Fitting Sovova's mass transfer model using an evolutionary algorithm and differential evolution

Authors:
Dejan Hrncic;Marjan Mernik;Masa Knez Hrncic;Zeljko Knez
Affiliations:
Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.;Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.;Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia.;Faculty of Chemistry and Chemical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, Slovenia
Venue:
International Journal of Innovative Computing and Applications
Year:
2010

Citing 4
Cited 1

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A Fuzzy Adaptive Differential Evolution Algorithm

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To explore or to exploit: An entropy-driven approach for evolutionary algorithms

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A parameter control method of evolutionary algorithms using exploration and exploitation measures with a practical application for fitting Sovova's mass transfer model

Applied Soft Computing

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Abstract

In chemical engineering, reliable models are necessary to reduce the cost of process design. An evolutionary algorithm with resizable population was used to estimate coefficients of Sovova's mass transfer model and was compared with a global optimiser found in the literature and commonly used differential evolution algorithm. Comparison of the evolutionary algorithm to the global optimisation technique proved that the evolutionary algorithm is more robust, efficient, and significantly better than the global optimiser in regards to the deviation of the model from experimental data. It is also shown that the proposed evolutionary algorithm performed better than differential evolution algorithm.