Temperature and key element content prediction based on BP neural network optimized by genetic algorithm

Ziheng Zhu; Jialu He; Zebin Xing

doi:10.54097/hset.v58i.10060

Authors

Ziheng Zhu
Jialu He
Zebin Xing

DOI:

https://doi.org/10.54097/hset.v58i.10060

Keywords:

Polynomial fitting, BP neural network, Cross experiment, genetic algorithm.

Abstract

In the process of metal smelting, the precise control of temperature and key element content has important significance in improving the performance of metal smelting. This paper uses the values of 2048 light intensity to predict the flame temperature and the content of the key elements in the raw materials in real time.First, Feature extraction of optical information data based on polynomial fitting model is established to find the characteristics of optical information data, and then these features will be extracted.Then, establish the prediction model based on the BP neural network to probe the relationship between Kelvin temperature T and key element content C.

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