AI-Based Induction Furnace Power Optimization System
Problem Overview
Induction furnace parameters such as power, current, voltage, temperature, charge weight and melting time are monitored separately, making it difficult to determine the optimum power profile for each heat. The challenge is to use real-time and historical heat data to recommend power adjustments, predict melting time, target temperature and energy consumption, and improve process efficiency.
Industrial Context & Background
AI/ML / Energy Optimization / Smart Foundry
Technical Requirements
1) Use real-time furnace parameters
2) Learn from historical heat data
3) Grade/charge-wise modelling
4) Operator-friendly recommendations
5) Track predicted vs actual performance
Suggested Technology Stack: Machine Learning; Time-Series Analytics; IIoT; Furnace Data Integration
Expected Solution & Prototype
1) Optimum power recommendation
2) Melting-time prediction
3) Energy-consumption prediction
4) Operator recommendations
5) Reduced energy and melting time
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