Performance prediction and optimization of lateral exhaust hood based on back propagation neural network and genetic algorithm

Abstract

The lateral exhaust hood (LEH) is commonly used to capture contaminated airflow in industrial plants. Its performance is influenced by numerous factors and exhibits a strong nonlinear relationship, optimizing this performance demands significant computational time and cost. This study proposes an LEH optimization design method that combines a backpropagation neural network (BPNN) with a genetic algorithm (GA), verified through experiments and numerical simulations. A BPNN prediction model is established based on 520 CFD simulation results. This study discusses seven factors influencing LEH performance, including the buoyant jet's velocity and temperature, the LEH's geometry and position, and the exhaust velocity. The results indicate that within the parameter range of the prediction model, there is a critical aspect ratio value that significantly affects LEH capture efficiency. The horizontal distance between the LEH and the pollution source significantly affects capture efficiency, especially when the LEH's installation height is low (H/D < 0.5). In addition, when combined with GA for fast searching, the expected combination of LEH design parameters can be obtained. This study aims to enhance the design efficiency of industrial exhaust hoods and improve worker health protection.