Process Parameter Optimization

Sensor data and historical logs predict and adjust manufacturing parameters to boost throughput and cut energy use.

Business impact

• Throughput increase by 15% — Optimized parameters enable higher production volume without additional resources
• Energy consumption reduction by 11% — Real-time adjustments lower energy waste during manufacturing processes

Data requirements

• Sensor telemetry from blast furnaces (Numeric) — Provides real-time operational data for parameter prediction
• Historical process logs (Structured) — Used to train predictive models on past parameter-performance relationships
• Operator input and feedback (Text) — Incorporated to refine model recommendations and ensure practical applicability

AI methods and techniques

• Predictive AI — Forecasts optimal process parameters based on current and historical data
• Agentic AI — Autonomously adjusts parameters in real time to maintain optimal conditions

AI models and model families

GPT-4o, Llama, Claude

Industries

Real-world evidence

2 documented case studies on record.

Companies using this: CITIC Pacific Special Steel, Hengtong Alpha Optic Electric.