AIRL Technologies consulting

Mine-to-Mill Analytics & Machine Learning

Investigate how upstream choices affect downstream performance.

Mine-to-Mill Analytics & Machine Learning is an AIRL Technologies service for technical and operations teams investigating relationships across blasting, fragmentation, digging, crushing, and milling using site-specific data analysis and models.

For: Drill and blast engineers · Processing and metallurgy teams · Operational analysts

Discuss Your Project

The operational challenge

Production outcomes span several stages of the operation. Teams need to connect the information across those stages and examine the assumptions before attributing downstream changes to an upstream decision.

What we help with

Blast and fragmentation analysis

Connect available blasting and fragmentation information with downstream observations.

Digging and crusher performance

Investigate relationships with digging performance, crusher throughput, and oversize-related downtime.

Mill energy review

Include mill energy and relevant processing information in the agreed analysis.

Site-specific models

Develop and validate analytical or machine-learning models around the project's question and data.

Broader operational analytics

Apply the same analysis approach to other operational performance and decision-support questions within scope.

How we work

Understand the operation
Agree on the outcomes, available data, and relationships to investigate.
Develop and validate
Connect relevant records, test assumptions, and review model findings with site specialists.
Implement and support
Hand over analyses, models, and recommendations with their assumptions and limitations, and support their use within the agreed engagement scope.

Typical deliverables

Data assessment and analytical scope
Connected analysis of agreed mine-to-mill measures
Site-specific analyses or validated models
Findings with assumptions and limitations
Recommendations, reports, and model handover where applicable

Related MineDash applications

Drill & Blast QAQC can provide execution and blast-history context; Reporting can support recurring review of analytical outputs. The engagement can use available site data independently of a MineDash licence.

Discuss your project

Tell AIRL Technologies about your site's priorities, systems, and desired outcome for mine-to-mill analytics & machine learning.