Lithium recovery - sample application
With demand for lithium-ion batteries due to increase in the coming years, more and more processes for the extraction and recovery of lithium salts such as lithium carbonate or lithium chloride from solutions and brines are proposed, evaluated and compared.
The overall economic viability of a lithium recovery process critically depends on balancing concentration and crystallization of the desired product against impurities buildup, water recycles and wastewater.
Using the simevo Process Simulation technology you can develop a "digital twin" for the recovery process, which can forecast the Key Performance Indicators (such as recovery, purity and water specific consumption) as a function of inlet and operating conditions.
Problem
During research and development it is often required to forecast the Key Performance Indicators (KPIs) of the lithium recovery process such as yields, efficiencies and specific utility consumptions, at conditions different from nominal:
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to evaluate the return of investment (RoI)
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to cross-check and validate vendor-supplied data
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to compare the performance of equipment from different vendors
Once the pilot plant is in operation, forecasting can be used for:
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troubleshooting
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diagnosis
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optimization
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and predictive maintenance.
Solution
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 lithium recovery 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:
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a small-footprint, standalone custom process simulator for desktop use
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hosted in the public or private cloud for integration with other services, access via a dedicated web application on saas.simevo.com
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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.