A Data-Driven Computational Framework for Ledge Morphology Prediction in Aluminum Engineering Systems
Keywords:
Engineering Systems, Intelligent Process Control, Aluminum Smelting, Ledge Dynamics, Python SimulationAbstract
In complex engineering systems like the Hall-Héroult process, sidewall ledge stability is crucial for thermal balance and aluminum reduction cell longevity. Traditional physics-based approaches such as Finite Element Analysis (FEA) are computationally intensive and operationally complex, limiting real-time decision-making. This paper presents a novel data-driven computational framework that bridges the gap between high-latency simulations and the need for rapid operational diagnostics. The Python-based lightweight tool leverages historical operational data to predict ledge evolution using only the cell’s operational age as temporal input. By integrating validated empirical algorithms, the system accurately forecasts electrolyte temperature, cryolite ratio, and ledge area. Rigorous train-test split validation on industrial data confirms the model’s robustness and generalization capability, achieving high coefficients of determination (R²) on unseen data. This work demonstrates how lightweight, data-driven applications can enhance process diagnostics in heavy engineering industries, offering a fast (<10s), accessible, and reliable alternative to traditional models, aligning with Industry 4.0 principles in metal smelting.
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