Research on the Prediction Method of Centrifugal Pump Performance Based on a Double Hidden Layer BP Neural Network

• Published in: Energies (Basel) Vol. 12; no. 14; p. 2709
• Main Authors: Han, Wei, Lingbo Nan, Su, Min, Chen, Yu, Li, Rennian, Zhang, Xuejing
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 15.07.2019
• Subjects: Accuracy; Algorithms; Cavitation; centrifugal pump; Centrifugal pumps; Convergence; Desalination; Design; Design parameters; double hidden layer; Efficiency; Experimental methods; Fault diagnosis; Fault tolerance; Flow rates; Flow velocity; Hydraulics; Impellers; Levenberg–Marquardt algorithm; Neural networks; Optimization; Performance prediction; Prediction models; R&D; Regularization methods; Research & development; Research methodology; Simulation; China
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en12142709

With the aim of improving the shortcomings of the traditional single hidden layer back propagation (BP) neural network structure and learning algorithm, this paper proposes a centrifugal pump performance prediction method based on the combination of the Levenberg–Marquardt (LM) training algorithm and double hidden layer BP neural network. MATLAB was used to establish a double hidden layer BP neural network prediction model to predict the head and efficiency of a centrifugal pump. The average relative error of the head between the experimental and prediction obtained by the double hidden layer BP neural network model was 4.35%, the average relative error of the model prediction efficiency and the experimental efficiency was 2.94%, and the convergence time was 1/42 of that of the single hidden layer. The double hidden layer BP neural network model effectively solves the problems of low learning efficiency and easy convergence into local minima—issues that were common in the traditional single hidden layer BP neural network training. Furthermore, the proposed model realizes hydraulic performance prediction during the design process of a centrifugal pump.