Energy performance prediction of the centrifugal pumps by using a hybrid neural network

It is of great significance to rapidly and accurately predict the energy performance of centrifugal pumps for the macro-control of the entire electric power system. However, some challenges are encountered, for example, the numerical simulation requires huge computing resources and calculating time, the theoretical loss model needs to improve the prediction accuracy, etc. Based on the multiple geometrical parameters and operation conditions, a hybrid neural network is proposed to predict the energy performance (i.e. the head, power and efficiency) of centrifugal pumps, where the theoretical loss model is incorporated into the back propagation neural network and then the neural network structure is optimized by automatically determining the node number of hidden layers. When compared with the experiments, the energy performance is well predicted by using the hybrid neural network with the mean-square-error (MSE) for the head, power and efficiency of 0.0062, 8.4E-4, 0.020, respectively. Besides, by considering the theoretical loss model, the hybrid neural network demonstrates a dramatic decrease in the head MSE and the efficiency MSE when compared with the original neural network. Furthermore, the hybrid neural network performs much better than the traditional linear regression in a wide flow-rate range for multiple centrifugal pumps.