Screening Circular-Economy Reuse Pathways for Phosphate Mine Waste Rock with Explainable Machine Learning: A Proof of Concept from the Benguerir Mine, Morocco

Phosphate mining secures the world’s fertilizer supply, yet it leaves behind large volumes of mine waste rock (MWR), most of which is stockpiled as a liability rather than managed as a resource. These stockpiles occupy land, affect local water quality, and represent a secondary resource left unused, at a cost to the sustainability of the sector. The practical obstacle to reuse is rarely a shortage of applications; it is the slow, sample-by-sample expert assessment needed to decide which material qualifies for which pathway. This study tests whether that decision step can be automated with the characterization data mines already collect. Fifty-two waste rock samples from two drillholes at the Benguerir mine (Morocco) were characterized mineralogically (QEMSCAN), chemically (ICP-AES), and geotechnically, then used to train a two-stage machine learning pipeline: geological facies classification (four classes) followed by valorization pathway prediction (brick and ceramic manufacturing, aggregates and concrete, or phosphorus recovery). Each of these pathways substitutes secondary material for primary raw material extraction, which is where the sustainability gain lies. Under repeated stratified cross-validation with fold-level preprocessing, XGBoost outperformed four alternative algorithms, reaching weighted F1-scores of 0.893 ± 0.095 for facies and 0.957 ± 0.079 for valorization. However, the phosphorus-recovery class contained only two independent samples, so its class-specific metrics should be considered descriptive. SHAP analysis confirmed that the predictions rest on the evidence practitioners themselves use: lithology, mechanical strength (UCS, RQD), and carbonate-silica chemistry, consistent with geological and engineering knowledge. Feeding predicted facies into the valorization stage yielded no measurable gain in accuracy (ΔF1 = +0.005, p = 0.143); the value of the staging lies in its alignment with established geological workflows. Presented openly as a single-site proof of concept, the framework shows that routine characterization data can support consistent, auditable, and near-instant reuse screening, and it maps the path from this demonstration to multi-site operational deployment.