Modeling of oxygen mass transfer in the presence of oxygen-vectors using neural networks developed by differential evolution algorithm

Authors:
Elena-Niculina Dragoi;Silvia Curteanu;Florin Leon;Anca-Irina Galaction;Dan Cascaval
Affiliations:
"Gheorghe Asachi" Technical University Iasi, Department of Chemical Engineering, Bd. Mangeron, No. 71A, 700050 Iasi, Romania;"Gheorghe Asachi" Technical University Iasi, Department of Chemical Engineering, Bd. Mangeron, No. 71A, 700050 Iasi, Romania;"Gheorghe Asachi" Technical University Iasi, Department of Computer Science and Information Technology, Bd. Mangeron, No. 53A, 700050 Iasi, Romania;"Grigore T Popa" University of Medicine and Pharmacy Iasi, Department of Biotechnology, Street Mihail Kogalniceanu, No. 9-13, 700454 Iasi, Romania;"Gheorghe Asachi" Technical University Iasi, Department of Chemical Engineering, Bd. Mangeron, No. 71A, 700050 Iasi, Romania
Venue:
Engineering Applications of Artificial Intelligence
Year:
2011

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Abstract

The search capabilities of the Differential Evolution (DE) algorithm - a global optimization technique - make it suitable for finding both the architecture and the best internal parameters of a neural network, usually determined by the training phase. In this paper, two variants of the DE algorithm (classical DE and self-adaptive mechanism) were used to obtain the best neural networks in two distinct cases: for prediction and classification problems. Oxygen mass transfer in stirred bioreactors is modeled with neural networks developed with the DE algorithm, based on the consideration that the oxygen constitutes one of the decisive factors of cultivated microorganism growth and can play an important role in the scale-up and economy of aerobic biosynthesis systems. The coefficient of mass transfer oxygen is related to the viscosity, superficial speed of air, specific power, and oxygen-vector volumetric fraction (being predicted as function of these parameters) using stacked neural networks. On the other hand, simple neural networks are designed with DE in order to classify the values of the mass transfer coefficient oxygen into different classes. Satisfactory results are obtained in both cases, proving that the neural network based modeling is an appropriate technique and the DE algorithm is able to lead to the near-optimal neural network topology.