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

Yang, Qiangda, Fu, Yichuan and Zhang, Jie (2021) Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search. Neural Computing and Applications, 33 (12). pp. 6487-6509. ISSN 0941-0643

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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.

Item Type: Article
Additional Information: Funding information: This work was supported by the Fundamental Research Funds for the Central Universities (Grant Number N2025032), the Liaoning Provincial Natural Science Foundation (Grant Number 2020-MS-362), and the National Key Research and Development Program of China (Grant Number 2017YFA0700300). The first author would also like to thank China Scholarship Council.
Uncontrolled Keywords: Hybrid modeling, Cuckoo search, Artificial neural networks, Molten steel temperature, Ladle furnace
Subjects: G400 Computer Science
G600 Software Engineering
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Mathematics, Physics and Electrical Engineering
Depositing User: Elena Carlaw
Date Deposited: 23 Dec 2021 14:02
Last Modified: 23 Dec 2021 14:15

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