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Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search

Lookup NU author(s): Professor Qiangda Yang, Dr Jie Zhang

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

This article presents a hybrid model for predicting the temperature of molten steel in a ladle furnace (LF). Unique to the proposed hybrid prediction model is that its neural network-based empirical part is trained in an indirect way since the target outputs of this part are unavailable. A modified cuckoo search (CS) algorithm is used to optimize the parameters in the empirical part. The search of each individual in the traditional CS is normally performed independently, which may limit the algorithm’s search capability. To address this, a modified CS, information interaction-enhanced CS (IICS), is proposed in this article to enhance the interaction of search information between individuals and thereby the search capability of the algorithm. The performance of the proposed IICS algorithm is first verified by testing on two benchmark sets (including 16 classical benchmark functions and 29 CEC 2017 benchmark functions) and then used in optimizing the parameters in the empirical part of the proposed hybrid prediction model. The proposed hybrid model is applied to actual production data from a 300 t LF at Baoshan Iron & Steel Co. Ltd, one of China's most famous integrated iron and steel enterprises, and the results show that the proposed hybrid prediction model is effective with comparatively high accuracy.


Publication metadata

Author(s): Yang Q, Fu Y, Zhang J

Publication type: Article

Publication status: Published

Journal: Neural Computing and Applications

Year: 2021

Volume: 33

Pages: 6487-6509

Print publication date: 01/06/2021

Online publication date: 23/10/2020

Acceptance date: 05/10/2020

Date deposited: 16/12/2020

ISSN (print): 0941-0643

ISSN (electronic): 1433-3058

Publisher: Springer

URL: https://doi.org/10.1007/s00521-020-05413-5

DOI: 10.1007/s00521-020-05413-5


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