décembre 2011) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 50 N° 24 (Décembre 2011) . - pp. 14105-14114 Titre : Online data reconciliation with poor redundancy systems Type de document : texte imprimé Auteurs : Flavio Manenti, Auteur ; Maria Grazia Grottoli, Auteur ; Sauro Pierucci, Auteur Année de publication : 2012 Article en page(s) : pp. 14105-14114 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Data  reconciliation Résumé : The paper deals with the integrated solution of different model-based optimization levels to face the problem of inferring and reconciling online plant measurements practically, under the condition of poor measure redundancy, because of a lack of instrumentation installed in the field. The novelty of the proposed computer-aided process engineering (CAPE) solution is in the simultaneous integration of different optimization levels: (i) the data reconciliation based on a detailed process simulation; (ii) the introduction and estimation of certain adaptive parameters, to match the current process conditions as well as to confer a certain generality on it; and (iii) the use of a set of efficient optimizers to improve plant operations. The online feasibility of the proposed CAPE solution is validated on a large-scale sulfur recovery unit (SRU) of an oil refinery. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25299879 [article] Online data reconciliation with poor redundancy systems [texte imprimé] / Flavio Manenti, Auteur ; Maria Grazia Grottoli, Auteur ; Sauro Pierucci, Auteur . - 2012 . - pp. 14105-14114. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 24 (Décembre 2011) . - pp. 14105-14114 Mots-clés : Data  reconciliation Résumé : The paper deals with the integrated solution of different model-based optimization levels to face the problem of inferring and reconciling online plant measurements practically, under the condition of poor measure redundancy, because of a lack of instrumentation installed in the field. The novelty of the proposed computer-aided process engineering (CAPE) solution is in the simultaneous integration of different optimization levels: (i) the data reconciliation based on a detailed process simulation; (ii) the introduction and estimation of certain adaptive parameters, to match the current process conditions as well as to confer a certain generality on it; and (iii) the use of a set of efficient optimizers to improve plant operations. The online feasibility of the proposed CAPE solution is validated on a large-scale sulfur recovery unit (SRU) of an oil refinery. DEWEY : 660 ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25299879

Sulfur recovery units / Stefano Signor in Industrial & engineering chemistry research, Vol. 49 N° 12 (Juin 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 12 (Juin 2010) . - pp. 5714–5724 Titre : Sulfur recovery units : adaptive simulation and model validation on an industrial plant Type de document : texte imprimé Auteurs : Stefano Signor, Auteur ; Flavio Manenti, Auteur ; Maria Grazia Grottoli, Auteur Année de publication : 2010 Article en page(s) : pp. 5714–5724 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Sulfur  recovery  units  Adaptive  simulation  parameters  Italy Résumé : The paper is aimed at discussing and fixing issues in providing a generalized approach to the simulation of sulfur recovery units (SRUs). The main goal is to get a simulation that is at the same time (i) reasonably detailed and robust to properly characterize SRUs and (ii) so generalized to provide a tool that is not only specific for the case in study. To achieve point (i), standard libraries belonging to commercial process simulators are coupled to specific heuristic relations coming from the industrial experience for modeling the thermal furnace and the catalytic Claus converters; this allows us to infer with a certain reliability those measures that are often missing or unavailable online in these processes. To achieve point (ii), a series of adaptive parameters are filled in the process simulation by making it more flexible and yet preserving all model details. The most recent techniques and numerical methods, to tune the adaptive simulation parameters, are implemented in Visual C++ and interfaced to PRO/II (by SimSci-Esscor) to obtain a robust parameter estimation solved by means of the BzzMath library. At last, the detailed and tuned adaptive simulation is validated along a period of 2 months on a large-scale SRU (TECHNIP-KTI SpA technology) operating in Italy. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901749t [article] Sulfur recovery units : adaptive simulation and model validation on an industrial plant [texte imprimé] / Stefano Signor, Auteur ; Flavio Manenti, Auteur ; Maria Grazia Grottoli, Auteur . - 2010 . - pp. 5714–5724. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 12 (Juin 2010) . - pp. 5714–5724 Mots-clés : Sulfur  recovery  units  Adaptive  simulation  parameters  Italy Résumé : The paper is aimed at discussing and fixing issues in providing a generalized approach to the simulation of sulfur recovery units (SRUs). The main goal is to get a simulation that is at the same time (i) reasonably detailed and robust to properly characterize SRUs and (ii) so generalized to provide a tool that is not only specific for the case in study. To achieve point (i), standard libraries belonging to commercial process simulators are coupled to specific heuristic relations coming from the industrial experience for modeling the thermal furnace and the catalytic Claus converters; this allows us to infer with a certain reliability those measures that are often missing or unavailable online in these processes. To achieve point (ii), a series of adaptive parameters are filled in the process simulation by making it more flexible and yet preserving all model details. The most recent techniques and numerical methods, to tune the adaptive simulation parameters, are implemented in Visual C++ and interfaced to PRO/II (by SimSci-Esscor) to obtain a robust parameter estimation solved by means of the BzzMath library. At last, the detailed and tuned adaptive simulation is validated along a period of 2 months on a large-scale SRU (TECHNIP-KTI SpA technology) operating in Italy. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie901749t