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Sensor network design for optimal process operation based on data reconciliation / M. Nabil in Industrial & engineering chemistry research, Vol. 51 N° 19 (Mai 2012) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 51 N° 19 (Mai 2012) . - pp. 6789-6797 Titre : Sensor network design for optimal process operation based on data reconciliation Type de document : texte imprimé Auteurs : M. Nabil, Auteur ; Sridharakumar Narasimhan, Auteur Année de publication : 2012 Article en page(s) : pp. 6789-6797 Note générale : Industrial chemistry Langues : Anglais (eng) Mots-clés : Data  reconciliation  Design Résumé : The fundamental problem in optimal sensor network design is choosing a set of important or strategic process variables to be measured. An optimization formulation for sensor network design that relates process economics and data reconciliation is proposed. To address this, an economic quantity is defined to quantify the loss of operational profit caused due to measurement uncertainty. The resulting analytical expression that quantifies the loss is shown to be the sum of weighted error variances of the reconciled estimates obtained from reconciliation. The final formulation is a mixed integer cone program that can be solved to obtain a globally optimal sensor network The effect of the process economics, capital cost, and marginal utility of additional sensors is illustrated using case studies. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25900233 [article] Sensor network design for optimal process operation based on data reconciliation [texte imprimé] / M. Nabil, Auteur ; Sridharakumar Narasimhan, Auteur . - 2012 . - pp. 6789-6797. Industrial chemistry Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 51 N° 19 (Mai 2012) . - pp. 6789-6797 Mots-clés : Data  reconciliation  Design Résumé : The fundamental problem in optimal sensor network design is choosing a set of important or strategic process variables to be measured. An optimization formulation for sensor network design that relates process economics and data reconciliation is proposed. To address this, an economic quantity is defined to quantify the loss of operational profit caused due to measurement uncertainty. The resulting analytical expression that quantifies the loss is shown to be the sum of weighted error variances of the reconciled estimates obtained from reconciliation. The final formulation is a mixed integer cone program that can be solved to obtain a globally optimal sensor network The effect of the process economics, capital cost, and marginal utility of additional sensors is illustrated using case studies. ISSN : 0888-5885 En ligne : http://cat.inist.fr/?aModele=afficheN&cpsidt=25900233