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      APLICACIÓN DEL MÉTODO DE OPTIMIZACIÓN DE RECOCIDO SIMULADO EN LA REGRESIÓN DE ISOTERMAS DE ADSORCIÓN

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          Abstract

          RESUMEN El ajuste de parámetros en modelos termodinámicos, entre ellos las isotermas de adsorción, es un problema multivariable y no lineal que puede presentar diversos óptimos locales. Generalmente los métodos de optimización utilizados en el ajuste de isotermas de adsorción son eficientes pero poco robustos. El objetivo de este trabajo es la aplicación del método estocástico de optimización global de recocido simulado en la regresión no lineal de modelos para isotermas de adsorción. Este método ha sido utilizado en el ajuste de datos experimentales empleando las isotermas de Toth y Sips. Los resultados obtenidos indican que dicho método es más confiable que las estrategias convencionales utilizadas en la obtención de los parámetros de las isotermas de adsorción.

          Translated abstract

          ABSTRACT The nonlinear parameter estimation of thermodynamic models, including the adsorption isotherms, is a multivariable and nonlinear problem that can present several local optimums. The conventional optimization methods used in the parameter estimation of adsorption isotherms are efficient but they are not reliable to solve such problem. In this paper, we describe the application of the stochastic global optimization method simulated annealing in the nonlinear regression of adsorption isotherms. This method has been applied with Toth and Sips models. Our results indicate that this method is a reliable optimization strategy for parameter estimation in adsorption isotherms.

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          Global optimization of statistical functions with simulated annealing

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            Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithm

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              • Record: found
              • Abstract: not found
              • Article: not found

              Adsorption of Benzene and Toluene from Aqueous Solution onto Granular Activated Carbon

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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Role: ND
                Journal
                rica
                Revista internacional de contaminación ambiental
                Rev. Int. Contam. Ambient
                Centro de Ciencias de la Atmósfera, UNAM (México, DF, Mexico )
                0188-4999
                December 2005
                : 21
                : 4
                : 201-206
                Affiliations
                [1] Aguascalientes Aguascalientes orgnameInstituto Tecnológico de Aguascalientes orgdiv1Departamento de Ingeniería Química Mexico
                Article
                S0188-49992005000400201
                0ecc115f-40b2-4547-af80-3b69e2f3698c

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

                History
                : August 2005
                : October 2005
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 16, Pages: 6
                Product

                SciELO Mexico


                adsorption,recocido simulado,regresión no lineal de parámetros,adsorción,simulated annealing,nonlinear regression

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