Development of an intelligent system for real-time detection of slab and billet rupture in continuous casting machines using artificial intelligence algorithms

Hossein Alirezaei ¹ ( Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. )
Sayed Mohammadali Zanjani ² ( Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran. )

Submited date : 2024-10-12 Accepted date : 2024-12-06

Keywords: Continuous casting machine, billet breakout, electronic detection system, breakout detection, Grey Wolf Optimizer (GWO), Support Vector Machines (SVM).,

Abstract :

 In continuous casting machines, slab and billet breakout is a significant operational challenge that leads to severe equipment damage and production downtime. This phenomenon occurs when the slab or billet shell ruptures in the early stages of casting, causing the molten metal inside to spill out and adhere to the machine components such as rollers and bearings. This paper proposes a smart system to mitigate the damages caused by this event. The Grey Wolf Optimizer (GWO) algorithm is employed to optimize the selection of wire materials used in the detection system, ensuring resistance to high temperatures and harsh environmental conditions. These wires, with the ability to withstand temperatures up to 1000°C, high tensile strength, and minimal creep, are installed between the rollers. Additionally, for real-time intelligent fault detection, Support Vector Machines (SVM) are used. Upon breakout, the system detects a grounding signal through a PLC module and immediately sends alerts to operators, preventing further damage. The proposed system provides high accuracy and reliability in demanding operational conditions by integrating AI and optimization techniques

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