24-MMP-A4 Mine Valuation and Mineral Resource Estimation · May 2017
Nivaar worked solution (AI-drafted; not reviewed by a licensed engineer)
EGBC National Exam — Mining and Mineral Processing Engineering, 09-MMP-A4 Mine Valuation and Mineral Resource Estimation, 2017-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 1.1–1.6); candidates then select THREE of the five optional Questions 2–6 (20 marks each) to complete the paper.
Reference texts: Isaaks & Srivastava, An Introduction to Applied Geostatistics (variogram modelling, kriging estimators, volume–variance relations); Hustrulid, Kuchta & Martin, Open Pit Mine Planning and Design (mine valuation, NPV and cut-off grade methodology, mineable reserves); Gentry & O'Neil, Mine Investment Analysis (Canadian mining taxation, smelter/refining contract terms, net smelter return, transportation logistics); SME Mining Engineering Handbook, 3rd ed. (cost-estimating relationships, mineral exploration/evaluation stages, ore reserve classification); Evans, An Introduction to Ore Geology and Guilbert & Park, The Geology of Ore Deposits (ore deposit models); CIM Best Practice Guidelines and NI 43-101 (Canadian Securities Administrators).
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.
Given.
| Component | Cost-per-day formula ($, T in t/d) |
|---|---|
| Drilling | 2.85 T0.7 |
| Blasting | 4.76 T0.7 |
| Loading | 4.01 T0.7 |
| Haulage | 27.10 T0.6 |
| General | 9.98 T0.7 |
| Daily tonnage, T | 50,000 t/day (ore + waste) |
Find. Cost per day and per tonne, for each component and in total, at T=50,000 t/day.
Approach. Evaluate T0.7 and T0.6 once at T=50,000, apply each component's own K, sum for the daily total, then divide every daily figure by T to get the per-tonne figures.
| Component | $/day | $/tonne |
|---|---|---|
| Drilling | 5,547.84 | 0.1110 |
| Blasting | 9,265.86 | 0.1853 |
| Loading | 7,805.91 | 0.1561 |
| Haulage | 17,879.33 | 0.3576 |
| General | 19,427.17 | 0.3885 |
| Total | 59,926.11 | 1.1985 (≈$1.20) |
Comment on automating feasibility studies with these formulae (part b). Because every component here shares the SAME general power-law form and the per-tonne figures (Column 3 above) are exactly the answer to part (b)'s "what would you expect these costs per tonne to be" – Haulage and General dominate the per-tonne total (0.358 and 0.389 $/t respectively, together ≈62% of the $1.20/t total), with Loading, Blasting and Drilling contributing the remainder. This structure is attractive for AUTOMATING early-stage (scoping/PEA) cost estimation in a spreadsheet or software tool: given only a candidate daily tonnage T, every cost line is generated instantly with no detailed equipment-list engineering, allowing rapid screening of many scale/site scenarios and quick sensitivity analysis on throughput. However, this convenience comes with real limitations that a feasibility-level study cannot accept: the K, x coefficients are historical regression averages that embed an implicit "typical" strip ratio, haul distance, rock hardness and labour cost structure that may not match the specific project; the formulae give NO breakdown into fixed vs. variable cost (needed for break-even and downside-scenario analysis) or capital vs. operating cost; and because the coefficients are tied to a specific historical cost-index base year, they must be escalated correctly (a step easy to omit when the model is "automated" and treated as a black box). Automating with these formulae is therefore appropriate for rapid PRE-feasibility screening across many scenarios, but must be replaced by first-principles, site-specific estimating once a project is carried to bankable feasibility.