Applying metamodels and sequential sampling for constrained optimization of process operations

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dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria Química
dc.contributor	Universitat Politècnica de Catalunya. CEPIMA - Center for Process and Environment Engineering
dc.contributor.author	Shokry, Ahmed F.
dc.contributor.author	Espuña Camarasa, Antonio
dc.date	2014
dc.identifier.citation	Shokry, A.; Espuña, A. Applying metamodels and sequential sampling for constrained optimization of process operations. A: International Conference on Artificial Intelligence and Soft Computing. "Lecture Notes in Computer Science". Zakopane: 2014, p. 396-407.
dc.identifier.citation	978-331907175-6
dc.identifier.citation	10.1007/978-3-319-07176-3_35
dc.identifier.uri	http://hdl.handle.net/2117/23533
dc.language.iso	eng
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Enginyeria química
dc.subject	Chemical industry
dc.subject	Mathematical models
dc.subject	Adaptive Sampling
dc.subject	Kriging
dc.subject	Optimization
dc.subject	Process Operation
dc.subject	Surrogate Models
dc.subject	Indústria química
dc.subject	Models matemàtics
dc.title	Applying metamodels and sequential sampling for constrained optimization of process operations
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	This paper presents a framework for nonlinear constrained optimization of complex systems, in which the objective function and the constraints are represented by black box functions. The proposed approach replaces the complex nonlinear model based on first principles with Kriging metamodels. Coupled to Kriging, the
dc.description.abstract	Peer Reviewed

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