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APSC 150 Engineering Case Studies Case Study 3: Sustainable Mining Part III: Automation in Mining Lecture 3.8. Autonomous Haulage Trucks. John A. Meech Professor and Director of CERM3 The Centre for Environmental Research in Minerals, Metals, and Materials

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Autonomous Haulage Trucks


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    1. APSC 150 Engineering Case Studies Case Study 3: Sustainable Mining Part III: Automation in Mining Lecture 3.8 Autonomous Haulage Trucks John A. Meech Professor and Director of CERM3 The Centre for Environmental Research in Minerals, Metals, and Materials The University of British Columbia Email: jameech@dccnet.com

    2. How does Automation relate to Sustainability? • Automation • Removes workers from positions of danger • Improves the operating efficiency of mining • Reduces carbon emissions (better fuel use) • Decreases stripping ratio • Increases recovery of the orebody • Provides consistency and eliminates error • Makes a mine more competitive • Makes the work easier and more intelligent

    3. Is Automation Obvious? • Mining operations are often chaotic • Complex systems are not easy to automate • Too many heuristic issues (environment) • Unions don’t take kindly to labour replacement • Industry is conservative (who has done` it?) • Failure in high risk situations is unacceptable • Who does the installation and maintenance? • What about back-up systems?

    4. Equipment being Automated • Underground digging and hauling (LHD) • Underground communication systems • Underground drilling • Underground surveying • Open Pit drilling • Slope stability monitoring • Truck Hauling and Dumping

    5. Autonomous Vehicles • DARPA Grand and Urban Challenges • 2004, 2005, and 2007 • Vehicles drove by themselves autonomously

    6. UBC Thunderbird Robotics • Formed in 2004 to enter the DGC • Over 450 students have participated in numerous mobile robotic projects • DARPA Grand and Urban Challenges

    7. UBC Thunderbird Robotics • Formed in 2004 to enter the DGC • Over 450 students have participated in numerous mobile robotic projects • DARPA Grand and Urban Challenges • Robot Racing Competition

    8. UBC Thunderbird Robotics • Formed in 2004 to enter the DGC • Over 450 students have participated in numerous mobile robotic projects • DARPA Grand and Urban Challenges • Robot Racing Competition • NASA Moon Regolith Excavator Competition

    9. UBC Thunderbird Robotics • Formed in 2004 to enter the DGC • Over 450 students have participated in numerous mobile robotic projects • DARPA Grand and Urban Challenges • Robot Racing Competition • NASA Moon Regolith Excavator Competition • Thunderbots RoboCup Soccer

    10. UBC Thunderbird Robotics At the Vancouver Auto Show • Formed in 2004 to enter the DGC • Over 450 students have participated in numerous mobile robotic projects • DARPA Grand and Urban Challenges • Robot Racing Competition • NASA Moon Regolith Excavator Competition • Thunderbots RoboCup Soccer • E-Beetle Electric Car http://www.ubcecc.com/blog/

    11. Snowstorm

    12. CAT797F

    13. Mining Truck Accidents

    14. Komatsu’s AHS

    15. Open Pit Mining Costs ($/t)

    16. Operating Cost Breakdown for CAT793

    17. Operating Cost Breakdown for CAT789

    18. Evolution of Mining Truck Capacities

    19. Key Components

    20. System Requirements • Communication network • Sensors for navigation and object- avoidance • GPS system accurate to 10cm (D-GPS) • Computer hardware on-board • Central processing system • Controller devices • Supervisory Software

    21. GPS-based navigation

    22. Autonomous Control Cabinet Hydraulic controls sealed on left side, electronic controls and truck interface on the right side for ease of access. Group includes PLM III. Autonomous Status Lights Mounted on all sides of the truck to safely display truck operating status Road Edge Guidance (REG) A mounted laser guidance system measures the distance to the road berm to provide additional navigation accuracy PLM III is a payload monitoring system that gives the operator accurate weight of payload, gross vehicle weight, cycle times and empty vehicle weight.

    23. Wheel Speed Sensors Wheel speed sensors and laser-ring gyro are combined to produce accurate navigation control (IMU). Steering Angle Sensors The steering angle of the wheels is measured at the control arm Autonomous Status Lights Mounted on all sides of the truck to safely display truck operating status

    24. GPS GPS technology is combined with Modular Mining’s Masterlink system to accurately track location of vehicles Masterlink Modular Mining’s Masterlink system monitors every vehicle in the system.

    25. Obstacle Detection System Millimeter wave radar System focuses only on the route. Perceivable target: a human 100 m distance

    26. Komatsu 930E-AT First two units were field tested by Komatsu at the Twin Butte mine near Tucson. Tests show no major problems with radar and GPS navigation. The system is also examining other equipment - bulldozer, front-end loader and several smaller ancillary vehicles.

