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

Authors

DOI:

https://doi.org/10.15587/1729-4061.2015.55482

Keywords:

glass furnace, regenerator, checker, refractory, prediction, neural networks, activation function

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.

Author Biographies

Артем Александрович Мигура, National Technical University «KPI» Frunze 21, Kharkiv, Ukraine, 61002

Jr. Researcher

Александр Вадимович Кошельник, IPMash NAS of Ukraine Str. Dm. Pozharskogo, 2/10, Kharkiv, Ukraine, 61046

Docent

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Published

2015-12-25

How to Cite

Мигура, А. А., & Кошельник, А. В. (2015). Development and analysis of neural networks to predict the efficiency parameters of regenerator checker of glass furnace. Eastern-European Journal of Enterprise Technologies, 6(8(78), 29–33. https://doi.org/10.15587/1729-4061.2015.55482

Issue

Vol. 6 No. 8(78) (2015): Energy-saving technologies and equipment

Section

Energy-saving technologies and equipment

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Copyright (c) 2015 Артем Александрович Мигура, Александр Вадимович Кошельник

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