Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search
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ORIGINAL ARTICLE
Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search Qiangda Yang1,2 • Yichuan Fu3 • Jie Zhang2 Received: 19 November 2019 / Accepted: 5 October 2020 The Author(s) 2020
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. Keywords Hybrid modeling Cuckoo search Artificial neural networks Molten steel temperature Ladle furnace
1 Introduction Ladle furnace (LF) is a pivotal equipment utilized to fully refine and alloy during secondary metallurgy processes in iron and steel industries [1]. Close control of the temperature of molten steel in LF is vital for the improvement of product quality and productivity [2]. However, the temperature of molten steel cannot be continuously measured in the actual production, which makes it difficult to achieve accurate control. Therefore, it has considerable practical significance to develop a model to predict the temperature of molten steel in LF.
& Jie Zhang [email protected] 1
School of Metallurgy, Northeastern University, Shenyang 110819, China
2
School of Engineering, Merz Court, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
3
Faculty of Engineering and Environment, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
Models for predicting the temperature of molten steel in LF are traditionally developed based on thermodynamics and the energy conservation law [3, 4]. However, due to the intrinsic complicacy of LF metallurgy processes, the fundamental mechanisms of involved physicochemical phenomena are not entirely clear by far, and developing a mechanistic prediction model is very time-consuming and costly. As a result, empirical modeling approaches have been exte
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