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Autonomous Haulage Trucks - the new way to mine John A. Meech University of British Columbia The Norman B. Keevil Institute for Mining Engineering The.

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Presentation on theme: "Autonomous Haulage Trucks - the new way to mine John A. Meech University of British Columbia The Norman B. Keevil Institute for Mining Engineering The."— Presentation transcript:

1 Autonomous Haulage Trucks - the new way to mine John A. Meech University of British Columbia The Norman B. Keevil Institute for Mining Engineering The Centre for Environmental Research in Minerals, Metals, and Materials Vancouver, British Columbia, V6T 1Z4

2 "…only 10-15% of mine sites currently 'leverage technology well'...what we’re moving...toward...with...autonomy is a factory-type environment,...and that’s going to require...a more clinical and more managed environment. If you look at a [fully-automated] factory,...they are very sterile, very structured environments. If you have robots operating...the floor can’t be dirty, it can’t be scattered with empty boxes... " - Carl Hendricks, CAT Mining Solutions Region Manager for Australia

3 "A haul road in a modern mine running autonomy …[has]...the same issues. You can’t have poorly constructed… roads...Some [automation]...we use is sensitive to dust...[which will]...cause the vehicle to sense...an obstacle...that really isn’t there. [T]hat’s going to hinder operation of the machine. There... [must]...be a new level of discipline in how we maintain that environment – just like a factory. These are...things...we should be doing,...the sorts of the things...modern processing plants do. They maintain their environment and operate with rigour." - Carl Hendricks, CAT Mining Solutions Region Manager for Australia

4 Outline Automation and Sustainability The "New" Mining Engineer Autonomous Open Pit Haulage Systems (AHS) Who is in the Game? Goals (safety, fuel use, tire wear, productivity) ETF Trucks Modeling AHS

5  Safety (removal of people from danger)  Lack of skilled personnel (training costs)  Loss of equipment and availability  Decreased energy use (fuel savings)  Decreased wear, maintenance, and replacement  Increased productivity  More consistent operations Motivation behind Automation

6 Mining Truck Accidents Somewhere in the World every year at least two to three truck drivers are killed because of an accident from human error.

7 Automation of Mining Operations  Batch versus Continuous (start-up/shut-down more relevant)  Disturbances (environment, nature, human)  Maintenance Issues  Key Performance Indicators  Supervisory Control vs. Autonomous Control  Data and Process Integration ("Big" Data)  Batch versus Continuous (start-up/shut-down more relevant)  Disturbances (environment, nature, human)  Maintenance Issues  Key Performance Indicators  Supervisory Control vs. Autonomous Control  Data and Process Integration ("Big" Data)

8 Batch Processes in Mining DrillingDrilling LoadingExplosivesLoadingExplosives BlastingBlasting DiggingDigging LoadingOre/WasteLoadingOre/WasteHaulingOre/WasteHaulingOre/WasteDumpingOre/WasteDumpingOre/Waste ReturningEmptyReturningEmptyMaintenanceMaintenance

9  Weather ( rain, snow, mud, dust, wind, heat )  Equipment failures ( breakdowns, accidents )  Maintenance issues ( scheduled vs. failure )  Ground conditions ( diggability, sticky ore, rocks )  Geology ( hardness, rock size, mineralogy )  Human breaks ( shift changes, coffee/lunch )  Driving behaviour ( passive, normal, aggressive ) Disturbances

10 Evolution of Control Mode Spectrum Controller Human Operator Actuator Task Display Sensor Computer Controller Human Operator Actuator Task Display Sensor Computer Controller Human Operator Actuator Task Display Sensor Computer Controller Human Operator Actuator Task Display Sensor Computer Human Operator Actuator Task Display Sensor Autonomous Control Human is not out of the loop – rather human is elevated in the hierarchy Goal: not HARDER, but SMARTER and SAFER! Manual Control Supervisory Control

11 What the new Miners of the 21 st Century are learning to do with Process Control, Robotics, and Artificial Intelligence (i.e., Fuzzy Logic) The "New" Mining Engineer

12 The "New" Mining Engineer

13 Fuzzy Logic Control of a 1/10 scale Autonomous Vehicle The "New" Mining Engineer

14 Understands the role of Automation – Enhance workplace safety – Reduce fuel use, GHG emissions, and tire wear – Increase life cycle time of mining equipment – Stabilize and Optimize (improve) – Improve production and productivity – Reduce haulage costs appreciable

15 DARPA Grand and Urban Challenges 2004 and 2005 – Mojave Desert Victorville Vehicles drove autonomously

16 Komatsu's AHS at the Gaby Mine, Chile

17 Basic Requirements Localization – where am I? Navigation – where do I want to go? Obstacle Avoidance– what is in my path? Condition Monitoring– how is my health?

