Nanocellulose production sample application
Nanostructured cellulose, in the form of cellulose nanocrystals (nanocrystalline cellulose, NCC) or cellulose nanofibrils (nanofibrillated or nanofibrillar cellulose, NFC), nano-ligno-cellulose or bacterial cellulose (BC) could be an interesting replacement for synthetic nanostructured materials.
The overall economic viability of nanocellulose depends on the natural feedstock (lignocellulosic biomass, cellulose pulp, or mechanical pulp), and on the conversion process: mechanical (homogenization or application of refining energy), chemical or biochemical. Using the simevo Process Simulation technology you can develop a "digital twin" for the nanocellulose production process, which can forecast the Key Performance Indicators (such as conversion, energy consumption, nanocellulose purity, crystallinity, mechanical properties etc.) as a function of feedstock, inlet and operating conditions.
During research and development it is often required to forecast the Key Performance Indicators (KPIs) of the nanocellulose 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 nanocellulose 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.