Optimization of vane demister based on neural network and genetic algorithm

Abstract

A vane demister is widely used for separating tiny droplets from gas streams in the petroleum industry, chemical engineering, and other industries. To obtain optimal structure and operation parameters, a method based on orthogonal experiment design is often adopted. However, in most cases, results from an orthogonal experiment design are suboptimal solutions when there are fewer experiments to optimize the vane demister performance. In this study, to obtain the maximum separation efficiency and minimum pressure drop, Fluent software was used to simulate the two-phase flow of gas and liquid in vane demister with different structural parameters and operation parameters, generating 473 solutions as the sample database. Based on this database, a back propagation neural network was used to establish the prediction model for the separation efficiency and pressure drop, and a genetic algorithm was used for multi-target optimization of this model. The optimization results were compared to Fluent simulation results and the orthogonal experiment design results. The results show that a genetic algorithm generates better results. The optimal separation efficiency of both methods is 100%. However, the optimal pressure drop of the genetic algorithm is 25.77% lower than that of the orthogonal experiment design.