Parameter optimization of unbaffled circular surface aeration tank

The efficiency of the surface aeration systems is generally governed by the geometric and dynamic parameters. The geometry is important because successful translation of the laboratory finding can be scaled up to field installations. Experimental optimization of the geometrical parameters (classical approach of one parameter variations at a time) has certain limitations, because it assumes a linear relationship among the various geometric parameters. In the real experimental process, it is not possible to vary all the 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 in unbaffled circular surface aerators: 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 blade width of the rotor is the most significant geometric parameter for the aeration process.