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Prediction of cold rolling gas based on EEMD-LSTM deep learning technology

Hui Zhai (College of Electrical Engineering, North China University of Science and Technology, Tangshan, China)
Wei Xiong (College of Electrical Engineering, North China University of Science and Technology, Tangshan, China)
Fujin Li (College of Electrical Engineering, North China University of Science and Technology, Tangshan, China)
Jie Yang (HBIS GROUP Tangsteel High Strength Automotive Strip Co. Ltd, Tangshan, China)
Dongyan Su (HBIS GROUP Tangsteel High Strength Automotive Strip Co. Ltd, Tangshan, China)
Yongjun Zhang (Information Automation Department of Tangshan Iron and Steel Group Co., Ltd, Tangshan, China)

Assembly Automation

ISSN: 0144-5154

Article publication date: 1 December 2021

Issue publication date: 24 March 2022

149

Abstract

Purpose

The prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a basis for gas dispatch and reduce the production cost of enterprises.

Design/methodology/approach

In this paper, a new method using the ensemble empirical mode decomposition (EEMD) and the back propagation neural network is proposed. Unfortunately, this method does not achieve the ideal prediction. Further, using the advantages of long short-term memory (LSTM) neural network for long-term dependence, a prediction method based on EEMD and LSTM is proposed. In this model, the gas consumption series is decomposed into several intrinsic mode functions and a residual term (r(t)) by EEMD. Second, each component is predicted by LSTM. The predicted values of all components are added together to get the final prediction result.

Findings

The results show that the root mean square error is reduced to 0.35%, the average absolute error is reduced to 1.852 and the R-squared is reached to 0.963.

Originality/value

A new gas consumption prediction method is proposed in this paper. The production data collected in the actual production process is non-linear, unstable and contains a lot of noise. But the EEMD method has the unique superiority in the analysis data aspect and may solve these questions well. The prediction of gas consumption is the result of long-term training and needs a lot of prior knowledge. Relying on LSTM can solve the problem of long-term dependence.

Keywords

Acknowledgements

The authors would like to thank the Editor-in-Chief, the Associate Editor and anonymous reviewers for their valuable comments and suggestions that helped improve the quality of this manuscript. This project was supported by the Natural Science Foundation of Hebei Province (Grant Number F2018209201).

Conflicts of Interests: The authors declare that there is no conflict of interests regarding the publication of this article.

Citation

Zhai, H., Xiong, W., Li, F., Yang, J., Su, D. and Zhang, Y. (2022), "Prediction of cold rolling gas based on EEMD-LSTM deep learning technology", Assembly Automation, Vol. 42 No. 2, pp. 181-189. https://doi.org/10.1108/AA-02-2021-0018

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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