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17.2 Data Accuracy, Assumption Control & Go/No-Go Decisions

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PRC Mining Engineer Licensure Examination Philippines

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1 Chapter 1: Surface Mining Methods & Pit Optimization

1.1 Surface Mining Unit Operations & Method Selection 1.2 Bench Geometry, Slope Stability & Geotechnical Pit Design 1.3 Cut-Off Grade Optimization & Lerchs-Grossmann Pit Limits

2 Chapter 2: Underground Mining Methods & Strata Control

2.1 Underground Mining Method Classification & Selection 2.2 Rock Mechanics, Stress Analysis & Strata Control 2.3 Underground Ground Support Systems & Excavation Design

3 Chapter 3: Materials Handling, Equipment & Mine Economics

3.1 Drilling, Blasting & Explosives Engineering 3.2 Mine Loading, Haulage & Materials Handling Systems 3.3 Mine Capital (CAPEX), Operating Costs (OPEX) & Financial Evaluation

4 Chapter 4: Mine Safety, Ventilation & Philippine Mining Laws

4.1 Mine Ventilation Network Analysis & Environmental Quality 4.2 Philippine Mine Safety Rules, Health & Disaster Prevention 4.3 Philippine Mining Act of 1995 (RA 7942), DAO 2010-21 & Mining Ethics

5 Chapter 5: Physical Geology, Mineralogy & Philippine Deposit Types

5.1 Structural Geology, Tectonics & Ore Controls 5.2 Igneous, Metamorphic & Sedimentary Ore Mineralogy 5.3 Major Philippine Ore Deposit Models

6 Chapter 6: Mineral Exploration, Geophysics & Core Logging

6.1 Geochemical & Remote Sensing Exploration Techniques 6.2 Geophysical Exploration Methods in Mining Geology 6.3 Diamond Drilling, Core Logging & QA/QC Protocols

7 Chapter 7: Geostatistics, Resource Estimation & PMRC Code

7.1 Ore Body Block Modeling & Spatial Variography 7.2 Inverse Distance & Ordinary Kriging Grade Estimation 7.3 Philippine Mineral Reporting Code (PMRC 2020) & Resource Classification

8 Chapter 8: Comminution, Sizing & Physical Mineral Separation

8.1 Crushing, Grinding & Bond Work Index Calculations 8.2 Particle Screening, Hydrocyclone Classification & Sizing 8.3 Gravity Concentration & Magnetic/Electrostatic Separation

9 Chapter 9: Froth Flotation & Extractive Hydrometallurgy

9.1 Froth Flotation Chemistry, Reagents & Cell Kinetics 9.2 Leaching Technologies (Heap, Agitation & Tank) 9.3 Precious Metal Recovery: CIP, CIL, Merrill-Crowe & Electrowinning

10 Chapter 10: Process Plant Flowsheets & Environmental Tailings Management

10.1 Metallurgical Plant Flowsheets & Pulp Mass Balances 10.2 Tailings Storage Facility (TSF) Design & Dam Safety 10.3 Acid Mine Drainage (AMD) Mitigation & Environmental Protection (EPEP/FMR/DP)

11 Chapter 11: 2025 PRC Blueprint, Prospecting Tools & Exploration Decisions

11.1 Official 2025 TOS, Three-Paper Structure & Exam Strategy 11.2 Prospecting Instruments, Base Maps & Surface Geological Mapping 11.3 Integrating Direct and Indirect Evidence for Exploration Decisions

12 Chapter 12: Mine Infrastructure, Sustainable Development & Climate

12.1 Power, Water, Drainage & Support-Facility Selection 12.2 Sustainable Development, Progressive Rehabilitation & Community Impact 12.3 Mining, Climate, Reforestation, Coal & Energy-Transition Minerals

