A 3D discrete-event simulation model of truck-shovel network system. W. Zeng, E. Baafi, D. Walker and D. Cai

Transcription

1 A 3D discrete-event simulation model of truck-shovel network system W. Zeng, E. Baafi, D. Walker and D. Cai

2 Introduction Truck-shovel Mining System Queuing Hauling empty Loading Hauling loaded Dump Dumping

3 Introduction System Efficiency Fleet Size Match Factor = (Number of trucks)(loader cycle time) (Number of loaders)(truck cycle time) Match Factor > 1 Sufficient trucks Trucks wait Match Factor < 1 Insufficient trucks Shovels wait Match Factor = 1 Balance point

4 Introduction Simulation approach is more suitable for modelling truck-shovel system. Queuing Hauling empty Dump Loading Loader type Truck type Loading method Material characteristics Operator s skill Hauling loaded Dumping Truck rimpull chart Route design & conditions Truck weight Traffic constraints: bunching, intersection

5 Simulation model development JaamSim

6 Simulation model development

7 Simulation model development

8 Simulation model development

9 Simulation model development

10 Simulation model development Material handling process in the model OreGenerator Any truck at loader? Y Loader Route Dump OreSink N Truck flow Entity flow

11 Simulation model development Some of the common truck-shovel simulation software packages have limitations: TALPAC One single loading unit at any time FPC Limitations for statistic inputs for loading and dumping events Developed Model Stochastic variables for statistic inputs Truck-shovel haulage network system

12 A truck-shovel network system model A simple truck-shovel network system layout (BHP Billiton Iron Ore Eastern Ridge OB23/25) D3 S4 D2 S1 S2 S3 D4 D1 Si: Shovel i Di: Dump i Assumptions: Only one truck is served at one time at a shovel or a dump. Each truck is assigned to a fixed route (non-dispatching mode). The trucks are allowed to overtake each other along the routes. The passing priority in intersection area is not considered. The simulation model performs 11 hours per shift during one simulation run. Hot seat shift changeover and no operation delays in the network system.

13 A truck-shovel network system model Model Inputs ( based on the time and motion study conducted at a mine site by Shaw (2014)) Haul route input The haul route spatial data includes the length and elevation of each segment of routes, locations of intersections. Truck type CAT 785C CAT 789C Empty weight(kg) Truck parameter input Capacity(kg) Length(m) Width(m) Height(m)

14 A truck-shovel network system model Shovel loading time input Shovel type Distribution Expression Liebherr 9250 Normal Normal(122.35, 18.08) Hitachi 1900BE (loading 785C) Hitachi 1900BE (loading 789C) LogNormal LogNormal(5.19, 0.16) Normal Normal(250.6, 33.14) Dumping time input Truck type Distribution Expression CAT 785C Normal Normal(35.77, 11.02) CAT 789C Normal Normal(46.88, 11.97)

15 A truck-shovel network system model Model outputs Truck queuing time Truck cycle time Shovel production Shovel utilisation Match factor

16 Model implementation and analysis Fleet sub-optimisation method S4 2 D3 D2 S1 S S3 D4 D1 Si: Shovel i Di: Dump i

17 Model implementation and analysis System Productivity System Efficiency Shovel utilisation Truck utilisation Over-trucking system (MF >1) Shovel utilisation Truck utilisation Productivity Operating expense (OPEX) Under-trucking system (MF <1) Shovel utilisation Truck utilisation Productivity Operating expense (OPEX) Capital expense (CAPEX)

18 Model implementation and analysis Simulation screenshot a

19 Model implementation and analysis Simulation screenshot b

20 Model implementation and analysis Simulation screenshot c

21 Model implementation and analysis Case 1: Over trucking to maximise production at the expense of OPEX Loader Truck fleet Ave waiting time (s) Ave cycle time (s) Truck utilisation Shovel utilisation Shovel production (t) Shovel % 99% Shovel % 99% Shovel % 99% Shovel % 99% MF Shovel utilisation 99% Truck utilisation 90%-95% Truck fleet size: 28 System shift production: t MF: 1

22 Model implementation and analysis Case 1: Over trucking to maximise production at the expense of OPEX Cost analysis Truck operating cost Truck queuing time 400 (AU$ per hour) x (hrs) + Shovel operating cost Shovel idle time = 800 (AU$ per hour) x 0.4 (hrs) Total OPEX (AU$ per shift) Or (AU$ per ton)

23 Model implementation and analysis Case 2: Slightly under trucking to reduce OPEX savings at the cost of production Loader Truck fleet Ave waiting time (s) Ave cycle time (s) Truck utilisation Shovel utilisation Shovel production (t) Shovel % 88% Shovel % 92% Shovel % 92% Shovel % 85% MF Shovel utilisation around 90% Truck fleet size: 24 Truck utilisation >= 97% System shift production: MF:

24 Model implementation and analysis Case 2: Slightly under trucking to reduce OPEX savings at the cost of production Cost analysis Truck operating cost 400 (AU$ per hour) Shovel operating cost 800 (AU$ per hour) 4 trucks saved Truck queuing time x 7.43 (hrs) + Shovel idle time = x 4.76 (hrs) CAPEX (AU$ per shift) Or (AU$ per ton) Total OPEX (AU$ per shift) Or (AU$ per ton) Total OPEX (AU$ per shift) Or (AU$ per ton)

25 Model implementation and analysis Case 1 - Case 2 Truck fleet size Truck queuing time (hrs) 7.43 (hrs) (hrs) Shovel idle time 0.4 (hrs) 4.76 (hrs) (hrs) Total OPEX (AU$ per shift) Or (AU$ per ton) (AU$ per shift) Or (AU$ per ton) (AU$ per shift) Or (AU$ per ton)

26 Conclusion The developed simulation model provides the capability of estimating the equipment performance for not only a single truck fleet but also for multiple truck fleets in the entire truck-shovel network system. The best truck fleet combination can be determined by varying shovel priorities. This capability offers management a tool to evaluate the performance and operating costs of the entire truck-shovel network system of a mine.

27 Thank you!