AI for Mine Planning & Production Optimization

Block Model Optimization

Every open pit mine plan starts with a block model and a question: what is the optimal pit shell? The Lerchs-Grossmann algorithm and its floating cone variants have solved this for decades — given block values (grade × recovery × price − mining cost − processing cost), find the ultimate pit limit that maximizes undiscounted profit.

AI enters when the problem gets more realistic. The ultimate pit is a theoretical endpoint. What matters operationally is the sequence of nested pit shells (pushbacks) that maximizes NPV over a 15-30 year mine life, subject to:

This is a combinatorial optimization problem that grows exponentially with the number of blocks and time periods. Classical mixed-integer programming (MIP) solves small cases but hits computational limits on real block models (500K-5M blocks).

AI-Driven Approaches

The practical hybrid: use a GNN to learn block value dependencies and reduce the problem dimensionality, then solve the reduced MIP. This has shown 95-98% of optimal NPV at 10-50× faster solve times on real-world block models.

Open data/block-model.csv in the code panel. Each row is a mining block with coordinates (easting, northing, elevation), tonnage, grades (Fe, SiO2, Al2O3), rock type, and density. This is the raw input for pit optimization.

Production Scheduling Under Constraints

Monsoon Impact

Indian mining operations face a constraint that most global mine planning software ignores: the monsoon. In Karnataka, Goa, Odisha, and Jharkhand, mining effectively shuts down for 3-4 months (June-September). This is not a gradual slowdown — it is a hard stop mandated by environmental clearance conditions and practical necessity (waterlogged pits, impassable haul roads, slope stability risk).

A production schedule that assumes 12-month operations will fail. AI-based schedulers must encode monsoon as a hard constraint:

For each year y, for each month m:
  if m in [June, July, August, September]:
    production_tonnes[y][m] = 0  (or reduced_capacity for covered operations)
    waste_stripping[y][m] = 0
  Constraint: annual_target must be met in 8 operational months

This concentrates production into 8 months, requiring 50% higher monthly throughput than a 12-month schedule — which cascades into equipment sizing, workforce planning, and processing plant capacity.

Grade Blending

Open data/production-schedule.json — it contains a multi-year schedule with monthly targets, source benches, and grade specifications.

Processing plants need consistent feed. An iron ore beneficiation plant designed for 62% Fe feed cannot handle 55% Fe ore without losing recovery, or 66% Fe ore without overwhelming the concentrate dewatering circuit. The blending problem:

Minimize: deviation from target feed grade
Subject to:
  - Total tonnes from all sources = plant capacity
  - Grade_min ≤ weighted_average_grade ≤ Grade_max
  - Each source bench has limited exposed inventory
  - Haul distances vary by source (affects cost and fleet requirement)

AI-based blending optimizers solve this in real-time, adjusting shovel-truck allocation as actual grades deviate from the block model estimate. At Coal India's subsidiaries, real-time blending has reduced coal quality variance by 30-40%, directly improving power plant efficiency.

Equipment Fleet Optimization

Open data/equipment-fleet.json — it contains fleet composition, maintenance schedules, fuel consumption curves, and availability data.

Dispatch Optimization

Shovel-truck dispatch is the minute-by-minute decision engine of an open pit mine. Classical dispatch sends the next available truck to the best shovel based on a priority rule (shortest queue, highest priority material). AI dispatch considers the full system state:

Reinforcement learning dispatch trains an agent in a simulation of the mine's truck-shovel system. The agent learns policies that outperform heuristic dispatch by 8-15% in tonnes moved per shift. The key insight: RL discovers non-obvious strategies like intentionally idling a shovel to prevent downstream bottlenecks, or routing trucks on longer paths to avoid congestion at a junction.

Fuel Efficiency

Haul trucks are the largest single operating cost in open pit mining — fuel alone can be 25-35% of total mining cost. AI-based fuel optimization targets:

At NMDC's Bacheli complex in Chhattisgarh, implementing AI-based dispatch and fuel optimization on a fleet of 35 Tata Hitachi EH1700 trucks and 8 Komatsu PC2000 shovels improved fleet productivity by 12% and reduced diesel consumption by 9% — a saving of approximately ₹15 crore annually.

Haul Route Optimization

Haul roads evolve as the pit deepens. The in-pit road network must be re-designed every 6-12 months as new benches open and old ones are mined out. AI-based route optimization considers:

Graph-based optimization with learned edge costs (accounting for gradient, surface quality, and traffic density) produces road networks that reduce average cycle time by 10-20% compared to manually designed roads.

Indian Operational Context

Coal India Limited

Coal India produces over 700 million tonnes annually from 300+ mines. The scale makes even small efficiency gains enormously valuable:

NMDC Iron Ore

NMDC operates India's largest mechanized iron ore mines at Bailadila (Chhattisgarh) and Donimalai (Karnataka):

BEML and Tata Hitachi Fleet

Indian mines predominantly use BEML (for dump trucks and dozers) and Tata Hitachi (for excavators and shovels). Fleet-specific AI models must account for:

Key Takeaways