Development and analysis of neural networks to predict the efficiency parameters of regenerator checker of glass furnace

Abstract

The basic modern types of checkers and refractory materials for regenerative heat exchangers of glass furnaces were described. Operating features of using refractory materials of the checker depending on the purpose and the height of the regenerator design were given. To solve the inverse problem of predicting the regenerator parameters and classifying the refractory material of the checker depending on the coolant temperature at the checker flue outlet, the methods of neural network programming were applied, and a neural network based on multi-layer perceptron was created. The structure of this neural network was analyzed, the patterns of using different types of activation functions to solve various prediction problems were identified. The advantages of neural network models for the successful solution of prediction problems and classification of parameters of regenerative heat exchangers compared to existing finite-difference methods used for solving non-stationary problems of complex heat transfer in the checker were revealed.