Cyber-physical System Supporting the Production Technology of Steel Mill Products Based on Ladle Furnace Tracking and Sensor Networks

Hajder, Piotr; Opaliński, Andrzej; Pernach, M.; Sztangret, Łukasz; Regulski, K.; Bzowski, Krzysztof; Piwowarczyk, Michał; Rauch, Ł.

doi:10.1007/978-3-031-36030-5_36

Cited by 2 publications

(1 citation statement)

References 12 publications

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“…The models have been verified during ongoing production and their effectiveness has been highly rated. The results of the analyses based on the monitoring system's indications have not yet been published, however, problems related to the system's implementation have already been the subject of publication [17]. A full evaluation will be possible when the system has been in operation for a longer period of time and will certainly be the subject of a separate publication.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Temperature of Liquid Steel in Ladle Using Machine Learning Techniques

Sztangret,

Regulski,

Pernach

et al. 2023

Coatings

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Maintaining the temperature of liquid steel in the ladle in the required range affects the quality of casted billets, reduces energy consumption, and guarantees smooth control of the melting sequence. Measuring its temperature is a challenging task in industrial settings, often hindered by safety concerns and the expensive nature of equipment. This paper presents models which enable the prediction of the cooling rate of liquid steel for variable production parameters, i.e., steel grade and weight of melt. The models were based on the FEM solution of the Fourier equation, and machine learning approaches such as decision trees, linear regression, and artificial neural networks are utilized. The parameters of the model were identified using data from the monitoring system and inverse analysis. The results of simulations were verified with measurements performed in the production line.

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Section: Discussionmentioning

confidence: 99%

Prediction of Temperature of Liquid Steel in Ladle Using Machine Learning Techniques

Sztangret,

Regulski,

Pernach

et al. 2023

Coatings

View full text Add to dashboard Cite

show abstract

Optimizing Continuous Casting through Cyber–Physical System

Regulski,

Rauch,

Hajder

et al. 2024

Processes

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This manuscript presents a model of a system implementing individual stages of production for long steel products resulting from rolling. The system encompasses the order registration stage, followed by production planning based on information about the billet inventory status, then offers the possibility of scheduling orders for the melt shop in the form of melt sequences, manages technological knowledge regarding the principles of sequencing, and utilizes machine learning and optimization methods in melt sequencing. Subsequently, production according to the implemented plan is monitored using IoT and vision tracking systems for ladle tracking. During monitoring, predictions of energy demand and energy consumption in LMS processes are made concurrently, as well as predictions of metal overheating at the CST station. The system includes production optimization at two levels: optimization of the heat sequence and at the production level through the prediction of heating time. Optimization models and machine learning tools, including mainly neural networks, are utilized. The system described includes key components: optimization models for sequencing heats using Ant Colony Optimization (ACO) algorithms and neural network-based prediction models for power-on time. The manuscript mainly focuses on process modeling issues rather than implementation or deployment details. Machine learning models have significantly improved process efficiency and quality; the optimization of planning has reduced sequencing plan execution time; and power-on time prediction models estimate the main ladle heating time with 97% precision, enabling precise production control and reducing overheating. The system serves as an example of implementing the concept of a cyber–physical system.

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Cyber-physical System Supporting the Production Technology of Steel Mill Products Based on Ladle Furnace Tracking and Sensor Networks

Cited by 2 publications

References 12 publications

Prediction of Temperature of Liquid Steel in Ladle Using Machine Learning Techniques

Prediction of Temperature of Liquid Steel in Ladle Using Machine Learning Techniques

Optimizing Continuous Casting through Cyber–Physical System

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