Predicting Metallurgical Length in Continuous Casting Using Machine Learning

This study investigates the application of machine learning, specifically Support Vector Machines (SVM), to predict the metallurgical length in continuous casting. The metallurgical length, defined as the distance from the molten metal surface to the point of complete solidification, significantly impacts product quality. Traditional methods for predicting metallurgical length, such as the K-factor model and numerical simulations, face limitations in accuracy, computational cost, and adaptability to real-time industrial applications. To address these limitations, this study proposes a novel approach using Support Vector Machines (SVM), a machine learning algorithm, to predict metallurgical length with high precision. Numerical simulations were conducted to model fluid flow, heat transfer, and solidification processes, validated against experimental data. The SVM model was trained on metallurgical length data derived from simulations at various casting speeds. Results demonstrated that the SVM model achieved a mean square error (MSE) of ۰.۰۷۸۹ compared to numerical data, significantly outperforming empirical methods (MSE = ۰.۵۳۵۳). The study highlights the potential of machine learning to enhance real-time decision-making in continuous casting, offering a computationally efficient and accurate alternative to traditional methods. This approach can be extended to analyze other process parameters, such as cooling water flow rate and initial superheat temperature, further optimizing steel production.

کلیدواژه ها:

continouse casting ، suppor vector Machines ، numerical simulation