Data underlying the MSc thesis "Extraction Sequence Optimization for the Dredge Mining of Heavy Mineral Sands: A Meta-Heuristic Approach"
Wat erin zit
This dataset contains the implementation of a genetic algorithm in Python developed during the MSc thesis "Extraction Sequence Optimization for the Dredge Mining of Heavy Mineral Sands: A Meta-Heuristic Approach" (January 2026). The genetic algorithm optimizes the life of mine extraction sequence for dredge mined heavy mineral sands projects. This is done by dividing defined spatial extraction limits into mining periods. The optimization objective is the project value in the form of the NPV (net present value), a measure of value that takes into account the time value of money. The extraction sequence is subjected to the following constraints, sequences breaking these constraints are penalized: -) Spatially continuity: consecutive mining periods must be spatially adjacent -) Fixed offtake rate: fixed mass of delivered product per mining period -) Equipment capacity: maximum capacity per period of extraction equipment (dredger) and processing equipmentThe sequence is defined by a centroid for every mining period, with all blocks in the block model being associated with the closest period centroid. This way the extraction sequence takes the form of a voronoi diagram.
Bestanden en diensten
- DataCite REST API (JSON)api.datacite.orgOpenen