Increasing process plant complexity requires more sophisticated ways of approaching KPIs and targets. Process digital twins have the capability of presenting deeper analytics through intrinsic KPIs. Continuous unit monitoring and model assurance help in the identification of opportunities for improvements. These benefits are evaluated, implemented, and sustained through various parameters estimated by the digital twins.
KPIs calculated by the process digital twin monitor the entire performance of the unit, and while some of these parameters are raw or reconciled values, others include complex calculations, e.g., the remaining life of catalyst. These KPIs are calculated using kinetics or equilibrium-based reactor models. Process digital twins facilitate an efficient and standardized methodology for ensuring that plant data, LP predications and nonlinear model performance are kept synchronized.
KPIs calculated by the process digital twin often help identify additional areas for improvement. This is especially true for KPIs that are difficult to estimate and are rarely calculated in normal operation, e.g., approach towards equilibrium, fractionation efficiencies, flooding in distillation columns, etc.
Join this webinar to understand:
- KBC’s value tree and mapping of a process digital twin
- The inputs required for the process digital twin
- How to achieve performance improvement with process digital twin
- A high-level deployment strategy
- A case study