Abstract:
The efficient and precise control of molten steel temperature in an induction furnace is
paramount in modern steelmaking processes. This master's thesis presents a novel
approach to address this critical challenge that combines the power of Artificial
Intelligence (AI) with Computational Fluid Dynamics (CFD) to predict and regulate the
molten steel temperature accurately.
The primary objective of this study is to develop an AI-based predictive model that can
anticipate the molten steel temperature in real-time, enhancing process control, optimizing
energy consumption, and ultimately improving product quality. The research
methodology encompasses four main stages: data collection and preprocessing, AI model
selection, CFD simulation, and model validation.
The first phase focuses on creating a comprehensive CFD environment to simulate the
induction furnace's thermal behavior, incorporating thermal boundary conditions and
leveraging data augmentation techniques to generate a substantial dataset. In the second
phase, several AI models are evaluated, and the most suitable one is selected based on
performance metrics. The final phase entails training and validating the chosen AI model
using the simulated data.
The thesis introduces an AI model to address the complexities and dynamics of the
induction furnace environment. This model harnesses the power of machine learning
algorithms, enabling it to capture intricate patterns and non-linear relationships in the data,
leading to more accurate temperature predictions.
The study's results showcase the effectiveness and reliability of the proposed AI-based
predictive model, demonstrating a notable improvement in temperature prediction
accuracy compared to traditional methods. The thesis also explores real-world case
studies, validating the model's applicability and efficiency in practical steelmaking
scenarios.
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In conclusion, this research successfully establishes a novel approach for predicting
molten steel temperature in an induction furnace using AI within a CFD environment. The
outcomes offer valuable insights into the steel industry, fostering more intelligent
decision-making, reduced energy wastage, and enhanced process control. Additionally,
this work sets a foundation for further exploration of AI integration in steelmaking
processes and opens avenues for future research in related domains.
