In most mining operations, data reconciliation in Metallurgical Accounting is very important, but not done to the required degree of accuracy.
This is usually the case due to the limited capability of the data capturing that is used, commonly involving entering values manually which is obviously arduous and time-consuming for the Metallurgist.

Data reconciliation is considered an effective technique to improve the accuracy and reliability of plant data, mainly tracking the movement of valuable metals(minerals).

It is normally formulated as an optimization problem minimizing the difference of the measured and estimated variables while respecting constraints imposed by the process model.

The reconciliation process seeks to take several measurements from different sources, over a chosen time period and compare the results. The objective is to realize the ‘true’ figures, based upon the input plant data.

In general, the major reasons for doing Data reconciliation in Metallurgical Accounting are:

  • A higher degree of accuracy - The mass balance of a metallurgical circuit done with the data reconciliation method will find and use the best estimates of the original data as close as possible. Whereas, the mass balance is done with only original data (throughput, assay, size fraction for cyclone, etc) is generally incoherent and inconsistent due to the errors of the data used (measured or estimated).

  • Better insight into the process at a more granular level, allowing site managers to respond to process issues in real-time.

  • Provides one single source of truth for all unit operations, which is easier to track.

  • Can be used for monitoring dynamic concentrate streams or circuits (through virtual sensors incorporated in brains.app)


Further reading:

How do I do Data Reconciliation?

Why use brains.app Reconciliation Service?

Did this answer your question?