JNTUH B.Tech IV MINE PLANNING Previous Question Paper (July/August - 2022 - R16)

Planning a 10 MTPA open cast coal mine requires a systematic approach integrating geological, technical, economic, and environmental factors. Assuming a large-scale deposit with a stripping ratio of 1:4 and a seam thickness of 15 meters, the following system approach is adopted:

1. Data Acquisition and Geological Modeling: Establish a comprehensive database using borehole logging and geostatistical modeling to define the coal seam geometry, quality, and overburden characteristics.
2. Mine Design and Method Selection: Given the 10 MTPA capacity, a shovel-dumper mining system is selected for high productivity. The mine layout is designed based on the pit shell optimization using the Lerchs-Grossmann algorithm to maximize the net present value.
3. Production Scheduling: Develop a long-term production schedule that balances the stripping ratio over the life of the mine, ensuring consistent coal supply to the washery or power plant.
4. Equipment Selection and Sizing: Calculate the fleet requirements based on cycle times and availability. For 10 MTPA, high-capacity electric rope shovels (20-25 m³) and 150-tonne dumpers are prioritized for optimal cost-per-tonne.
5. Infrastructure and Logistics: Plan the internal haul road network, coal handling plants (CHP), and rapid loading systems. Incorporate a conveyor system for primary coal transport to reduce truck haulage costs.
6. Environmental and Social Management: Design an integrated waste dump management plan, including progressive reclamation and reforestation. Implement water management systems for drainage and dust suppression.
7. Economic Evaluation: Perform a detailed financial analysis, including capital expenditure (CAPEX) and operating expenditure (OPEX) estimation, sensitivity analysis against coal price fluctuations, and internal rate of return (IRR) calculation to ensure project viability.

Mine modelling is the process of creating a digital representation of a mineral deposit and its surrounding geological environment. It integrates geological data, structural controls, and assay values to estimate the spatial distribution of ore grades. The primary techniques are categorized based on how they handle spatial continuity and data interpolation.

The main techniques include:

• Inverse Distance Weighting (IDW): A deterministic method where the grade of an unsampled point is calculated as a weighted average of surrounding samples. The weights are inversely proportional to the distance raised to a power (p). It is simple and computationally efficient but fails to account for spatial correlation or clustering of data.
• Kriging (Geostatistical Method): A sophisticated technique that uses the variogram to model spatial correlation. It provides the Best Linear Unbiased Estimator (BLUE) and quantifies the uncertainty (kriging variance) of the estimate. Common types include Ordinary Kriging, Simple Kriging, and Indicator Kriging.
• Nearest Neighbor (Polygon Method): Assigns the grade of the closest sample to a block or point. It is useful for preliminary assessments but creates a 'blocky' representation that does not reflect natural grade transitions.
• Conditional Simulation: Unlike interpolation methods that smooth data, simulation generates multiple equally probable realizations of the deposit. This is used to assess geological risk and grade uncertainty.

Example: In a large porphyry copper deposit, geologists first define the Wireframe Model (the geological boundary of the ore body) using drill hole logs. Within this volume, a Block Model is created, consisting of a grid of 3D cells (e.g., 10m x 10m x 10m). Using Ordinary Kriging, the copper grade for each block is estimated based on the assay data from nearby drill holes. The resulting model allows for accurate calculation of total tonnage and metal content, which is essential for mine design and economic feasibility studies.