Graphene-based membranes - sample application
Nanoporous graphene, perforated graphene or graphene-reinforced membranes look promising for liquid separations (nanofiltration, desalination, and pervaporation) and for gas separations.
The benefits of these innovative separation membranes over conventional ones must be evaluated under different scenarios and membrane module configurations.
Using the simevo Process Simulation technology you can develop a "digital twin" for the separation process based on graphene membranes, which can forecast the Key Performance Indicators (such as removal efficiency, recovery, purity and energy consumption) as a function of inlet and operating conditions, and of membrane characteristics (thickness, permeability and selectivity).
During research and development it is often required to forecast the Key Performance Indicators (KPIs) of the graphene membranes separation process such as yields, efficiencies and specific utility consumptions, at conditions different from nominal:
to evaluate the return of investment (RoI)
to cross-check and validate vendor-supplied data
to compare the performance of equipment from different vendors
Once the pilot plant is in operation, forecasting can be used for:
and predictive maintenance.
For all these purposes a steady-state, first-principle process model ("digital twin") is the way to go; just stay away from spreadsheets (because they suck at modeling !) and simulate your graphene membranes separation process using the simevo process technology.
The process model calculates the KPIs as a function of inlet and operating conditions, and can be deployed as:
a small-footprint, standalone custom process simulator for desktop use
hosted in the public or private cloud for integration with other services, access via the simevo process app or via a dedicated web application
a standalone online application that communicates with the control system or with the Supervisory Control and Data Acquisition (SCADA).
In all cases you can shield the user from implementation details, modeling assumptions and hypotheses. This is useful to make the model robust and simple to use, and / or to protect your know-how.