18 Komatsu – VHMS >>> KOMTRAX

19 IEEE n, ac, ad WLAN Computer hardware on-board Central data processing system Supervisory Software Modular Mining’s DISPATCH® fleet management system and MASTERLINK® communication system Front Runner Modular Mining Systems Komatsu/Modular Mining Approach

20 IEEE n, ac, ad WLAN Computer hardware on-board Central data processing system Supervisory Software CAT’s MineStar System COMMAND for hauling Caterpillar's Approach

21 IEEE Communication network Sensors for Navigation >>> GPS and Radar Object- Avoidance GPS accurate to 10 cm (D-GPS) Sensors – Localization and Navigation

22 IEEE Communication network Sensors for Navigation Object- Avoidance >>> Radar and LIDAR Radar range to 80 m Front LIDAR range to 20 m Sides and Rear mm-wave Radar Obstacle Detection System Sensors – Object Recognition and Avoidance

23 IEEE Communication network Sensors for Navigation Object- Avoidance >>> Radar and LIDAR Radar range to 80 m Front LIDAR range to 20 m Sides and Rear IBEO and SICK scanning laser instruments Sensors – Object Recognition and Avoidance

24 IEEE Communication network Sensors for Navigation Object- Avoidance >>> Radar and LIDAR Radar range to 80 m Front LIDAR range to 20 m Sides and Rear CAT’s Radar and LIDAR-based Obstacle Detection System Sensors – Object Recognition and Avoidance

25 Obstacle Detection - System Reliability Reliability Actually Present Not Present Detected Nothing Detected Measure of Success << Goal ^^^^

26 Elements of an Autonomous Haulage System Additional Sensors Wheel Speed Steering Angle Road Edge Guidance Lasers Payload Monitoring Tire Temperatures (embedded in tread) Status Lights

27 Requirements for Success A Project Champion is essential at the highest levels Long-term organization commitment based on benefits Significant workplace culture change is needed Revolutionary vs. Evolutionary Small steps better than one large step Develop new core competencies first Engage with workforce personnel Replace labour by attrition and promotion Build-in system redundancies

28 Implementation of a Successful Project ReplaceReplace Moonshot ManualManual RoboticRobotic % Autonomous KPI(core)KPI(core) KPI(core)KPI(core)

29 StagedStaged ‘Baby’ steps Implementation of a Successful Project ManualManual RoboticRobotic % Autonomous KPI(core)KPI(core) KPI(core)KPI(core)

30 ManualManual RoboticRobotic IntegrateIntegrate KPI(core)KPI(core) KPI(core)KPI(core) ?? Implementation of a Successful Project

31 KPIs may decrease initially until full adaptation Which plan is best? 1.Replace MHS with AHS in one step – no interaction 2.Isolate AHS from MHS : Separate routes, staged introduction 3.Integrate AHS with MHS: Significant safety concerns Safety concerns require careful design and planning Is a back-up or fall-back system necessary or desired? Implementation of a Successful Project

32 Develop Core Competencies Process Control fundamentals Process Control fundamentals Understanding control stability Understanding control stability Supervisory control hierarchies Supervisory control hierarchies Software algorithms Software algorithms Artificial Intelligence methods Artificial Intelligence methods Managing large databases Managing large databases Sensor knowledge and maintenance Sensor knowledge and maintenance Remote operation of equipment Remote operation of equipment

33 Change Management Requirements Mine Personnel Issues – Truck Drivers >>> Hardware/Software Maintenance – Introduce AHS with all affected personnel involved – Humans in-the-loop must be accounted for Machine Issues – Monitoring health of sensors on regular basis – Soft-sensors to confirm operational effectiveness – Data Collection to integrate into planning/scheduling