13 Chapter 13: Mine Valuation, Financial Analysis & Due Diligence

13.1 Time Value of Money, Future Value & Present Value 13.2 Financial Statements, Mine Economics & External Price Drivers 13.3 Exploration Provenance, Technical Review & Legal Due Diligence

14 Chapter 14: Mine Organization, Communication & Accountable Leadership

14.1 Line and Staff Functions in a Mining Organization 14.2 Communication With Superiors, Peers & Subordinates 14.3 Authority, Responsibility, Delegation & Safety Accountability

15 Chapter 15: Philippine Mining Law, Indigenous Rights & Professional Ethics

15.1 Legal Hierarchy, Mineral Tenure & Compliance Analysis 15.2 Constitutional Participation, IPRA, FPIC & Ancestral Domains 15.3 RA 4274, Professional Scope, Ethics & Document Integrity

16 Chapter 16: Applied Mathematics & Mine Surveying

16.1 Engineering Mathematics for Safe, Efficient Mine Decisions 16.2 Survey Control, Instruments, Traverses & Error Adjustment 16.3 Topographic Maps, Underground Set-Out & Survey Timeliness

17 Chapter 17: Feasibility Studies, Data Quality & Project Decisions

17.1 Scoping, Pre-Feasibility & Final Feasibility Study Maturity 17.2 Data Accuracy, Assumption Control & Go/No-Go Decisions 17.3 Multidisciplinary Integration, Interfaces & Independent Review

18 Chapter 18: Mining Computer Applications, Digital Control & Global Developments

18.1 Computer Functions, Office Applications & Data Governance 18.2 Geological Modeling, Mine Planning, Scheduling & Valuation Software 18.3 Real-Time Control, Critical Minerals & Regional Mining Developments

19 Chapter 19: Earth Systems, Geologic Time & Philippine Hazards

19.1 Solar-System Origin, Earth Interior & Atmospheric Layers 19.2 Rock Cycle, Landforms & the Geologic Time Scale 19.3 Philippine Tectonics, Geologic Hazards & Mitigation

20 Chapter 20: Metallurgy, Foundry Practice & Assaying

20.1 Extractive, Physical & Adaptive Metallurgy Principles 20.2 Foundry Materials, Casting, Solidification & Defects 20.3 Sampling, Fire Assay, Cupellation, Parting & Assay Calculations

21 Chapter 21: Solid–Liquid Separation, Environmental Law & Mine Ecology

21.1 Thickening, Filtration & Solid–Liquid Separation 21.2 Philippine Air, Water, Waste & Hazardous-Substance Controls 21.3 Mine Ecology, Closure, Tailings, Waste & Community Livelihood

17.2 Data Accuracy, Assumption Control & Go/No-Go Decisions

Key Takeaways Explain This Topic AI

Study data are validated for origin, completeness, representativeness, accuracy, precision, timeliness, units, and fitness for the decision—not merely presence in a database.

An assumption register identifies owner, basis, date, range, dependency, verification plan, and effect if wrong; high-value assumptions receive early testing.

Mass, water, energy, area, schedule, and cash-flow reconciliations expose inconsistent workstream inputs before they enter the investment case.

A decision gate uses pre-defined criteria for stop, recycle, hold, or advance and records dissent, conditions, residual risk, and accountable approvals.

Optimism bias is controlled through reference-class evidence, independent review, scenario analysis, and transparent treatment of exclusions and contingency.

Last updated: August 2026

Feasibility failures often begin at interfaces: geology uses one density, mining another recovery, processing another feed tonnage, and finance an outdated schedule. Each model can balance internally while the project model does not. Decision quality therefore depends on controlled data, assumptions, and reconciliation.

Data Fitness Tests

For each critical dataset, ask:

Origin: who measured or calculated it, by what method?

Completeness: what intervals, locations, periods, or fields are missing?

Representativeness: does it cover ore types, weathering, depth, season, and operating range?

Accuracy and bias: what reference, calibration, QA/QC, or reconciliation tests it?

