Traditionally, Model Predictive Controllers are trained using process data. which requires substantial time and material investment to execute the process response tests required for model development and validation. A more efficient approach is to employ digital design techniques to reduce the requirement for process run time during controller development.
Darren will discuss how attaching automation tools to a virtual plant can allow the evaluation of QbD, Machine learning and Advanced Process Control approaches in a risk-free, in-silico manner. A comparison of the information gained by each approach when applied to a flow chemistry process will be presented along with discussion on how this can translate into process knowledge.
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and read more about digital design and scale-up here