34 Mine Management Issues – Must be on-side with all decisions about the changes – New safety/traffic rules required (some are positive) – More maintenance / less operational activities – Drilling and Blasting practices must change Headquarter Issues – Move to Central Control must be done with care – Initial focus on integrating massive data collections – Decisions must support local mine site personnel Change Management Requirements

35 Who is in the Game?

36 Mines using AHS CodelcoRadomiro Tomic, ChileKomatsu Cu2005 CodelcoGabriela Mistral, ChileKomatsu Cu2008 Rio TintoWest Angelas, AustraliaKomatsu Fe2010 BHP-BillitonNavajo Coal, NM, USACATcoal2012 BHP-BillitonJimblebar mine, AustraliaCATFe2013 FortescueSolomon mine, AustraliaCATFe2013 StanwellMeandu mine, AustraliaHitachicoal2014

37 Komatsu – Codelco Radomiro Tomic mine

38 Radomiro Tomic mine Komatsu – Codelco

39 In 2006: 5 AHS 930E trucks; 32,000 tpd; 256 days Mechanical Availability: > 90% Cost per tonne reduced from $1.36 to $0.50 Est. maintenance reduction:7 % Est. depreciation reduction:3 % Gaby mine AHS trucks: 2008 – – 18 Safety issues (accidents): 2006 – – 2 Komatsu – Codelco

40 AHS trucks operate in an "electronic bubble" Each truck is aware of all other machines on site Unknown machine in AHS area causes shutdown Navigation is a hybrid of – High-precision GPS, and – Dead-reckoning IMU (accelerometers/gyroscopes) Komatsu – Codelco

41 Change how mine operations are planned & implemented Must consider all vehicles, not only AHS trucks Complexity increases exponentially with number of trucks "There are hardware restrictions...Information exchanged between trucks and central control is enormous. At Gaby, 11 trucks and 30 pieces of equipment...limit...information transfer." – Jeffery Dawes, Komatsu Chile Komatsu – Codelco

42 Rio’s “Mine of the Future” concept Began in 2008 at West Angelas Mine, Australia First 24 months – 42,000,000 tonnes – 145,000 cycles (290 t) – Short haul distance ~1.5 km 5 trucks – 25 min. cycles Ave. Velocities (initial trial): – Loaded= 7-10 kph – Empty= kph Komatsu – Rio Tinto

43 Rio Tinto's Mine of the Future – the Future is NOW!

44 Caterpillar - BHP-Billiton Joint venture at 2 mines since 2007 – Mt. Keith Nickel Mine in Australia – Navajo Coal Mine in New Mexico Initial 2 truck trial in Arizona and at Mt. Keith Planned a staged implementation from 5 > 55 > 150 Plan was adjusted after 2008 Financial Crisis Planning an Integrated Remote Operations Centre (IROC) in Perth to schedule/plan/control Pilbara mines

45 MOU with Fortesque and WesTrac in 2011 – Solomon Iron Ore Mine in Australia CAT MineStar TM system & Command for hauling Initial fleet - 12 AHS 793F trucks – 2012 At full capacity, 45 AHS trucks by Caterpillar - Fortescue

46 European Truck Factory – the next step? Decoupling Maintenance from Operation 95% Mechanical Availability

47 ETF MT-240 Truck - Haul Trains

48 ETF MT-240 Truck on Empty Haul

49 ETF MT-240 Truck Turning Circle

50 ETF MT-240 – Oscillating Axle Advantage

51 ETF MT-240 – Stability on Rough Roads

52 ETF MT-240 – Simultaneous Tipping

53 ETF MT-240 – Engine Change-out (15 min.)

54 ETF MT-240 – Axle swap-out (45 min.)

55 ETF MT-240 – tire change (15 min.)

56 ETF MT-240 – decreased tire scrubbing Conventional 240 t truck ETF 240 t truck

57 ETF MT-240 –tire size comparison

58 ETF Haulage Truck Capacities

59 Modeling Open Pit Haulage Operations

60 – Production per dayper truckper month – Fuel Useper hourper kmper tonne – Tire Wearper hrper kmper tonne – Cycle Timeincreased/decreased – Truck SpeedsIncreased/decreased – Cycles per day increased – %Mechanical availabilityincreased – %Utilizationincreased – O&M Costsdecreased Key Performance Indicators – KPIs

