Optimization prediction and experimental verification of cyclone performance based on BP neural network

Abstract

As a kind of centrifugal separation equipment, hydrocyclone has developed rapidly with the advantages of high economic benefits, good compactness, high processing efficiency, simple process and so on. Hydrocyclone has broad practical significance and application prospects in chemical, light industry, oil mining and refining, environmental protection, medicine, food processing, ship transportation and surface oil spill treatment. BP neural networks are mainly used in the following four aspects: function approximation, pattern recognition, classification, and data compression. In this paper, we introduce the principle of BP neural network, establish an analytical model, apply BP neural network to predict the performance of hydrocyclone, and conduct experimental verification to optimize the hydrocyclone under different working conditions according to the experimental results.