Our Client’s metallurgists conduct periodic production forecasting of production at their mine site, a large copper deposit and production plant in South Australia. Forecast predictions were historically made using an Excel-based model, associated with the specified mine plan and processing constraints from historic operational data. The Excel model can be prone to entry errors, carry over from previous forecasts and lost revision history. The client therefore saw the need to convert the model engine to Python, with an Excel user interface was required.
WGA took key design parameters from the engineering design team’s Process Design Criteria and implemented them into an Excel interface. These input parameters were fed into a Python model which generated and solved a system of linear equations derived from the law of conservation of mass applied around each unit process. To solve for the desired process parameters, additional constraints were generated using the design criteria inputs and an understanding of the processes.
The model exported a stream table into the Excel interface containing key process parameters including solids and liquor mass and volumetric flow rates, stream composition, density, and resin loading rates onto the Ion-Exchange columns.
Our Client received a powerful Python-based mass balance model with increased model security and increased efficiency. The model has increased security due to being independent from the Excel user inputs, which ensures a single point of truth remains secure and minimises the risk of errors.
A Python-based model has a higher level of automation than a traditional Excel-based model and allows for simplified and faster forecasting estimates, meaning multiple scenarios can be rapidly executed and compared.
The Uranium from Tails Mass Balance is included in the following sectors & services.
Click on one to find out more about that sector or service.