61 Batch or Discrete Process - Loading Shovel Waiting Shovel begins to Load Shovel Waiting Truck Driving to Spot Truck stops (an Event) Truck being Loaded Truck starts (an Event) Truck Driving from Spot (an Event) (a State) after: John Sowa, Processes and Causality, (a State)

62 Components of the Model Driver Behaviour module (fuzzy) Road Conditions module (fuzzy) Fuel Consumption module (deterministic) Tire Wear module (thermodynamic/fuzzy) Truck Movement module (deterministic) Lateral Displacement module (probabilistic) Loading, Dumping, Queuing module (stochastic) Maintenance and delays module (stochastic)

63 Route SegmentLength (m)Grade (%) Speed Limit (km/h) Maximum Acceleration (m/s²) Stop at end? Waste Shovel to Dump (5.203 km) no no yes no no 61, yes yes no yes Dump to Parking (4.330km) no yes yes 101, yes yes yes yes yes Ore Shovel to Crusher (6.041 km) no no yes 161, yes yes yes yes no yes Crusher to Parking (1.427 km) no yes yes Road Segment Characteristics

64

65 9 CAT 793D haulage trucks (7-10) Initially 3 assigned to waste Initially 6 assigned to ore Command system reschedules to address – Stripping ratio requirements (set to 0.5) – Queuing delays due to maintenance 2 shovels (one digging waste / one digging ore) Auxiliary equipment (grader, water truck, dozers) Equipment

66 Truck Movement

67 ExtendSIM Software Dynamic modeling of real-world processes Uses building blocks to explore processing steps Benefits Easy to use Inexpensive MS-Windows environment Handles both Discrete and Deterministic Models

68 Discrete and Deterministic Discrete Events Probabilistic method Maintenance, Loading, Dumping Deterministic (100 msec) First Principles Truck movement – Fuel consumption – Tire temperature Fuzzy Models (A.I.) Road conditions (rolling resistance and traction) Tire wear Driver behaviour (velocity, acceleration, reaction time)

69 Model accounts for all forces that affect motion Rolling resistance and traction Grade and Gravity Aerodynamic forces (wind) Motive power from the engine Truck Movement

70 Acceleration and velocity are limited by road conditions Significant influence of the driver behaviour Wind effects are included to account for head-winds, following winds, and side-winds (significant above 25 kph) Truck Movement

71 Truck Movement - Rimpull

72 Truck Movement - Retarding

73 Comparison of Manual vs. Autonomous

74 Comparison of Driver Behaviour Output

75 – 9-truck manual fleet and 7-truck AHS fleet (AHS set to same production as manual) – 9-truck manual fleet and 9-truck AHS fleet (different production for both cases); Significant Assumptions 1. AHS infrastructure costs = $6,690, AHS Start-up costs = $500, AHS incremental capital costs = $1,000,000 Economic Comparison

76 ∆CF = (1 – t) (∆Opex) + t∆D where ∆CF= cash flow difference between two projects t= Tax Rate of 50% (conservative analysis) ∆Opex= operating cost difference ∆D= depreciation difference (straight-line) Net Present Value is given by: i) = [∆CF/(1+i) y ] – ∆Capex + ∆S/(1+i) d where i= interest rate of 10%; y= yearand d = Project life (years); ∆Capex = capital cost difference ∆S= salvage value difference at end of life Incremental Economic Analysis

77 Initial tread depth = 97 mmSource: mine (shop visit) Ave Time to Scrap a tire (hours)= 5,300 hrsSource: mine (shop visit) Maintenance costs ($/h) = 130Source:mine report Operators per truck = 4.2Including vacation/training Labour per AHS = 0.45 Turnover = 35%Source: mine report Truck Depreciation= 7.0 yrsSource: mine (visit) Purchase price to site (Manual)= $4,000,000 Purchase price to site (AHS)= $5,000,000 Assumptions

78 Assumptions – common costs Village Cost (fly-in/fly-out)US$/person/night62.73 Flight CostUS$/person/flight Tire CostUS$/tire33,000 Fuel Price per LUS$L delivered0.90 Training cost (simulator)US$ Real Qtr25,000 MiningUS$/t 2.30 Quarterly wageUS$/person/Qtr30,000 Labour costs - HR overheads% of wage15% Hiring CostUS$/new starter3,200

