Centrifugal pumps are critical components in industrial processes and heating, ventilation, air conditioning, and cooling (HVAC) systems. To mitigate CO 2 emissions, it is essential to reduce the energy consumption of centrifugal pumps across various applications. Conventionally, valve-based flow control is widely adopted in industrial processes and HVAC systems; however, this approach results in excessive energy consumption when the flow demand is low. This study introduces a soft-sensor-based proportional pressure control method for centrifugal pumps. The prediction model integrates back-propagation neural networks (BPNN) with genetic algorithms (GA) to estimate pump flow rate and head without requiring feedback signals from flow meters or pressure transducers. Motor speed and power serve as input variables for the prediction model. A motor speed correction equation compensates for the slip effect in asynchronous motors. Furthermore, two PID control loops based on the GA-BPNN prediction model are implemented to independently adjust the valve opening and pump speed, enabling the pump to operate in a proportional pressure mode. The novelty lies in the soft-sensor for pump flow/head prediction using motor power/speed with slip compensation for the integrated control strategy. Experimental results demonstrate that the proposed soft-sensor-based control method achieves high control accuracy and reliability on pump flow and head. This new control method eliminates the need for physical sensors used in flow or pressure measurement and transmission feedback, thereby enabling precise control of the centrifugal pump’s operating state based on the performance prediction model. Compared with traditional valve-based flow control, this new strategy can achieve energy savings exceeding 23.5%.
Wu et al. (Mon,) studied this question.