AI-Driven Monitoring and Optimizing of Striko Aluminium Melting Furnace
Keywords:
AI, Aluminium Furnace, Energy Efficiency, Real-Time Monitoring, OptimizationAbstract
This study focuses on optimizing the Striko Aluminium Melting Furnace by leveraging AI-driven predictive analytics to enhance operational efficiency and sustainability. Two machine learning models, Linear Regression (LR) and Radial Basis Function Network (RBFN), were developed to predict critical furnace parameters, including temperature, gas flow, and CO2 emissions. These models were integrated into a real-time monitoring framework featuring a dashboard for live data visualization and automated alerts to notify deviations from optimal conditions. Comparative analysis revealed the superior performance of the RBFN model, achieving higher prediction accuracy and contributing significantly to operational improvements.
The results demonstrated a 21% reduction in energy consumption, an 18% decrease in CO2 emissions, and a 4.35% increase in product yield. This study underscores the transformative potential of AI in driving energy-efficient, sustainable, and cost-effective industrial furnace operations.
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