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

Łukasz Sztangret, Krzysztof Regulski, Monika Pernach, Łukasz Rauch

Coatings 2023 article cited by 11

Source Open-access PDF More in Engineering & Materials

importance 4/10 ai assisted Metallurgical Processes and Thermodynamics steel manufacturing stage: research

Problem

Measuring liquid steel temperature in the ladle is challenging, expensive, and unsafe in industrial settings.

Why it matters

Ladle temperature control affects billet quality, energy consumption, and smooth melting sequence control.

What the AI did

Machine-learning models (decision trees, linear regression, neural networks) combined with FEM solutions of the Fourier equation predicted liquid steel cooling rate from production parameters.

Results

Simulations verified against production-line measurements; no numeric accuracy stated.

Impact

Models were verified against real production measurements, supporting cooling-rate prediction.

Record

Venue	Coatings
Published	2023
Work type	article
Authors	Łukasz Sztangret, Krzysztof Regulski, Monika Pernach, Łukasz Rauch
Citations	11 (OpenAlex, at import time)
References	16
Model or method	decision trees, linear regression, artificial neural networks
Organization	not stated in source
Primary source	https://doi.org/10.3390/coatings13091504
Open access	gold
Keywords	liquid steel, cooling rate, machine learning, FEM, ladle

Importance rating

4 / 10

real but narrower advance

Ladle temperature control affects billet quality, energy use, and melting sequence at steel plants; a real industrial advance but no scale or industry-wide impact stated.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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