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During the global energy crisis, it is essential to improve the energy efficiency of pumps by adjusting the pump's control strategy according to the operational states. However, monitoring the pump's operational states with the help of external sensors brings both additional costs and risks of failure. This study proposed an interpolation method based on PN curves (power–speed curves) containing information regarding motor shaft power, speed, and flow rate to achieve high accuracy in predicting the pump's flow rates without flow sensors. The impact factors on the accuracy of the estimation method were analyzed. Measurements were performed to validate the feasibility and robustness of the PN curve interpolation method and compared with the QP and back-propagation neural network (BPNN) methods. The results indicated that the PN curve interpolation method has lower errors than the other two prediction models. Moreover, the average absolute errors of the PN curve interpolation method in the project applications at 47.5 Hz, 42.5 Hz, 37.5 Hz, and 32.5 Hz are 0.1442 m3/h, 0.2047 m3/h, 0.2197 m3/h, and 0.1979 m3/h. Additionally, the average relative errors are 2.0816%, 3.2875%, 3.6981%, and 2.9419%. Hence, this method fully meets the needs of centrifugal pump monitoring and control. [ABSTRACT FROM AUTHOR]

Pumps consume nearly 8% of global electricity as the essential equipment for liquid transportation. A practical method for improving centrifugal pump energy efficiency is accurately predicting and controlling the pump operation status. However, current estimation methods for sensorless flow rate prediction have a significant error at low flow rate conditions. This study adds valve opening as the estimation model input variable, including motor shaft power and speed, to form a back-propagation neural network (BPNN) on an asynchronous motor-driven multistage centrifugal pump. By optimizing the initial weights and thresholds of BPNN, a GA-BPNN model was proposed to improve the prediction accuracy by using a genetic algorithm (GA). The results indicate that, with the addition of the valve opening as an input variable, the accuracy of BPNN-VO and GA-BPNN prediction improves significantly more than BPNN-NVO. Furthermore, the GA-BPNN model produces a significantly lower mean square error (MSE) and root mean square error (RMSE) than the original BPNN model. According to the experimental comparison and analysis, the absolute error of GA-BPNN between predicted flow rate and measured flow rate is less than 0.3 m3/h, the average relative error is less than 2%, and the relative error of low flow rate is less than 5%. This GA-BPNN estimation model significantly improves the accuracy of flow rate prediction, especially at small flow rates, and extends the scope of centrifugal pumps' monitoring and control technology without flow sensors. [ABSTRACT FROM AUTHOR]