79 Assumptions – AHS infrastructure Infrastructure Telecom / ITQuant.Unit $Total Basic transmission station30$30,000$900,000 Servers (with redundancy)8$12,500$100,000 Routers (24-ports/PoE)10$40,000$400,000 Switches20$5,000$100,000 Energy System (with Redundancy)1$150,000 Network Adaptation (Cables CAT 6)1$200,000 Monitoring System (Camera, SW specific, etc.)1$1,500,000 Positioning System with redundancy (DGPS, antennas, etc.)1$200,000 Subtotal$3,550,000 Services Installation and Commissioning1$700,000 Consulting (12 months)4$180,000$720,000 Project Manager (6 months)2$100,000$200,000 Transmission Link2$10,000$20,000 Training20$50,000$1,000,000 Transport/logistics1$500,000 Subtotal$3,140,000 Total$6,690,000

80 AHS Trucks to match Manual Production (37,867 tpd) Element Manual (9) 7 AHS8 AHS9 AHS % Change (AHS-Man) 7 AHS8 AHS9 AHS V full - km/h V empty - km/h Ave Fuel Use - L/t Ave Fuel Use - L/cycle Ave Fuel Use - L/hour Ave tire wear - mm/cycle Ave tire time to scrap - hrs5,5044,8765,8346, Ave. Number of Cycle/day Ave. Total Cycle Time (min) Ave. Queuing (min)/cycle Percent Utilization (%)

81 AHS Trucks to match Manual Production (37,867 tpd) Element Manual 9 trucks 7 AHS8 AHS9 AHS 7 AHS vs. Manual 8 AHS vs. Manual 9 AHS vs. Manual CAPEX (M$)*$36.00$42.19$47.19$ OPEX (M$/year)$50.17$44.63$46.08$ ∆CC (M$)----$6.19$11.19$16.19 ∆OC (M$/year)-----$5.54-$4.09-$3.06 ∆D - Depreciation (M$/year)----$0.88$0.76$0.66 ∆CF (M$/year)----$3.21$2.42$1.86 ∆SV (M$) - Salvage DCFROR----$0.00$0.65$5.23 After Tax After Tax DCFROR %11.7%-5.2%

82 AHS Trucks running at Default Speeds ElementManual*7AHS8 AHS9AHS Ave Fuel - L/tonnes Ave Fuel - L/cycle Ave tire - mm/cycle Fuel burn rate (L/h) Tire life (hours) % Utilization65%78%73%74% Maintenance (%)4.0%3.4%4.3%4.9% Annual Material Moved (t)13,821,605 16,185,56018,156,560 Years of Mining

83 AHS Trucks running at Default Speeds Element Manual 9 trucks 7 AHS8 AHS9 AHS 7 AHS vs. Manual 8 AHS vs. Manual 9 AHS vs. Manual CAPEX (M$)*$36.00$42.19$47.19$ OPEX (M$/year)$50.17$44.63$51.51$ ∆CC (M$)----$6.19$11.19$16.19 ∆OC (M$/year)-----$5.54$1.34$6.91 ∆D =Depreciation (M$/yr)----$0.88$2.72$4.65 ∆CF (M$/year)----$3.21$0.69-$1.13 ∆SV (M$) = Salvage Value----$0.00 After Tax After Tax DCFROR %14.8%9.5% Trend due to advancement of revenue from later years into early years

84 Comparison of KPIs Fuel consumption Truck haulage speeds Investment cost per truck Mechanical Availability -5% +22% -7% Tire Wear -8% Manual AHS + 20% % % % % %

85 Comparison of KPIs Increased Truck life Maintenance costs Increased Productivity Labour costs* + 22% -8% +10% -15% AHS Manual -80% * Labour savings depend on current mine circumstances – union and turnover issues

86 Conclusion “New” miners of the 21 st Century have new skills – Environment – Socio-political – Automation Mining industry is a new frontier for automation AHS is the first step in Open Pits Komatsu & CAT are well "down the road" to robotic mining Mine site management change is an important element

87 Modeling a mine is useful in determining change benefits AI methods can play a major role in developing these models Benefits of AHS – Production/productivity+ 15 to 20 % – Fuel Consumption – 10 to 15 % – Tire Wear Rate– 5 to 15 % – %Utilization+ 10 to 20 % – Maintenance – 8 % Conclusion


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