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Intelligent Mining: Integrated Mine Planning and Operations
The document outlines ABB's vision for transforming the mining industry through integrated mine planning and operations, highlighting the challenges of depleting resources, increasing costs, and the need for improved productivity. It details technology advancements such as automation and real-time asset management that enhance safety, efficiency, and returns for shareholders. ABB's proposed solutions aim to integrate various aspects of mining operations, offering a holistic approach to optimize performance and asset value.
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Intelligent Mining: Integrated Mine Planning and Operations
- 1. © ABB Slide 1 November10, 2015 Intelligent Mining Integrated Mine Planning and Operations Rafal Walecki – Head of EMEA Delivery, Mining Solutions, ABB
- 2. © ABB Slide 2 November10, 2015 Challenges & History Our vision ABB’s Solution Components How it all fits together
- 3. © ABB Slide 3 November10, 2015 OUR VISION
- 4. © ABB Slide 4 November10, 2015 Challenges driving shareholders returns Depleting Reserves Export Tonnage Inland Tonnage Declining Grades PrimaryGrade,% 1.5 1.4 1.3 1.2 1.1 0.9 Increasing Costs 80 60 40 20 0 2000 2011 Consumables Labor Energy Other Source: Xstrata Copper Ore grade quality is declining Reserves are depleting in some commodities. Assets and ore bodies are becoming more complex. Labor is more costly, energy costs are going up and the doing business in new markets puts pressure on margins The bottom line: how to continue to deliver high returns to shareholders 1980 2000 20202000 2015 2030
- 5. © ABB Slide 5 November10, 2015 Step-changes in mining technology • Standardization of processes • Mechanization means dramatic shifts in production capabilities • Operation of equipment still requires human interaction • Integrated modeling and planning for higher quality yield • Greater visibility into parts of the value chain • More detailed information coming from equipment and plant to enable remote mining • More responsive demand and supply • Higher level of automation driven by labor shortages and remote mining locations • Limiting bottlenecks by adopting more continuous processes • High levels of visibility across the value chain and between operations Mechanization Automation Optimization The mining industry has been through a number of technology ‘step-changes’ which have driven dramatic increases in productivity. Research into mining technologies indicate significant new technologies heralding the next era of mining productivity
- 6. © ABB Slide 6 November10, 2015 ABB Mining Vision Coverage To drive fundamental change in the way a Mining Enterprise works, creating dramatic increases in Health and Safety Productivity Predictability Asset Availability Energy Efficiency Quality
- 7. © ABB Slide 9 November10, 2015 Mining Conveyer Asset Health Temperature sensors detect overcurrent condition on conveyor drive and create alarm Control system slows belt speed to reduce risk of an issue Work order automatically raises a parts requisition Work order is sent to maintenance crew mobile devices and work is completed Condition, inspection, and work combines for a unified view of conveyer health and optimization of the current maintenance programs 1 2 3 456 The old way: No information on upstream downstream impact Cant prioritize significant data volumes - manual Production, equipment and other control systems not integrated Work order scheduling in disparate systems No optimization of assets Value Proposition Transform real-time and historical asset data into strategic insights on the health of your mission-critical assets.
