AI in Industrial Process Optimisation
Overview
Industrial processes generate huge amounts of data that often go unused. This course shows engineers how AI can turn that data into better yields, lower energy use and more stable operations, covering soft sensors, advanced control support, optimisation and the practical steps to deploy AI in a plant safely.
Who should attend
Process and production engineers, control engineers, plant managers, and operations improvement teams.
Training methodology
Interactive presentations, live demonstrations of AI tools, hands-on exercises using participants' own work examples, case studies, group discussion, and a personal action plan.
Objectives
By the end of this course, participants will be able to:
- Identify process optimisation opportunities using data
- Understand AI models for process prediction and control
- Apply AI to improve yield, quality and energy use
- Integrate AI recommendations with operator decisions
- Manage safety and change when deploying AI in plants
Course outline
Day 1Data in Industrial Processes
- Process historians and data sources
- Typical optimisation goals
- Where AI helps
- Opportunity review
- Data quality in plants
- Team roles
Day 2Prediction and Soft Sensors
- Predicting quality variables
- Soft sensors
- Model validation
- Hands-on example
- Lab data integration
- Exercise
Day 3Optimisation
- Yield and throughput
- Energy efficiency
- Constraints and trade-offs
- Case study
- Emissions reduction
- Digital twins
Day 4AI and Control
- Advisory versus closed-loop
- Supporting operators
- Alarm management
- Exercise
- Operator trust
- Training operators
Day 5Safe Deployment
- Management of change
- Cybersecurity in operations
- Skills and roles
- Implementation plan
- Business case
- Vendor and partner selection
