Simulation of an Open Pit Mine to Study Autonomous Haulage Trucks

Juliana Parreira, John Meech Norman B. Keevil Institute of Mining Engineering,

The University of British Columbia, Vancouver, BC, Canada, V6T1Z4 Corresponding author: (jpareng@hotmail.com)

Abstract

This paper describes a simulation model that compares an Autonomous Haulage Truck (AHT) system to a manual one by estimating benchmarked Key Performance Indicators (KPIs) such as productivity, safety, maintenance and labour costs, cycle time, fuel consumption, and tire wear. The model extends conventional shovel/truck simulation into a variety of truck sub-systems such as truck movement, driver behaviour, fuel consumption, and tire wear to capture the mechanical complexities and physical interactions of these sub-systems with the mine environment on a 24/7 time basis. Running the model in identical scenarios for the two cases allows comparison of benchmarked KPIs that demonstrate the utilization and adaptability of an AHT.

Biography

Juliana Parreira has a Bachelor's degree in Electrical Engineering and is currently completing a doctoral program focused on autonomous haulage trucks at the Norman B. Keevil Institute of Mining Engineering, UBC. She has five years of experience in the oil industry and two years in the electric power industry.

John Meech is Professor of Mining Engineering at the Norman B. Keevil Institute of Mining Engineering where he teaches and conducts research in Automation and Process Control, Artificial Intelligence techniques, and Mining and the Environment.

Introduction Over the past decade, mining companies have been challenged by increasing global demand, fluctuating commodity prices, difficult-to-access minerals due to location and harsh environments, as well as social and environmental issues. Automation is one tool that can be used to maximize production, reduce costs, and create a safer work environment in a customer-driven economy that demands rapid response. In particular, automation of open pit haulage systems is receiving attention to remove workers from potential dangers, to reduce costs, and to help improve mining equipment efficiency.

This research project is examining how an open pit mine can adapt itself to an autonomous haulage fleet by creating a tool to compare conventional and Autonomous Haulage Truck (AHT) systems. What level of improvement in KPIs can be achieved using an AHT? What aspects of a mine haulage system must change to adapt to an AHT? In order to answer these questions, two discrete-event models (one manual and one autonomous) were developed to predict benchmarked KPIs: productivity; safety; maintenance and labour costs; cycle times; fuel consumption; and tire wear under different road and load conditions. The scope of the work is limited to offline simulation software that project managers can use to guide decision-making on the possible application of an AHT system in a specific mine.

Model Overview The model has been built using the ExtendSim simulation software suite. ExtendSim is a graphical discrete-event tool that can break a network down into unique components each having specific time delays and characteristics with respect to maintenance, speed, fuel consumption, braking, acceleration, etc. A system database is used to input model parameters such as route characteristics, weather, equipment fleet parameters, driver behaviour, and stochastic sets (averages and variances) that characterize different interruptions and delays. The data is held in a RAM-resident database that opens, saves, or closes whenever the model is opened, saved, or closed respectively.

The test model consists of two shovels, one digging waste and one assigned to ore production. According to a specified stripping ratio, one third of the fleet (9 trucks in total) are set to work with the waste shovel with the other six are assigned to the ore shovel. The model can designate dedicated service or trucks can be reassigned on-the-fly after dumping depending on the current delay situation. All trucks in the model are CAT 793D, each having a nominal GMW of 383,749 kg, a total net power of 1,743 kW, and equipped with standard radial tires 40.00R57. Different trucks can be easily configured through the database.

For each segment of a haulage route, the movement of each truck, its fuel consumption, and tire wear are determined using a continuous (or deterministic) approach. The objective is to handle speed and acceleration control issues at any point in time and to calculate fuel consumption and tire wear for each time step (t = 0.1 seconds). The Gross Machine Weight (GMW), truck resistance forces, traction, and drive axle forces are used to calculate the resultant force causing movement. For each t, a new force and acceleration is calculated until the truck's cumulative travelling distance equals the total distance of the segment. At the end of each road segment, data is initialized for the next segment with simulation continuing until the truck reaches its final destination.