- 8. © ABB Slide 10 November10, 2015 Mining Plant Operations Optimization Advanced process control (APC) manages reagent dosing and flow rates in real-time (OT) Geological model and real-time material tracking provides short term feed characteristic schedule to APC Sales system monitors global index pricing for products Sales system periodically provides relative pricing updates to APC APC refines control set points to maximize returns for current feed material and product pricing On stream analyzers in plant reconcile actual feed with model to further improve predictability and optimization 1 2 3 567 The old way: No information on relative product pricing No information on feed material Plant set points static Production, equipment and other control systems not integrated Value Proposition Increase plant recovery and optimize product mix based on current pricing. 4
- 9. © ABB Slide 11 November10, 2015 SOLUTION COMPONENTS
- 10. © ABB Slide 12 November10, 2015 Solution Components Plant Reporting Mass Balance Plant Efficiency Material Logistics & Blending Inventory Management Sales & Marketing Enterpise Asset Management Work and Projects Management Maintenance Management System Lab Management Sample Tracking QA/QC Quality Geological Modeling Mine Planning/Scheduling Mine Operations
- 11. © ABB Slide 13 November10, 2015 Geoloigical Modeling & Mine Planning Geology & Mine Planning Other Geological Database Geological Model Excavations Model Production SchedulingMine Design Geological Database Exploration Survey Asset & Work Management Product Logistics & Sales
- 12. © ABB Slide 14 November10, 2015 Material Logistics and Sales Mining Activities Processing Transportation Top Soil Drilling Overburden Waste Dump Parting Removal Loading Hauling Reject Disposal Rehabilitation ROM StockpilesHauling Processing Plant Product Stockpiles Load Products Transporting Train Loading Ship LoadingPort StockpilesTrain UnloadingRail Head Stockpiles
- 13. © ABB Slide 15 November10, 2015 Mining Laboratory Management System (LIMS) Q B L Lab Status QA Flag Budget / Actual Sample Analytical Result Dispatch S A D Legend Assay Mgmt Lab Instrument Integration/Interface Layer ABB CCLAS Modules Lab Config. Web Interface, Dashboards & Reporting BI Reporting Rules User Forms QA & SPC ERP System D A AS DataAudit Sample Registrat’n Data Capture & MDE Data Calculat’n Scripting Sample Tracking Lab Maint. L Invoice & Revenue Task Automt’n Consum- ables Instr. Mgmt Archive/ Retrieve A A
- 14. © ABB Slide 16 November10, 2015 Production Accounting Management Systems (e.g. ERP) Total production by period, KPIs, qualities, inventories tons, volume, grade, moisture, hardness, penalty elements - transaction, shift, day, week, month METAL ACCOUNTING BUSINESS PROCESS
- 15. © ABB Slide 17 November10, 2015 Asset Management Enable PAS 55 • Standardize Asset Management practice Mobile Workforce Management • Manage assets from the point of inspection • Automate field-based processes • Optimize mobile workforce, including 3rd party contractors Asset Management Paradigms for Critical Assets • Manage in terms of both Risk and Criticality • Location based asset management with GIS integration Advanced Work Planning • Closed-loop long and real-time work planning and scheduling Materials Management • Total asset visibility • Financial fidelity of assets and improved inventory management Enterprise Visibility • Highly flexible, open analytics and reporting
- 16. © ABB Slide 18 November10, 2015 How it fits together – Complete Integration PA MineScape CCLAS • Supply Chain Network Configuration • Inventory & Stockpile Management • Material Tracking • Waste Dump Management • Outbound Logistics Scheduling • Blending to and Optimization • Port operations & Ship loading • Sales & Invoicing • Shipping documents • Supply & Demand Balancing • Contract Fulfilment & Management • Mining Data Reconciliation • Transaction assays • Transport assays • Ship loading assays MineMarket Laboratory Information Management System Resource block model Production Schedule Pit/UG Surveys Actual Production Aggregated transactions Daily stock balance End of period survey Reconciled transactions • Sample ID • Transaction details Block ID Assay data Moisture Assay data • Geological DB • Stratmodel • Block model • Opencut design • Underground design • Ring design • Scheduling • Mine & pit survey • Dragline • Haul roads • Drill & blast • Auto data acquisition • Auto data validation • Statistical validation • Data checks • Data sign-offs • Auditing • Cascading calculations • Check in – Check out • Statistical balancing • Data locking (quarantined) • Data publishing Ellipse ASSETS • Asset Policy • Capital Work • Register Asset • Develop Strategy • Deploy Strategy • Asset Analysis WORK • Create • Plan • Schedule • Execute • Complete • Work Analysis MATERIALS • Requirements • Source Materials • Procure • Use Materials • Distribution • Performance Analysis PEOPLE • Maintain Organization • Workforce Planning • Organizational Development • Time & Attendance • Pay & Benefits • HSSRE FINANCIAL • Financial Model • Accounts Payable • Accounts Receivable • Fixed Assets • General Ledger • Performance & Compliance ProductionSchedule EquipmentData Equip.Availability Productionbyperiod Qualitydata Inventories Financial&KPI Reserves • Production by period • Stock balance • Equipment delays • Equipment usage • Shipment actuals • Invoicing details • Budget & Forecasts • Production plans • Contracts details • Shipment schedule • Equipment master
- 17. © ABB Slide 19 November10, 2015 How it fits together - Operations Visibility VP Health, Safety & Compliance VP OPS CFO CIO VP Procurement KW-hours/ton Delay penalties per month Equipment- incidents / month Ship over-under load/month Drill-blast cost/ton Reagents/ton Fuel/ton Metric per Unit-Product- Produced Dig rate by shovel Dilution Availability Throughput Shipping queue length
- 18. © ABB Slide 20 November10, 2015 How it fits together - Intelligent Operation Data Processing and Analytics Subject Matter Workbench Geology & Mine Planing Product Logistics Equipment Performance Work Parameters Technical Specifications Sensors SCADA SCADA Feedback Information Volume Mass Quality Feedback Conditional Monitoring Operations Control Center InformationDecisions Results Historian Enterprise Data Sources EA M
- 19. © ABB Slide 21 November10, 2015 How it fits together - One Control Room
- 20. © ABB Slide 23 November10, 2015 FIND OUT MORE Visit www.abb.com/enterprise-software Email info.PGES@abb.com
Editor's Notes
- #5 The slide shows an It/OT convergence scenario from the perspective of asset health. By using real-time condition data, in this case the temperature sensors on conveyer drive, the actual condition of the asset can be detected allowing proactive actions to be initiated automatically greatly reducing the risk of potential failure which would cause a loss of production. Old way: No information on upstream /downstream impact makes it hard to prioritize the manual review of significant data volumes Network, outage, equipment and other control systems not integrated making it difficult to automate actions Highly reliant on skilled operators – difficult with aging workforce Not integrated with EAM and mobility to optimize work order scheduling Lack of integration between systems makes it hard to get a holistic view on asset health – making it difficult to optimize asset management strategies.
- #6 The slide shows an It/OT convergence scenario from the perspective of asset health. By using real-time condition data, in this case the temperature sensors on conveyer drive, the actual condition of the asset can be detected allowing proactive actions to be initiated automatically greatly reducing the risk of potential failure which would cause a loss of production. Old way: No information on upstream /downstream impact makes it hard to prioritize the manual review of significant data volumes Network, outage, equipment and other control systems not integrated making it difficult to automate actions Highly reliant on skilled operators – difficult with aging workforce Not integrated with EAM and mobility to optimize work order scheduling Lack of integration between systems makes it hard to get a holistic view on asset health – making it difficult to optimize asset management strategies.
- #9 KPI – Key Performance Indices OEE – Overall Equipment Effectiveness SLA – Service Level Agreement
- #10 The slide shows an It/OT convergence scenario from the perspective of asset health. By using real-time condition data, in this case the temperature sensors on conveyer drive, the actual condition of the asset can be detected allowing proactive actions to be initiated automatically greatly reducing the risk of potential failure which would cause a loss of production. Old way: No information on upstream /downstream impact makes it hard to prioritize the manual review of significant data volumes Network, outage, equipment and other control systems not integrated making it difficult to automate actions Highly reliant on skilled operators – difficult with aging workforce Not integrated with EAM and mobility to optimize work order scheduling Lack of integration between systems makes it hard to get a holistic view on asset health – making it difficult to optimize asset management strategies.