Energy-saving oriented optimization design of the impeller and volute of a multi-stage double-suction centrifugal pump using artificial neural network

Abstract

To broaden the efficient operating zone and increase the energy efficiency of a multi-stage double-suction centrifugal pump, a multi-component and multi-condition optimization design method involving high-precision performance predictions, a flow loss visualization technique based on entropy production theory, and machine learning is proposed. First, the accuracy of the baseline pump numerical methodology is verified via a grid convergence analysis and experiments. Thereafter, nine design parameters of the impeller and double volute are selected as design variables. Subsequently, 150 designs are created according to the Latin hypercube sampling method (LHS) and numerically simulated using an automatic simulation program. A backpropagation neural network (BPNN) and a multi-objective genetic algorithm (MOGA) are adopted to maximize the efficiency at 0.6Qd, 1.0Qd, and 1.2Qd. Finally, the optimal results are verified via numerical calculations and analyzed. The results indicate that the efficiency of the optimized pump is increased by 2.05%, 3.56%, and 5.36% at 0.6Qd, 1.0Qd, and 1.2Qd, respectively. The comparative analysis of the energy characteristics reveals that the improved performance of the optimized pump can be attributed to the improved matching between the rotor and stator. This research further demonstrates the accuracy and reliability of the optimization method using an artificial neural network (ANN).

Keywords

• Energy efficiency
• multi-stage double-suction centrifugal pump
• multi-objective optimization
• artificial neural network
• entropy production