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Current process plant design practices follow a waterfall engineering approach in which each process subsystem is designed and dimensioned separately without higher level dynamic simulation, optimization and co-operation between all stakeholders.
Due to these limitations of the traditional workflow, components end up being over dimensioned and stacking of the risk margins make the entire design of the plant inefficient.
SIMaaS is a simulation service based on a Collaborative Digital Twin. It offers the possibility to collaboratively design, simulate and optimize the processes together with the relevant stakeholders.
In SIMaaS workflow, end users and engineering companines (EPCs) provide process data as well as system requirements, equipment manufacturers (OEM) provide their component data. Collected information is used to create a Digital Twin of the system. SIMaaS is a simulation-based Digital Twin where behavior of the system can be simulated dynamically.
The dynamic simulation results are collaboratively reviewed by stakeholders and next steps and adjustments to the system are agreed. Workflow can include several iterations depending on the customer needs or system specifications. Finally, optimal set of components can be selected based on the performance results obtained with SIMaaS.
SIMaaS is based on a dynamic co-simulation approach, making it ideal to test the process behavior together with the control application. It is based on the FMI standard, which reduces effort required to integrate heterogeneous simulation models from different process sub-systems.
SIMaaS enables optimized selection of equipment and its configuration. It helps avoiding stacking risk margins and over-dimensioning. Additionally, simulation analyses can be targeted to increase energy efficiency of the system, resulting is cost savings over the system operation as well as savings in both, CAPEX and OPEX.
First industrial pilot cases have resulted in up to 46% savings in energy consumption of a pump line after SIMaaS was applied for dynamic simulation and optimal component selection.
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