Development of single point prediction model using artificial neural network and experimental validation for pump as turbine applications

Abstract

This paper proposed a new approach based on artificial neural networks (ANNs) to select the most appropriate centrifugal pumps for reverse operation i.e. pump as turbines (PAT), by predicting parameters at Best Efficiency Point from pump mode data. Both, exhaustive experimental data of 49 pumps (specific speeds 9−104) collected from open literature and by in-house experimentation data, considered input data-set as pump and target data-set as PAT are used for training the ANN models with two different training functions: Levenberg–Marquardt and Bayesian regularisation. The proposed ANN prediction model shows a deviation lower than 10% compared to the respective experimental value. Additionally, the trained ANN model tested with four pumps (not included in training data sets of proposed models) shows maximum absolute deviation in head number, flow number, and efficiency within the 10%, 7%, and 6% range, respectively, compared to the experimental values. Furthermore, the efficiency of reverse mode evaluated for variation in rotational speed shows a maximum of 3.3% (within 5%) absolute deviation compared to respective experimental results. Overall, the ANNs based prediction model of PAT parameters is recommended compared to conventional models available in the literature as it gives superior results (less than 10% deviation) for practical application.