doi:10.3808/jei.200700092
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Square Surface Aerator: Process Modeling and Parameter Optimization

A. R. K. Rao* and B. Kumar

Department of Civil Engineering, Indian Institute of Science, Bangalore 560012, India

*Corresponding author. Email: ark@civil.iisc.ernet.in

Abstract


The performance of an aeration system generally depends upon the geometric and dynamic parameters. The main purpose of doing experiment in the area of surface aeration system is to interpret the laboratory results for the field application that is to scale-up the results. This requires a geometrical similarity conditions. 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 a time and keeping others as constants. 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, two approaches have been used to model the phenomena: i) Multiple regression and ii) Neural network. It has been found that neural network approach is showing better predictability compared to the multiple regression approach. 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. Thus each segment of the dynamic parameter has its set of optimal geometrical conditions. Results obtained are having less variation among them and they are very nearer to the experimental optimal conditions. Input parameter significance test of neural network model reveals that, in general the blade width of rotor is a major geometric parameter to enhance the aeration process.

Keywords: Dynamic similarity, geometric similarity, multiple regressions, neural network, prediction error surface, surface aerator


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