Prediction of Air Leakage Rate of Sintering Furnace Based on BP Neural Network Optimized by PSO

Abstract

Aiming at the difficulty of air leakage detection in the sintering process of the sintering furnace, especially the problems of high detection cost and poor timeliness of detection results when traditional methods are used for detection, we propose an air leakage rate prediction algorithm. Firstly, we use the particle swarm optimization algorithm to optimize the initial parameters of the neural network based on back propagation and get the best set of initial parameters through continuous search. Secondly, the optimized parameters are substituted into the neural network to train them with training data, and the trained parameters are obtained. Finally, the air leakage rate of the test set data is predicted by using the trained parameters. Compared with traditional calculation methods such as gas analysis and calorimetry, the proposed method can greatly simplify the detection process, shorten the detection time, and control the error within 5%, allowing the user to deal with the air leakage problem more timely and improve the overall sintering quality.