24-MMP-A5 Surface Mining Methods and Design · May 2014
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, 2014-May. 3 hours duration, closed book; one hand-written 8.5×11 inch reference sheet and an approved Casio or Sharp calculator permitted. Question 1 is compulsory (40 marks, all six parts 1.1–1.6); a candidate then selects THREE of Questions 2–6 (each worth 20 marks).
Reference texts: Hartman & Mutmansky (eds.), SME Mining Engineering Handbook, 3rd ed. (dragline stripping systems, truck-shovel productivity, mine cost estimation — the primary reference throughout this paper); Hustrulid, Kuchta & Martin, Open Pit Mine Planning and Design, 3rd ed. (block-model economics, floating/moving-cone algorithm, the Lerchs–Grossmann graph-theoretic pit-optimization method, annual push-back scheduling); Kennedy, B.A. (ed.), Surface Mining, 2nd ed., SME (dragline range-diagram geometry, stripping methods); Lerchs, H. & Grossmann, I.F. (1965), “Optimum Design of Open-Pit Mines,” CIM Bulletin, 58, 47–54.
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.
3.1 Applying Pareto’s Law (the 80/20 rule). Pareto’s Law says that, in almost any cost breakdown, a small fraction of the line items accounts for most of the total cost. To apply it: pull the full general-ledger cost detail for the cost centre (drilling, trucking, or shovel loading) over a representative period, sort every line item from largest to smallest dollar total, and plot (or simply tabulate) the cumulative percentage of total cost against the cumulative count of line items. The point where the curve flattens — typically after the top 2–4 items, which usually account for 70–80% of the sector’s cost — identifies the “vital few” cost drivers that deserve engineering/procurement attention; the long tail of small remaining items (“trivial many”) is not worth the analytical effort of itemizing individually. This lets a mine engineer isolate the 2–3 numbers that actually move the sector’s total cost without auditing every account.
Illustrative major cost items and approximate shares (representative hard-rock truck/shovel operation; the exam explicitly gives an example for blasting as a model — actual splits are mine- and contract-specific):
| Cost sector | Major items (approx. % of sector total) |
|---|---|
| 3.1.1 Drilling (blast-holes) | Drill bits/consumables ~30%; labour & benefits ~25%; fuel/power ~20%; maintenance parts ~15%; other ~10% |
| 3.1.2 Truck (haulage) | Fuel ~30%; tyres ~25%; labour & benefits ~20%; maintenance parts ~15%; other ~10% |
| 3.1.3 Shovel (loading) | Labour & benefits ~30%; ground-engaging tools/dipper teeth ~25%; maintenance parts ~20%; power/fuel ~15%; other ~10% |
3.2 — Inflation indexes. Once the vital-few cost items in 3.1.1–3.1.3 are identified, a mine can escalate FUTURE estimates far more accurately by applying a SEPARATE inflation index to each major item category (a fuel-price index to fuel, a tyre/rubber commodity index to tyres, a wage index to labour) rather than one blended plant-cost index to the whole sector total — since Question 1.3 already showed a single index mis-tracks a mixed cost basket, using Pareto’s result to target the 2–3 indexes that actually matter converts a rough escalation into a defensible one for a small extra analytical cost.
Given (3.3). Parametric capital-cost model $P = aX^b$ (1997 basis), three equipment classes with their own $a$, $b$ and sizing variable $X$ (drill: pull-down force in lb; shovel: bucket capacity in yd³; truck: capacity in short tons), plus a 1997→today escalation factor of 2.00.
| Equipment | a | b | Size given |
|---|---|---|---|
| Drill | 400 | 0.67 | 55,000 kg pull-down |
| Shovel | 540,000 | 0.75 | 53 m³ bucket |
| Truck | 20,000 | 0.90 | 300 t capacity |
Find. The 1997 and escalated-to-today capital cost of each machine.
Approach. Convert each given size into the units the model expects (lb, yd³, short tons), evaluate $P=aX^b$ for the 1997 cost, then multiply by the given 2.00 escalation factor for today’s cost.
| Machine | 1997 cost | Today's cost (×2.00) |
|---|---|---|
| Drill (55,000 kg pull-down) | USD 1,018,913 | USD 2,037,825 |
| Shovel (53 m³ bucket) | USD 12,973,112 | USD 25,946,224 |
| Truck (300 t) | USD 3,702,653 | USD 7,405,305 |
Are the escalated values realistic? Only partly. A flat ×2.00 multiplier correctly captures general monetary inflation over the intervening years, but (per Question 1.3) it does NOT capture the real productivity/technology gains this equipment class has seen since 1997 (larger, more automated, more fuel-efficient machines at a given nameplate size), nor the exchange-rate and freight differences for a Canadian buyer importing from a US/European OEM, nor genuine step-changes in steel and rare-metal input costs. In practice an index-escalated 1997 parametric estimate is a reasonable ORDER-OF-MAGNITUDE check (±20–30%) but should always be cross-checked against a current OEM quotation before being used for a sanction-grade capital estimate.