The mine being modeled is a partial haulage system at a real mine site. A total of 19 road segments were distributed over 4 haulage routes: Ore Shovel to Crusher; Waste Shovel to Dump; Dump to Parking; and Crusher to Parking. If the truck is set at the beginning of the simulation to attend the waste shovel, its overall route is waste shovel to dump, while for trucks set to work on ore production, the route is ore shovel to crusher. When a truck arrives at a shovel queue, it waits for the shovel to become free. Values for spotting and loading times are set stochastically. After loading, the truck leaves the shovel and travels to its dumping site. At the dump or crusher queues after waiting for the site to become free, the truck reverses and dumps with reversing and dumping times selected stochastically. All queuing times depend on the presence and number of other trucks in the queue. After unloading, the truck returns to its original route and continues in this loop until an event or stochastic delay occurs unless the shovel is down for maintenance. In that case, the truck is reassigned either to maintenance or the other shovel. The model is run over a simulated real-time of at least 14 days of continuous operation.

A refuelling delay takes place whenever the tank level falls below a set minimum value (10% of a full tank) when the truck is at a dumping location. When that occurs, the truck dumps its payload and is reassigned to the refuelling station (Parking). A stochastic refuelling time is selected to characterize the time to complete this event. If a truck requires maintenance or a driver needs a break (lunch, coffee, or shift change), the truck drives to parking, but only after its load is dumped. When running in Autonomous Haulage mode, driver breaks do not happen, but other event delays may occur such as tire cool-down.

After a truck is repaired, values for MTBF (mean time to failure) and MTTR (mean time to repair) are reset. During the simulation, data is stored and managed in the internal database and the relevant run results are exported to an Excel template spreadsheet when the simulation run is completed. The model flowchart is shown in Figure 1.

Figure 1: Overview of Model Flowchart.

Truck Movement The truck movement model is based on the assisting and opposing forces such as Rimpull available, usable, required (FRR) and drag forces (FD). Rimpull available is the force produced by the engine delivered at the point that the tires contact the ground. Usable Rimpull deals only with the weight on the drive wheels and the friction between the tire and the ground [King Fahd University, 2004/2005]. Feffective is the lower value between rimpull available and rimpull usable. Knowing the resultant forces and payload, acceleration can then be calculated [Parreira, et.al, 2011]. Acceleration is calculated deterministically for each time step and distinguishes between acceleration responsible for movement and that responsible for fuel consumption. When a truck is moving on a flat road, acceleration responsible for truck movement is:

Accresultant = (Feffective - Fresist) / M Eq. 1.

where Feffective is the assisting force propelling the truck; Fresist is the force opposing movement; and M is the truck weight and payload. If the grade is positive, Fresist = Fgravitational + Frolling Fdrag. If the grade is flat, the equation becomes Fresist = Frolling Fdrag. The value of Fdrag depends on wind direction and speed which may either oppose or assist with truck movement.

Figure 2: Truck Forces when Moving on a Grade = 0%.

Figure 3: Truck Forces when Moving on a Grade < 0% and > 0% respectively.

If the grade is negative and the gravitational force (normal component of truck weight plus payload) is sufficient to trigger truck motion, then this force is used as the truck assisting force. However, if the weight is not enough, an extra force (Rimpull available) must be produced by the engine. After calculating Accresultant, the model applies a factor to this value dependent on driver behaviour. The value is limited to the maximum acceleration of the type of driver in question as discussed below. When a truck reaches the velocity set point for the road segment or the limiting velocity based on driver behaviour, Accresultant is set to a value close to zero. A slight variation exists, also dependent on driver behaviour.

Driver Behaviour The objective of the driver model is to generate stochastic differences in driver behaviour to obtain output ranges for fuel consumption, tire wear, cycle times, and production for a manually-operated system that mimics real mine site data. These ranges can then be compared to that achieved by a simulated AHT system in which the variances and deviations from normal or accepted results are significantly reduced.

A parameter called the Aggressiveness Factor is used to represent how a person drives. A driver can be aggressive, normal, or passive. A driver is very stable, stable, or variable such that 9 different behaviours result. This factor is set at the beginning of the simulation with the fleet crew being distributed amongst these 9 types. Autonomous trucks are set to the normal-stable driver behaviour with a smaller deviation than a human. The Aggressiveness Factor is applied to the acceleration, velocity, and reaction time set points for each driver/truck which can trend up or down during the shift [Meech et al., 2011] to reflect slight changes in behaviour. Some drivers become more aggressive while others become more passive. Figure 4 shows 3 different driver behaviours over a simulated 1,000 m segment with a -12% effective grade. There are 9 different driver types but 3 are dominant: passive-very stable; normal-stable; and aggressive-variable.

Figure 4. Acceleration and velocity of different drivers over a 1,000 m haulage segment with a 12% effective grade from ExtendSim Model.

