Process Parameter Optimization

AI adjusts production settings in real time or through process models to improve yield, productivity, and product quality while reducing resource use.

Business impact

• Yield — AstraZeneca uses process digital twins to optimize manufacturing conditions, including yield.
• Productivity — AstraZeneca uses process digital twins to optimize manufacturing conditions, including productivity.
• Raw-material use — AstraZeneca reports reduced raw-material use from the optimization work.
• Throughput — CEMEX’s AI solution is described as improving throughput at the Balcones plant.
• Energy consumption — CEMEX’s AI solution is described as reducing energy consumption.
• CO₂ emissions — CEMEX’s AI solution is described as reducing CO₂ emissions.
• Wafer quality attributes — Nestlé detects wafer quality attributes and adjusts the process in response.

Data requirements

• Manufacturing conditions — AstraZeneca uses process conditions as inputs to the digital twins.
• Process and quality signals — Nestlé detects wafer quality attributes and adjusts the process in response.
• Plant operations data — CEMEX applies the system to optimize cement operations in real time.

AI methods and techniques

• Process digital twins — AstraZeneca uses process digital twins to optimize manufacturing conditions.
• Closed-loop control — Nestlé uses self-regulating control loops that adjust the process in response to wafer quality attributes.
• Real-time optimization — CEMEX’s AI solution optimizes cement operations in real time.

Industries

Real-world evidence

3 documented case studies on record.

Companies using this: AstraZeneca, Cemex, Nestlé.