MAXTA Add-Ons

Add new insight and advanced capabilities

MAXTA

Optimise®

Available For the following MAXTA applications:

MAXTADrill&Blast®

MAXTAProcess®

MAXTAOptimise® recommends design levers and set points for optimum downstream performance based on machine learning prediction model and optimisation techniques.

MAXTA

Interpret®

Available For the following MAXTA applications:

MAXTADrill&Blast®

MAXTAProcess®

MAXTAGeomet®

MAXTALoad&Haul®

MAXTAInterpret® provides a window into the digital twin model to understand and interrogate the influence and relevance of all relevant data sources in the prediction, simulation and optimisation of your mine.

MAXTA

Simulate®

Available For the following MAXTA applications:

MAXTAProcess®

MAXTASimulate® allows investigation of “what if” scenarios to assess impact of design levers and process set points on digital twin model behaviour.

MAXTA

Domain®

Available For the following MAXTA applications:

MAXTADrill&Blast®

MAXTAProcess®

MAXTADomain® automates domain definition for any downstream value driver. For example; if you want to maximise throughput across multiple mills, then domains are automatically created for total mill throughput. AI powered domaining enables differential blasting (multiple blast designs within the same shot).

Blast designs for each domain are then seamlessly optimised in MAXTADrill&Blast®.

MAXTA

SensorHealth®

Available For the following MAXTA applications:

MAXTALoad&Haul®

MAXTAProcess®

MAXTASensorHealth monitors all sensor data to identify drift, error and anomalies in sensor data. It assesses the validity of data used in live digital twin models, providing real-time data quality warnings for MAXTAProcess and MAXTALoad&Haul.

MAXTA

BlendOptimiser®

Available For the following MAXTA applications:

MAXTAGeomet®

MAXTABlendOptimiser enables rapid comparison of predicted processing results for non-additive blending characteristics.

For example mill throughput and metallurgical recovery are both examples of geometallurgical properties known to exhibit non-additive blending characteristics.

A parameter is deemed “additive” if you can combine two samples of a known value, and the blended test results is the arithmetic average of the two.

Want to extract value from your mining data?

Get in touch with the PETRA team to discuss what would be a good fit, relevant to your mine.

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