Analysis and optimization of oil injection parameters for screw compressors based on RSM and NSGA-II

Abstract

Oil-injected screw compressors use oil-injected lubrication and sealing. Compared with dry screw compressors, they reduce costs and improve performance and have been widely applied in the refrigeration and industrial gas fields. Firstly, a comprehensive mathematical model of the oil-injected twin-screw compressor was constructed, and an experimental platform for performance testing of the oil-injected twin-screw air compressor was established. The mathematical model was verified, and the accuracy of the simulation results was verified. Subsequently, the response surface models of oil injection volume, oil injection temperature, oil injection port position and back pressure were established by using the Box-Behnken method, and the interactions of oil injection volume, oil injection temperature, oil injection port position and back pressure with volumetric efficiency, adiabatic efficiency and specific power were analyzed. The results show that optimizing volumetric efficiency and adiabatic efficiency merely by adjusting the oil injection volume is contradictory. When optimizing performance, the oil injection temperature should be reduced as much as possible. Finally, the oil injection parameters of the oil-injected screw compressor were optimized by using the non-dominated sorting multi-objective genetic algorithm (NSGA-II), and the optimal oil injection parameters under different rotational speeds were obtained using the entropy-weighted TOPSIS (E-TOPSIS) method. The average volumetric efficiency of the optimized compressor has increased by 2.76%, the average adiabatic efficiency has increased by 3.39%, and the average specific power has decreased by 3.28%. This method effectively improves the performance of the twin-screw compressor.