Reported impact
Business outcome
BHP expects the deployment to improve copper recovery and optimize concentrator performance so the operation can generate more value from an existing resource.
- Productivity Improvement
MicrosoftEnergyReal-time AnalyticsAI and Machine LearningData AnalyticsProductivity Improvement2023 MicrosoftMS#0078d4EnergyChileBHP wanted to optimize concentrator performance and improve copper recovery at its Escondida mine as existing mine grades decline and more value must be extracted from existing resources.May 30, 20235Real-time AnalyticsAzure platform0 2023-05-30 5
BHP
Real-time Analytics
BHP wanted to optimize concentrator performance and improve copper recovery at its Escondida mine as existing mine grades decline and more value must be extracted from existing resources.
Solution
BHP used Microsoft’s Azure platform to apply artificial intelligence and machine learning to concentrator operations at its Escondida copper mine in Chile. The implementation begins with real-time plant data from the concentrators, which is combined in Azure with AI-based recommendation logic to evaluate current operating conditions. Azure then produces recommendations for concentrator operators, who use that guidance to adjust operational variables that affect ore processing and grade recovery. The design augments frontline operating workflows rather than replacing them: plant telemetry is the upstream input, Azure is the cloud analytics and model execution layer, and human operators remain the decision-makers at the serving edge of the process. The source does not name separate storage, orchestration, or governance services, but it clearly identifies Azure as the platform hosting the AI/ML capability used to optimize concentrator performance and lift copper recovery.
Data flow
Real-time plant data from Escondida’s concentrators is fed into Microsoft’s Azure platform, where AI and machine learning generate recommendations. Those recommendations are delivered to concentrator operators, who adjust ore-processing variables to improve grade recovery and overall concentrator performance.
Solution architecture
- Uncategorized
- Azure platform Provides the cloud platform for AI-based recommendations built from real-time concentrator plant data.
Architecture clues
- Deployed at BHP’s Escondida operation in the Antofagasta Region of northern Chile
- Recommendations are delivered to human operators, who adjust operational variables affecting ore processing and grade recovery
- Uses real-time plant data from concentrators as the upstream input
Evidence from the source
A new collaboration between BHP and Microsoft has used artificial intelligence and machine learning with the aim of improving copper recovery at the world’s largest copper mine.
By using real-time plant data from the concentrators in combination with AI-based recommendations from Microsoft’s Azure platform, the concentrator operators at Escondida will have the ability to adjust operational variables that affect ore processing and grade recovery.
The use of new digital technology to optimise concentrator performance at BHP’s Escondida operation in Chile is expected to improve copper recovery.
We are excited to partner with BHP on this transformative project that demonstrates the power of AI, machine learning and cloud technologies
We expect the next big wave in mining to come from the advanced use of digital technologies.
Transparency Evidence & methodology
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
How scores are calculated
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Customer reference value75/100
- Problem clarity20/20Measures whether the customer's operational problem is specific enough to compare.
- Implementation depth25/25Rewards named components, data flow, integrations, and architecture detail.
- Outcome specificity20/20Measures whether the reported benefit is concrete rather than a generic claim.
- Quantified impact0/250 metrics describe measurable impact.
- Deployment scale10/10Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence92%
- Source authority25/25The deployment is described by the adopting organization or a government source.
- Claim traceability21/2510 of 12 key fields link to a named source.
- Direct evidence20/205 direct source quotes retained.
- Date provenance10/10The publication date comes from the source page or publisher.
- Source transparency8/101 source reference document publisher, URL, authority, retrieval, and supported fields.
- Implementation specificity8/10Rewards source-backed components, data flow, quantified impact, and a specific outcome.
Traceability
Sources & provenance
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
- https://www.bhp.com/news/media-centre/releases/2023/05/bhp-and-microsoft-use-ai-to-lift-escondida-copper-recoveryBHPFirst-party
BHP customer story
Supports- Customer
- Business problem
- Solution
- Products
- Solution components
- Data flow
- Integrations
- technical area
- use case category
- Business outcome
- outcome category
- Metrics
- Architecture
- evidence quotes
- Publication date
Date evidence
- Published on source page May 30, 2023 · BHP · First-partyOpen date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.
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