Reliability analysis of pumping station for sewage network using hybrid neural networks - genetic algorithm and method of moment

Abstract

The reliability of pumping stations is of great importance for the robust design of wastewater networks. In order to strike a balance between safe design and energy consumption of the pumping system, a complex performance function using multiple-failure mode under various uncertainties is required. In the present research to evaluate the failure probability of wastewater pumping station, a hybrid reliability analysis framework using artificial neural network (ANN) coupled by moment methods is proposed. Drawing upon genetic algorithm (GA), ANN model is trained to approximate the failure domain of the pump. The ANN is approximated input variables captured by the optimal condition of the rotational speed of the pump in turn it is obtained by GA. The main strength of the reliability method is to diminish the computational burden with accurate predictions of safety margin in the pumping systems of Zabol. The machine learning-based backpropagation (BP) for training feedforward neural networks using GA and gradient methods are compared for the regressed process of ANN models. This hybrid reliability framework involves three main levels including i) the pump rotational speed is minimized using GA as optimal hydraulic parameters such as inlet flow, static head, pumping head and outlet flow rate, ii) the limit sate function is approximated using ANN for optimal rotating speed pumps and iii) reliability analysis is disused using MCS and method of the moment for sewage pumping stations located at Zabol (Iran) station. Given the results, the machine learning-based GA for training ANN model provides accurate predictions compared to the ANN-based gradient method. Three moments of reliability method is an efficient and accurate approach to evaluate the reliable conditions of the pumping system.