Precision: how repeatable is it and what variability is natural?

Timeliness: does it reflect current design, price, law, and site condition?

Consistency: are units, coordinates, dry/wet basis, currency, and definitions aligned?

Fitness: is the quality sufficient for the consequence of this decision?

A preliminary datum can be acceptable for screening but unacceptable for equipment purchase or reserve conversion.

Assumption Register

An assumption is a placeholder treated as true for analysis. Record:

  • Field Example
  • Assumption Grid power available by commissioning
  • Basis/date Utility letter dated 15 June
  • Owner Infrastructure manager
  • Range 0–9 month delay
  • Dependencies Construction power, plant start, diesel backup
  • Validation Connection agreement and design
  • Consequence Capital, operating cost, emissions, schedule
  • Due gate Before investment approval

Do not hide an assumption in a spreadsheet cell. High-uncertainty, high-consequence assumptions should be tested first.

  • Reconciliation Checks
  • Mass

Ore mined plus opening stockpile must equal plant feed plus closing stockpile and accounted losses. Plant feed metal must reconcile to product metal, tailings metal, and inventory.

Water

Inflows minus outflows equal storage change for dry, average, wet, startup, upset, and closure cases.

Energy

Equipment load lists, operating hours, process models, and utility demand should agree, including starts and standby.

Schedule

Access, permits, engineering, procurement, construction, commissioning, development, and ore availability must follow logic. A date imposed without predecessor activities is not a schedule.

Economics

Revenue tonnes and grade must match the mine and plant schedule. Capital timing must match construction. Closure cost and working capital must enter cash flow at the correct time.

Risk, Opportunity and Bias

Uncertainty can be a symmetric range; risk is an uncertain event with consequence; bias is systematic optimism or pessimism. Contingency may address estimated scope uncertainty but cannot make an impossible schedule credible.

Use:

  • sensitivity analysis for value drivers;
  • scenarios for linked conditions;
  • probabilistic analysis where inputs and correlations are defensible;
  • quantitative schedule-risk analysis;
  • reference-class comparison with similar completed projects; and

independent technical and commercial review.

Beware double counting. If a cost distribution already includes quantified risk, adding the same risk again as flat contingency inflates cost. Conversely, excluding schedule delay from both risk and contingency understates exposure.

Decision Gate

Define criteria before reviewing the result:

  • advance: evidence and value support the next commitment, with accepted residual risk;
  • conditional advance: specified conditions must be completed before or during the next phase;
  • recycle: redesign or collect defined information and return to the gate;
  • hold: wait for an external event such as permit or market clarification; or

stop: fatal flaw or insufficient value.

Record decision, evidence, dissent, conditions, owners, dates, and what would trigger re-review. Senior approval does not turn missing data into accurate data.

Example

A base case has positive NPV, but 70% of revenue comes from an inferred geological zone excluded from the Mineral Reserve and scheduled in the first two years. This is not a small sensitivity. The mine and cash-flow premise are inconsistent with the reserve basis. Rebuild the schedule using supportable material, obtain more drilling, or stop; do not approve the base case because its spreadsheet NPV is positive.

The best exam answer identifies the data defect, traces its effect through schedule and value, and chooses a gate response proportionate to consequence.

Assumption Expiry

Set an expiry or revalidation trigger for volatile assumptions such as price, exchange rate, tariff, fuel, law, land access, and vendor delivery. A figure that was accurate at study start may be stale at approval. Re-baseline material changes and show their effect rather than silently overwriting the record.

Challenge the Base Case Before Approval

A gate team should reproduce the material balances and value drivers independently, then compare downside combinations rather than only one-at-a-time sensitivities. Test delay with inflation, lower recovery with higher processing cost, or wet-season productivity with water constraints where dependencies are credible. Conditions of approval need an owner, evidence, deadline and consequence; otherwise a conditional advance becomes an undocumented unconditional commitment.