Application of Bayesian regularization back propagation neural network in sensorless measurement of pump operational state

Under the strategic framework of global carbon emission reduction, it is urgent to reduce the energy consumption of centrifugal pumps. Monitoring pump’s operational states can effectively help people to understand system’s behavior, and achieve the optimal energy management on pump equipment. In view of installation space, cost, reliability and other factors, it is necessary to find an alternative to the traditional sensor monitoring scheme. Therefore, it is significant to develop a prediction method for the operational state of centrifugal pump using sensorless technology. In this paper, a neural network estimation model based on Bayesian regularization back propagation (BRBP) algorithm is proposed. Compared with the traditional QP estimation model, the new model has better nonlinearity and higher precision. Using power and rotational frequency as input neurons, BRBP model realizes the output of flow rate and head through neuronal calculations. By comparing the predicted flow rate and head values of a multistage centrifugal pump with the measured values at different rotational frequencies, it is found that the mean relative error of flow rate of BRBP model is in the range of 1% to 4%, and the mean relative error of head is in the range of 1% to 2%. The prediction accuracy of BRBP neural network model is obviously higher than that of QP model, which meets the requirements of industrial application.