<![CDATA[The power usage in mass transfer operations is very important in judging the aeration performance of the aerator on which geometry of the aeration tank imparts a major effect. Optimal geometric conditions are also needed to scale up the laboratory result to the field installation. Finding geometrical optimal conditions of a surface aeration system through experiments involves physical constraints and classically parameters can be optimized by varying one variable at one time and keeping others at constant. In the real experimental process, it is not possible to vary all others geometric parameters simultaneously. In such a case, the model of the system is built through computer simulation, assuming that the model will result in adequate determination of the optimum conditions for the real system. In this paper, functional model of power consumption in the surface aeration systems has been obtained by using neural network technique. Predictability capability of such functional model has been found satisfactorily. In process of optimization, the pertinent dynamic parameter is divided into a finite number of segments over the entire range of observations. For each segment of the dynamic parameter, the neural network model is optimized for the geometrical parameters spanning over the entire range of observations. © 2010 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0)]]>
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