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Autonomous Haulage Trucks - the new way to mine

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Presentation on theme: "Autonomous Haulage Trucks - the new way to mine"— 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'
"…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
"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 Motivation behind Automation
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

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)

8 Batch Processes in Mining
Drilling Loading Explosives Blasting Digging Loading Ore/Waste Hauling Ore/Waste Dumping Ore/Waste Maintenance Returning Empty

9 Disturbances 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)

10 Evolution of Control Mode Spectrum
Human is not out of the loop – rather human is elevated in the hierarchy 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 Manual Control Supervisory Control Goal: not HARDER, but SMARTER and SAFER!

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

12 The "New" Mining Engineer

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

14 The "New" Mining Engineer
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 Komatsu/Modular Mining Approach
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

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

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

22 Sensors – Object Recognition and Avoidance
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

23 Sensors – Object Recognition and Avoidance
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

24 Sensors – Object Recognition and Avoidance
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

25 Obstacle Detection - System Reliability
Actually Present Not Present Detected 100 1-5 Nothing Detected 95-99 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
Manual Robotic Moonshot KPI (core) Replace KPI (core) % Autonomous 100

29 Implementation of a Successful Project
Manual Robotic ‘Baby’ steps KPI (core) Staged KPI (core) % Autonomous 100

30 Implementation of a Successful Project
Manual Robotic KPI (core) Integrate KPI (core) ? % Autonomous 100

31 Implementation of a Successful Project
KPIs may decrease initially until full adaptation Which plan is best? Replace MHS with AHS in one step – no interaction Isolate AHS from MHS : Separate routes, staged introduction 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?

32 Develop Core Competencies
Process Control fundamentals Understanding control stability Supervisory control hierarchies Software algorithms Artificial Intelligence methods Managing large databases Sensor knowledge and maintenance 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 Change Management Requirements
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

35 Who is in the Game?

36 Mines using AHS Codelco Radomiro Tomic, Chile Komatsu Cu 2005 Codelco Gabriela Mistral, Chile Komatsu Cu 2008 Rio Tinto West Angelas, Australia Komatsu Fe 2010 BHP-Billiton Navajo Coal, NM, USA CAT coal 2012 BHP-Billiton Jimblebar mine, Australia CAT Fe 2013 Fortescue Solomon mine, Australia CAT Fe 2013 Stanwell Meandu mine, Australia Hitachi coal 2014

37 Komatsu – Codelco Radomiro Tomic mine

38 Komatsu – Codelco Radomiro Tomic mine

39 Komatsu – Codelco 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: – – 18 Safety issues (accidents): – – 2

40 Komatsu – Codelco 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)

41 Komatsu – Codelco 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

42 Komatsu – Rio Tinto 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 = kph Empty = kph

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 Caterpillar - Fortescue
MOU with Fortesque and WesTrac in 2011 Solomon Iron Ore Mine in Australia CAT MineStarTM system & Command for hauling Initial fleet - 12 AHS 793F trucks – 2012 At full capacity, 45 AHS trucks by 2015

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 Key Performance Indicators – KPIs
Production per day per truck per month Fuel Use per hour per km per tonne Tire Wear per hr per km per tonne Cycle Time increased/decreased Truck Speeds Increased/decreased Cycles per day increased %Mechanical availability increased %Utilization increased O&M Costs decreased

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

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 Maximum Acceleration (m/s²)
Road Segment Characteristics Route  Segment Length (m) Grade (%) Speed Limit (km/h) Maximum Acceleration (m/s²) Stop at end? Waste Shovel to Dump (5.203 km) 1 98 25 0.42 no 2 537 5 40 3 680 7 0.21 yes 4 761 10 44 6 1,502 500 8 944 60 9 137 Dump to Parking (4.330km) -5 -2 1,201 11 300 12 353 13 554 14 359 Ore Shovel to Crusher (6.041 km) 15 78 16 1,904 17 867 18 968 19 100 Crusher to Parking (1.427 km)

