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.
Highlights
Ladle furnace (LF) is a pivotal equipment utilized to fully refine and alloy during secondary metallurgy processes in iron and steel industries [1]
In the proposed hybrid prediction model, two single-hidden layer feed-forward neural networks (SLFNs)-based empirical models are incorporated within the structure of a mechanistic thermal model, to represent the unknown functions in the mechanistic thermal model
The primary difference between the proposed hybrid prediction model and existing ones is that its empirical part is not trained in the traditional direct way since the target outputs of the two empirical models are unavailable in advance
Summary
Ladle furnace (LF) is a pivotal equipment utilized to fully refine and alloy during secondary metallurgy processes in iron and steel industries [1]. This allows the empirical part being trained indirectly without having its target outputs. It is noteworthy that the concrete expression of this function is hard to derive by mechanistic approaches
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