Responsible Mining Practices and Resource Optimization

Module: Sustainable Tech | Difficulty: Premium

Ore Grade Estimation

Mining Waste Ratio

Comparison

Python Implementation

import numpy as np
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import Matern

class SustainableMiningOptimizer:
    def __init__(self):
        self.model = GaussianProcessRegressor(kernel=Matern(nu=2.5))

    def estimate_ore_grade(self, drill_holes, assay_data, target_grid):
        self.model.fit(drill_holes, assay_data)
        return self.model.predict(target_grid, return_std=True)

    def optimize_extraction(self, ore_blocks, costs, market_price):
        return sorted([{'block': b, 'profit': g * market_price - costs.get(b, 100)}
                       for b, g in ore_blocks.items() if g * market_price > costs.get(b, 100)],
                      key=lambda x: x['profit'], reverse=True)

Research Insight: ML-based ore body modeling improves grade prediction accuracy by 15-20%.