Move From Static Process Models to Living Predictive Systems
Chemical process models have traditionally been built for design studies, scale-up calculations, troubleshooting or offline optimization. Digital twins take that capability further by continuously connecting process data, physical models and predictive analytics to represent how a real process is behaving and how it may respond to changing conditions.
For chemical R&D and process-development teams, the opportunity is much broader than creating a virtual copy of a plant. A useful digital twin can combine first-principles models, historical process data, sensor measurements, soft sensors, machine-learning models and real-time model updating to support decisions before changes are made to the physical process.
The challenge is deciding what should actually be modelled, which data can be trusted, how physical and data-driven models should be combined, how models remain valid as operating conditions change, and when a prediction is reliable enough to influence process decisions.
This advanced training focuses on the practical architecture and development of digital twins for chemical processes, from model selection and data integration through hybrid modelling, soft sensing, real-time prediction, optimization and deployment. The emphasis is on building technically credible twins that chemical engineers and R&D teams can use for process development, scale-up, monitoring and decision support.
What Problem This Training Solves
Many chemical companies already have process models, historian data, laboratory results and simulation tools, but these often remain disconnected from day-to-day process-development decisions. This training addresses the practical gaps that prevent those resources from functioning as an integrated digital twin:
Process models become outdated when real operating conditions shift
Critical quality variables cannot always be measured directly in real time
Laboratory, pilot and production data often remain poorly connected
Purely data-driven models can violate important physical process constraints
First-principles models may miss complex behaviour seen in actual operation
Process teams struggle to know when model predictions are reliable enough
Optimization studies remain offline instead of influencing current operating decisions
Digital-twin projects often become software exercises rather than engineering tools
The training shows how to combine process knowledge, physical models, sensor data and AI into a digital representation that can support prediction, optimization, scale-up and process-development decisions.
Why Attend?
Select suitable digital-twin architectures for chemical processes
Combine first-principles and data-driven process models
Develop useful soft sensors from process data
Update models using real operating information
Apply digital twins to process optimization
Evaluate model uncertainty and deployment reliability
Who Should Attend?
This advanced training is particularly relevant for:
Chemical process development and scale-up engineers
Process modelling and simulation specialists
Chemical and materials R&D scientists
Process control and optimization engineers
Manufacturing and process technology teams
Data scientists supporting chemical process development
Training Outline
Digital Twins for Chemical Process Development
Selecting the Right Process Representation
Building Hybrid Physics and Data-Driven Models
Process Data, Sensors and Soft Sensing
Model Calibration and Real-Time Updating
Prediction, What-If Analysis and Optimization
AI Integration and Intelligent Decision Support
Validation, Uncertainty and Deployment
Case Studies
Expert Q&A Session
Go Beyond Process Simulation
Build digital twins that connect engineering models, real process data and AI to improve process prediction, accelerate development and support better optimization decisions. Register Now.