Fuel Consumption For this sub-model, truck manufacturer data combined with fundamental physics is used to estimate fuel use in L/hour. Instantaneous velocity is calculated for each time interval t. By knowing the desired velocity, engine speed is calculated and then correlated to Power and BSCF [Parreira, et.al, 2011]. Fuel consumption is estimated by the following equation:

Cf = Csf (Powerused / f) Eq. 2.

where: Cf = fuel consumption rate (L/h) Csf = Brake Specific Fuel Consumption (BSFC) during time interval t (g/kWh) (see Figure 5) f = fuel density (g/L) Powerused = power produced by the engine during time interval t (kW) based on Figure 5.

Figure 5. Instantaneous Power and BSCF vs. Engine Speed - from fuel consumption model.

In the example shown in Figure 6, using gear 1 at 1739 rpm consumes 451 L/h. However, if the driver switches to gear 2, fuel consumption decreases to 383 L/h (a reduction of 15%). The model gives fuel consumption according to which gear is in use. When human behavior is eliminated from the system, there is an opportunity to redesign haulage roads based on this increased efficiency and more precise engine speed changes and acceleration set points.

Figure 6. Using the correct gear leads to a 68 L/h reduction in fuel consumption.

Tire Wear In general, tire wear for a 40.00R57 radial tire on a CAT 793D truck is about 75 mm over an operating life of 6,000 to 6,500 hours. Tire treads are measured on a regular basis during routine maintenance and tires are removed from service when the depth falls below about 25 mm from an initial depth of 100 mm.

We have created a sub-model based on fuzzy logic that correlates tire wear to truck velocity and payload. The model can be calibrated to reflect known operating conditions at any specific mine site and can be adjusted throughout the life of a set of tires to reflect the current tire condition on a truck. The model also predicts changes in tire temperature as a truck operates in order to account for wear rate differences when hauling under full load or returning to the shovel at a higher velocity with zero load. The idling time during loading, dumping, and queuing is also accounted for in the temperature model. Figure 7 shows the contour plot of tire wear rate in mm/10,000km (as a % of maximum), depending on payload and velocity.

Figure 7. Tire Wear vs. Payload at Different Velocities Accumulation Method of Defuzzification. Calibration Factors: Maximum Speed = 40 kph; Maximum Payload = 400 t;

Maximum Tire Wear = 10 mm / 10,000 km

Note that the two boxes that represent typical ranges for full-load and empty conditions indicate similar tire wear rates since one is at a higher GMW while the other is at a higher velocity. Three parameters are required to calibrate the model - maximum tire wear rate in mm/10,000 km, maximum velocity in kph, and maximum payload in tonnes.

Many mines use tire suppliers' TKPH model to reduce tire failures and avoid overheating tires during operation. Using this model provides a reliable and practical constraint beyond which a truck must stop to allow tires to cool-down or the truck is reassigned to a longer haulage route and restricted to 4th gear (to prevent speeding). When the real-time TKPH calculation returns to normal, the truck is reassigned to normal service. While this may help reduce failures from cut or blown tires, it doesn't provide a real-time measure of temperature or wear rate. Furthermore, restricting gear changes will lead to increased fuel use.

(a)

(b)

(c)

Figure 8. Effect of idling time on tire temperature during the haulage cycle. Idling time as % of total cycle time: (a) 14.7%; (b) 9.3%; (c) 9.3% at elevated velocities.

We believe there is significant advantages to be gained from a system in which tire temperature is monitored directly and used with an empirical model to provide a real-time measure of tire wear for 40.00R57 radial tires on a CAT 973D truck. Figures 8a and 8b shows the theoretical model predictions of

how a tire heats up and cools down under constant travelling conditions. The model is designed so it takes about 60 minutes of constant driving at 16 kph under target payload conditions to arrive at a steady state temperature of 75C from an initial ambient temperature of 35C, while cool-down under idling conditions requires about 120 minutes to return to ambient conditions.

Figure 8a shows that when idling time represents about 15% of total cycle time, a steady state temperature of 61 C is reached, whereas when the idling time is only about 10% of total cycle time, the steady state temperature rises to 80 C as in Figure 8b. Figure 8c shows what can happen when truck velocities are increased to 19 and 38 kph for loaded and empty conditions respectively. Although the rise in temperature initially is lower than in Figure 8b, the system is unstable with the temperature continuing to rise well above the danger point of 90 C after 9 cycles. Clearly, this truck will require cool-down.

Results and Validation The model output shown in Tables 1 and 2 is based on a 14-day work period; the crew make-up was set to 33% aggressive, 44% normal, and 22% passive. In the autonomous model, all trucks were 100% normal, i.e. best drivers, with a lower variance. The AHT system shows a 14.4% improvement in production, a decline of 12.9% in fuel consumption (L/t) and a 7.2% decline in tire wear (mm/t). The tire wear model is currently being validated against real mine data. Fuel consumption and truck movement are both close to the measured data. These improvements are now being validated.

