Algal oil production - sample application
There is a growing interest in algal oils, both as energy sources (bio-diesel and bio-fuels) and as nutrition and feed additives.
Besides the microbiology and algae growth issues, the overall economic viability of algal fatty acids critically depends on the post-processing of the algal biomass, which inevitably involves solids and wastewater handling, but also (depending on the chosen route) enzyme systems, physical and mechanical operations, multi-stage concentration and solvent extraction.
As often happens for new processes, non-standard unit operations and custom-built machines may be required. Using the simevo Process Simulation technology you can develop a "digital twin" for the algal lipid production process, using simple, empirical models or more sophisticated, custom models for the key units.
During research and development it is often required to forecast the Key Performance Indicators (KPIs) of the algal oil production 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 algal oil production 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.