64

65 Equipment 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)

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

70 Truck Movement 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)

71 Truck Movement - Rimpull

72 Truck Movement - Retarding

73 Comparison of Manual vs. Autonomous

74 Comparison of Driver Behaviour Output

75 Economic Comparison 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 = $6,690,000 2. AHS Start-up costs = $500,000 3. AHS incremental capital costs = $1,000,000

76 Incremental Economic Analysis
∆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 = year and d = Project life (years); ∆Capex = capital cost difference ∆S = salvage value difference at end of life

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

78 Assumptions – common costs
Village Cost (fly-in/fly-out) US$/person/night 62.73 Flight Cost US$/person/flight 169.86 Tire Cost US$/tire 33,000 Fuel Price per L US$L delivered 0.90 Training cost (simulator) US$ Real Qtr 25,000 Mining US$/t 2.30 Quarterly wage US$/person/Qtr 30,000 Labour costs - HR overheads % of wage 15% Hiring Cost US$/new starter 3,200

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

80 AHS Trucks to match Manual Production (37,867 tpd)
Element Manual (9) 7 AHS 8 AHS 9 AHS % Change (AHS-Man) V full - km/h 17.4 18.5 13.4 12.7 6.2 -23.1 -26.8 V empty - km/h 27.1 28.6 23.4 17.2 5.7 -13.6 -36.4 Ave Fuel Use - L/t 0.83 0.76 0.78 -8.1 -0.1 -5.1 Ave Fuel Use - L/cycle 185.3 168.4 209.3 223.9 -9.1 12.9 20.9 Ave Fuel Use - L/hour 218 235.5 252 239.4 8.1 15.6 9.9 Ave tire wear - mm/cycle 0.015 0.014 0.013 -5.0 -7.8 -13.8 Ave tire time to scrap - hrs 5,504 4,876 5,834 6,029 -11.4 6.0 9.6 Ave. Number of Cycle/day 18.9 24.5 21.1 19 29.4 11.9 0.4 Ave. Total Cycle Time (min) 51 42.9 49.8 56.1 -15.9 -2.3 10.0 Ave. Queuing (min)/cycle 1.8 0.9 1.0 -49.4 -50.8 -47.8 Percent Utilization (%) 65 78 73 74 19.4 14.1

81 AHS Trucks to match Manual Production (37,867 tpd)
Element Manual 9 trucks 7 AHS 8 AHS 9 AHS 7 AHS vs. Manual 8 AHS vs. Manual 9 AHS vs. Manual CAPEX (M$)* $36.00 $42.19 $47.19 $52.19 - OPEX (M$/year) $50.17 $44.63 $46.08 $47.11 ∆CC (M$) $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 $9.45 $1.26 -$1.92 After Tax DCFROR 48.7% 11.7% -5.2%

82 AHS Trucks running at Default Speeds
Element Manual* 7AHS 8 AHS 9AHS Ave Fuel - L/tonnes 0.83 0.78 Ave Fuel - L/cycle 185.27 172.53 172.91 172.89 Ave tire - mm/cycle 0.015 0.014 Fuel burn rate (L/h) 218 236 252 239 Tire life (hours) 5504 4876 5834 7029 % Utilization 65% 78% 73% 74% Maintenance (%) 4.0% 3.4% 4.3% 4.9% Annual Material Moved (t) 13,821,605 16,185,560 18,156,560 Years of Mining 7 5.98 5.33

83 AHS Trucks running at Default Speeds
Element Manual 9 trucks 7 AHS 8 AHS 9 AHS 7 AHS vs. Manual 8 AHS vs. Manual 9 AHS vs. Manual CAPEX (M$)* $36.00 $42.19 $47.19 $52.19 - OPEX (M$/year) $50.17 $44.63 $51.51 $57.08 ∆CC (M$) $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 $9.45 $3.36 $15.59 After Tax DCFROR 48.7% 14.8% 9.5% Trend due to advancement of revenue from later years into early years

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

85 Increased Productivity
Comparison of KPIs AHS AHS AHS AHS + 22% -15% +10% -8% -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

86 Conclusion “New” miners of the 21st 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 Conclusion 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 to 20 % Fuel Consumption – 10 to 15 % Tire Wear Rate – 5 to 15 % %Utilization to 20 % Maintenance – 8 %


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