24-MMP-A5 Surface Mining Methods and Design · May 2013
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
EGBC National Exam — Mining and Mineral Processing Engineering, 09-Mmp-A5 Surface Mining Methods and Design, 2013-May. 3 hours duration; one handwritten 8.5×11 in reference sheet permitted (not an open-book exam); only approved Sharp or Casio calculators allowed. Question 1 is compulsory (40 marks, parts 1.1–1.7); candidates then select FOUR of the six optional Questions 2–7 (15 marks each) to complete the paper.
Reference texts: Hartman & Mutmansky, SME Mining Engineering Handbook, 3rd ed. (dewatering, slope stability classification, dragline stripping geometry, truck dispatch, mine closure); Hustrulid, Kuchta & Martin, Open Pit Mine Planning and Design (moving-cone and Lerchs–Grossmann pit optimization, capital-cost estimating, truck-shovel match factor); Lerchs, H. & Grossmann, I.F. (1965) “Optimum Design of Open-Pit Mines,” CIM Bulletin (the graph-theoretic 2-D worked example this question is drawn from); O’Hara, T.A. (1980) “Quick Guides to the Evaluation of Orebodies,” CIM Bulletin, Feb. 1980, and Mular, A.L. & Poulin, R. (1998) CANCOST, CIM Special Volume 47 (capital-cost formulae); Bieniawski, Z.T. (1989) Engineering Rock Mass Classifications (RMR system).
Question text not reproduced: the examination questions are © Engineers and Geoscientists BC. Open the official past paper (linked at the top of this page) to read the question, then follow the worked solution below.
Closed out vs. dispatched. A closed-out truck is permanently assigned to one fixed shovel–destination loop (e.g. Shovel 1 always to the crusher and back) regardless of what is happening elsewhere in the pit; it is the manual, fixed-route way of running a truck fleet. A dispatched truck has its destination assigned dynamically, load by load, by the dispatch system (or a human dispatcher) based on the real-time state of every shovel and dump in the pit, so a truck that finishes dumping can be sent to whichever loading unit currently needs it most – exactly the distinction exploited quantitatively in Question 5.4 below, where routing trucks over the short cross-links instead of forcing them back to their own shovel cuts the required fleet by 25%.
2.1 Maximized production. The dispatch algorithm (commonly a linear-programming or “best-path” assignment run every few minutes) continuously matches available trucks to the shovel with the shortest queue / highest current productivity, minimizing truck queueing at the shovel and shovel idle time waiting for a truck, so that the combined truck-shovel system approaches its theoretical maximum tonnes/hour.
2.2 Minimized equipment. By balancing the fleet across all active shovels (rather than each shovel needing its own dedicated, worst-case-sized sub-fleet), the same total production is achieved with fewer trucks – the dispatched vs. closed-out comparison in Question 5.4 shows this directly (12 dispatched trucks vs. 16 closed-out trucks for the same two-shovel production).
2.3 Optimal mill head grade. The system tags each truckload with the grade of the block it was loaded from (from the block model/short-term plan) and can route ore loads to blend crusher feed toward the target head grade in real time – sending a high-grade load to the crusher now and a marginal load to a low-grade stockpile, rather than simply sending every ore truck to the mill in loading order.
2.4 Maintaining the stripping ratio. The dispatcher tracks cumulative waste and ore tonnes moved against the short-term plan’s target stripping ratio and can bias truck assignments toward waste or ore faces as needed to keep the mine on its planned exposure sequence, preventing the common failure mode of a fleet drifting toward whichever material is easiest to load that shift.
2.5 Computerised database. Every truck cycle logged by the dispatch system (load location, tonnage, cycle time, delays, destination) accumulates into a production and equipment-utilization database that supports payroll/production reporting, condition-based maintenance scheduling (cycle counts and engine hours per unit), consumable tracking (tyres, GET) and, over the longer term, statistically grounded planning for future truck purchases sized to the fleet’s actual measured cycle-time and utilization history rather than a first-principles estimate alone.