The set points for acceleration and speed according to driver behaviour have also been calibrated against 12 drivers at the mine; those who drive slower and those who are more aggressive. The tire wear model is the most difficult one to validate since most mines simply measure tire wear based on TKPH. The tire manufacturer sets a "theoretical" TKPH value and the mine dispatch system verifies if the real truck TKPH is higher than this set point based on haulage distances and payloads. We believe that improvements can be derived from direct measurement of tire temperature to predict tire wear rates. An AHT fleet will work 24/7, so sensors on belt tires will be necessary to ensure tire cool-down is optimized since an AHT fleet will operate for longer periods of time.

Table 1: Comparison of different drivers - from model template spreadsheet.

Element Passive Normal Aggressive Autonomous

Total Cycle Time min. 53.5 47.3 42.5 50.5

Idling and

Spotting Time

min. 5.6 5.3 4.8 5.1

% 10.5 11.2 11.3 10.1

Fuel Consumption (Idling) L/hr 26 27 28 27

Fuel Consumption (Full) L/hr 366 415 437 410

Fuel Consumption (Empty) L/hr 134 138 179 127

Total Litres/cycle 261.2 289.5 314.4 286.5

% difference -9.8 0.0 8.6 -1.0

Total Litres/tonne 1.35

% difference -12.9

Tire Wear Rate (Idling) mm/hr 0.0032 0.0032 0.0032 0.0032

Tire Wear Rate (Full) mm/hr 0.0177 0.0253 0.0299 0.0212

Tire Wear Rate (Empty) mm/hr 0.0096 0.0120 0.0164 0.0106

Tire Wear (mm/hr) 0.0139 0.0149 0.0152 0.0138

% difference -6.7 0.0 2.3 -7.2

Tire Wear (mm/ 10,000 tonnes) 0.536

% difference -6.0

It is interesting to note in Table 1 that passive drivers have lower tire wear than do the aggressive drivers and they use less fuel. Note that the AHT system matches the tire wear of the passive drivers and uses less fuel than the average driver. This translates to an 8.1% reduction in fuel used per tonne of material moved. Assumptions about maintenance have held delay times due to repairs relatively close to those measured for the manual system except for unplanned maintenance which is expected to decline by about 10% as can be seen in Table 2.

Table 2: Productivity of Manual Haulage versus AHT System - from model template spreadsheet.

Element Manual Autonomous

%Difference - hours - Hours

Number of Cycles/day 19.82 - 22.35 - 14.00

Ave. Total Cycle Time/truck - min. 46.8 - 50.5 - 5.71

Total Haulage Time/day/truck - 15.46 - 18.79 23.13

Shift Change/day/truck - 0.60 - 0.00 -100.00

Coffee and Break Time/day/truck - 2.50 - 0.00 -100.00

Average Process Delay Time/day/truck - 1.64 - 1.60 -2.44

Unplanned Maintenance/day/truck - 1.16 - 1.04 -10.34

Planned Maintenance/day/truck - 2.64 - 2.67 1.14

Percent Utilization (%) 63.6 78.3 - 23.14

Total Production (tonnes) /day/truck 4,235 - 4,844 - 14.35

Ave. Production / cycle/truck 213.7 - 216.7 - 1.40*

* this difference should be close to zero, meaning the simulation needs to be run for more than 14 days.

Conclusion This model is in verification/validation process and when finished, it will be able to provide a precise comparison between manual and autonomous haulage trucks regarding KPIs such as productivity, safety, maintenance, labour costs, cycle time, fuel consumption, and tire wear. The model is structured in a way to allow quick adjustment to simulate any open pit mine that is considering an autonomous haulage fleet in the future.

Acknowledgment The authors wish to thank BHP-Billiton Nickel West Division for supporting this research.

References King Fahd University of Petroleum & Minerals, Construction Engineering & Management Department:

Construction Equipment & Methods (2004). Course: CEM 530 (Spring 2004/2005) Meech, J., Parreira, J. (2011) . An interactive simulation model of human drivers to study autonomous

haulage trucks. Complex Adaptive System Conference. Procedia Computer Science, 6, 118-123. Parreira, J., Meech, J. (2011) . Autonomous Haulage Systems Justification and Opportunity.

Autonomous and Intelligent Systems, International Conference IS-2011. Springer, 63-72