    27. ASI’s Obstacle Avoidance System

    28. Radomiro Tomic Mine in Chile • Tonnage Hauled 2006 = 8,222,000t - 32,000 tpd for 256 days • Mechanical Availability = 90.5% AHS vs. 80.2% total fleet • Effective Utilization = 84.2% • Daily Haulage Time = 24 x 0.905 x 0.842 = 18.2 hrs - percentage gain = 25.4% - potential to 20.5 hours • Accidents = none (2 in 2007) • Cost per tonne = U.S.$1.36/t >>>> U.S.$0.58/t • Maintenance Reduction = 7% • Depreciation Decrease = 3% • Impact on Mine Design - increased slope angle - decreased road width • Significant increase in safety

    29. Software / Sensor Features • Localization • Navigation • Obstacle Recognition • Obstacle Avoidance • Lane Following (where am I?) (where do I want to go?) (what is in the way?) (how do I avoid it?) (what is best route?)

    30. Shovel-Truck Modeling • ExtendSim software to model discrete event processes • Probability of failures (maintenance) • Model based on First-Principles • Rimpull and speed • Fuel consumption • Fuzzy model of road conditions • Tire wear based on empirical data • Manual versus Autonomous operation

    31. Truck Operation The basic truck cycle considers ore/waste being loaded at a shovel and delivered to a surface stockpile/Crusher. 31 Http://ssabh188.blogspot.com

    32. Pit Layout Dump link5 extension extension link6 Waste Ore Shovel link1 link7 extension link2 link4 Maintenance Waste Shovel Ore Parking link3 Crusher Http://ssabh188.blogspot.com

    33. Traffic Management • Speed limits • 2 way traffic • No passing • Minimum separation between vehicles(50m) Http://ssabh188.blogspot.com

    34. Batching of Resources – trucks & drivers

    35. Digging and Loading Module • ExtendSim Blocks for Discrete-Event Modeling

    36. Maintenance and Delay Module

    37. Driver Attributes Work Period = 14 days Shift Duration = 12 hours Breaks Shift Time (hr) Duration (hr) Shift Change 0/12 0.25 Lunch 6 0.75 Bathroom 3 and 9 0.17 Total - 1.34 (11%) Attribute Efficiency short term long term learner 80 90 experienced 85 95 tired (shift start) 85 90 tired (shift end) 80 85 tired (start work period) 85 90 tired (end of work period) 80 85 time since trained (short) 85 95 time since trained (long) 75 80 personality (aggressive) 80 90 personality (conservative) 85 95 In addition, variance will be higher for negative attributes.

    38. Components • Two Shovel • Eight Trucks • One dump area • One Crusher • Auxiliary equipment such as graders, dozers, water trucks, fuel trucks, drills and light vehicles • Breakdown and maintenance events Http://ssabh188.blogspot.com

    39. Manual & AHT Model Output The model outputs Benchmarking KPIs*: • Productivity • Safety • Breakdowns • Cycle times • Maintenance costs • Labour costs • Fuel Consumption • Tire Wear • Truck Costs ( annualized) • Reduced GHGs * KPI = Key Performance Index

    40. Rimpull vs. Speed

    41. Sub-Questions in the Research Key Performance Indicators - Targets AHS AHS AHS AHS AHS + 30% +8% -7% -10% -12% Manual Manual Manual Manual Manual Investment cost per truck Truck haulage speeds Fuel consumption Mechanical Availability Tire Wear

    42. Sub-Questions in the Research Key Performance Indicators - Targets AHS AHS AHS AHS 12% + 5% -15% -14% -80% Manual Manual Manual Manual Increased Productivity Maintenance costs Increased Truck life Labour costs Labour savings depend on current mine circumstances – union and turnover issues

    43. Potential Labour Savings • Current Situation: • Drivers per truck = 4 (2 on / 2 off) • Total = 55 trucks x 4 = 220 plus vacation subs (20) • Drivers retrained every 6 months • Annual turnover = 40% • Annual Labour cost = $36,000,000 + O/H • Annual training costs = $10,000,000 + O/H • Future Situation: • No drivers and no training • Increased maintenance personnel (3 trucks/person) • Annual costs = 48 x $150,000 = $7,200,000 + O/H

    44. Potential Labour Savings • Care must be taken when introducing automation into a union operation • Labour replacement must be done by attrition, not by lay-offs • Sabotage will result otherwise • Time to implement will increase • System may fail ultimately

    45. Mine Design Issues • Steepen pit slopes to reduce stripping ratio • Reduce haulage road width • Select smaller trucks to increase flexibility

    46. One-way (straights/corners) Two-way (In straights) Two-way (In corners) Haul Road Width • One-way straights and corners: 2.5 – 3 widths • Two-way traffic: In straights, 3 – 3.5 truck widths • Two-way traffic: In corners, 3.5 – 4 truck widths

    47. Komatsu’s Autonomous Haulage System

    48. Autonomous Solutions Applications

    49. ASI’s Remote Dozer Control

    50. Fuzzy Control of an Autonomous Vehicle