Artificial neural network based modeling of performance characteristics of deep well pumps with splitter blade

Experimental studies were made to investigate the effects of splitter blade length (25%, 35%, 50%, 60% and 80% of the main blade length) on the pump characteristics of deep well pumps for different blade numbers (z = 3, 4, 5, 6 and 7). In this study, an artificial neural network (ANN) was used for modeling the performance of deep well pumps with splitter blades. Two hundred and ten experimental results were used to train and test. Forty-two patterns have been randomly selected and used as the test data. The main parameters for the experiments are the blade number (z  ), non-dimensional splitter blade length (L¯), flow rate (Q, l/s), head (Hm, m), efficiency (η, %) and power (Pe, kW). z  , L¯ and Q have been used as the input layer, and Hm and η have also been used as the output layer. The best training algorithm and number of neurons were obtained. Training of the network was performed using the Levenberg–Marquardt (LM) algorithm. To determine the effect of the transfer function, different ANN models are trained, and the results of these ANN models are compared. Some statistical methods; fraction of variance (R2) and root mean squared error (RMSE) values, have been used for comparison.