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Stochastic modeling for uncertainty analysis and multiobjective optimization of IGCC system with single-stage coal gasification / Yogendra Shastri 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.4879-4892 Titre : Stochastic modeling for uncertainty analysis and multiobjective optimization of IGCC system with single-stage coal gasification Type de document : texte imprimé Auteurs : Yogendra Shastri, Auteur ; Urmila Diwekar, Auteur Année de publication : 2011 Article en page(s) : pp.4879-4892 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Multiobjective  optimization  Gasification Résumé : Integrated Gasification Combined Cycle (IGCC) system using coal gasification is an important component of future energy alternatives. Consequently, understanding the system operation and optimizing it in the presence of uncertain operating conditions is important. Moreover, since gasification is the most important component of the system, it is particularly critical to understand the impact of uncertainty in gasification operation on the IGCC system. This article presents research conducted to achieve these objectives. The work initially focuses on developing a computational fluid dynamics (CFD) model for the single-stage coal gasifier, which is a part of the IGCC system. The impact of varying coal composition on the gasifier operations is determined from the CFD simulations. The stochastic CFD simulation results are then compared with those for an approximate gasifier model developed in ASPEN Plus as a part of the IGCC model to characterize and quantify the gasifier operation uncertainty. The CAPE-OPEN compliant stochastic simulation capability is also used to perform deterministic and stochastic multiobjective optimization of the IGCC system. This is based on the Parameter Space Investigation (PSI) method of multiobjective optimization useful for nonconvex nonlinear surface. Stochastic modeling is also useful for identifying important decision variables for such surfaces using Partial Rank Correlation Coefficients (PRCC). DEWEY : 660 ISSN : 0888-5885 En ligne : http://www.mendeley.com/research/stochastic-modeling-uncertainty-analysis-multio [...] [article] Stochastic modeling for uncertainty analysis and multiobjective optimization of IGCC system with single-stage coal gasification [texte imprimé] / Yogendra Shastri, Auteur ; Urmila Diwekar, Auteur . - 2011 . - pp.4879-4892. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 50 N° 9 (Mai 2011) . - pp.4879-4892 Mots-clés : Multiobjective  optimization  Gasification Résumé : Integrated Gasification Combined Cycle (IGCC) system using coal gasification is an important component of future energy alternatives. Consequently, understanding the system operation and optimizing it in the presence of uncertain operating conditions is important. Moreover, since gasification is the most important component of the system, it is particularly critical to understand the impact of uncertainty in gasification operation on the IGCC system. This article presents research conducted to achieve these objectives. The work initially focuses on developing a computational fluid dynamics (CFD) model for the single-stage coal gasifier, which is a part of the IGCC system. The impact of varying coal composition on the gasifier operations is determined from the CFD simulations. The stochastic CFD simulation results are then compared with those for an approximate gasifier model developed in ASPEN Plus as a part of the IGCC model to characterize and quantify the gasifier operation uncertainty. The CAPE-OPEN compliant stochastic simulation capability is also used to perform deterministic and stochastic multiobjective optimization of the IGCC system. This is based on the Parameter Space Investigation (PSI) method of multiobjective optimization useful for nonconvex nonlinear surface. Stochastic modeling is also useful for identifying important decision variables for such surfaces using Partial Rank Correlation Coefficients (PRCC). DEWEY : 660 ISSN : 0888-5885 En ligne : http://www.mendeley.com/research/stochastic-modeling-uncertainty-analysis-multio [...]