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Robust optimization for petrochemical network design under uncertainty / K. Al-Qahtani in Industrial & engineering chemistry research, Vol. 47 n°11 (Juin 2008) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 47 n°11 (Juin 2008) . - p. 3912–3919 Titre : Robust optimization for petrochemical network design under uncertainty Type de document : texte imprimé Auteurs : K. Al-Qahtani, Auteur ; A. Elkamel, Auteur ; K. Ponnambalam, Auteur Année de publication : 2008 Article en page(s) : p. 3912–3919 Note générale : Bibliogr. p. 3918-3919 Langues : Anglais (eng) Mots-clés : Petrochemical  network;  Two-stage  stochastic  MINLP Résumé : This paper addresses the strategic planning, design, and optimization of a network of petrochemical processes under uncertainty and risk considerations. In this work, we extend the deterministic model proposed by Al-Sharrah et al. [Ind. Eng. Chem. Res. 2001, 40, 2103; Chem. Eng. Res. Des. 2006, 84, 1019] to account for parameter uncertainty in process yield, raw material cost, product prices, and lower product market demand. The problem was formulated as a two-stage stochastic mixed-integer nonlinear programming model (MINLP). Risk was accounted for in terms of deviation in both projected benefits in the first stage variables and process yield and forecasted demand in terms of the recourse variables. For each term, a different scaling factor was used to analyze the sensitivity of the petrochemical network due to variations of each component. The study showed that the final petrochemical network bears more sensitivity to variations in product demand and process yields for scaling parameters values that maintain the final petrochemical structure obtained form the stochastic model. The concept of expected value of perfect information (EVPI) and value of the stochastic solution (VSS) are also investigated to numerically illustrate the value of including the randomness of the different model parameters. Modeling uncertainty in the process parameters provided a more robust analysis and practical perspective of this type of problem in the chemical industry. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0713184 [article] Robust optimization for petrochemical network design under uncertainty [texte imprimé] / K. Al-Qahtani, Auteur ; A. Elkamel, Auteur ; K. Ponnambalam, Auteur . - 2008 . - p. 3912–3919. Bibliogr. p. 3918-3919 Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 47 n°11 (Juin 2008) . - p. 3912–3919 Mots-clés : Petrochemical  network;  Two-stage  stochastic  MINLP Résumé : This paper addresses the strategic planning, design, and optimization of a network of petrochemical processes under uncertainty and risk considerations. In this work, we extend the deterministic model proposed by Al-Sharrah et al. [Ind. Eng. Chem. Res. 2001, 40, 2103; Chem. Eng. Res. Des. 2006, 84, 1019] to account for parameter uncertainty in process yield, raw material cost, product prices, and lower product market demand. The problem was formulated as a two-stage stochastic mixed-integer nonlinear programming model (MINLP). Risk was accounted for in terms of deviation in both projected benefits in the first stage variables and process yield and forecasted demand in terms of the recourse variables. For each term, a different scaling factor was used to analyze the sensitivity of the petrochemical network due to variations of each component. The study showed that the final petrochemical network bears more sensitivity to variations in product demand and process yields for scaling parameters values that maintain the final petrochemical structure obtained form the stochastic model. The concept of expected value of perfect information (EVPI) and value of the stochastic solution (VSS) are also investigated to numerically illustrate the value of including the randomness of the different model parameters. Modeling uncertainty in the process parameters provided a more robust analysis and practical perspective of this type of problem in the chemical industry. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie0713184