← Back to project catalogue
GP-GE-187C9E4GeologyReady

Mineralogy-Constrained Critical-Mineral Recovery Screening for Legacy Mine Waste

A completed geology and mineral-processing study screening legacy mine-waste classes for critical-mineral recovery while keeping mineral hosts, liberation, process compatibility, residue stability and evidence uncertainty visible.

Mineralogy-Constrained Critical-Mineral Recovery Screening for Legacy Mine Waste project visual
GP-GE-187C9E4 · Geology
  • Python 3.12
  • NumPy
  • pandas
  • Matplotlib

Software compatibility

Python 3.12.14 or later on macOS, Linux, and Windows

The released results use the frozen 30 August 2026 evidence catalogue, pinned Python packages, 20,000 uncertainty draws per material, and random seed 20260830. Site data or changed evidence positions require a fresh validation run.

Project definition

Problem statement

Legacy mine waste can contain critical minerals that were not targeted or recoverable when the material was produced.

The geology problem is to distinguish elemental occurrence from a plausible secondary resource by connecting grade to mineral host, association, liberation, process response and environmental liability.

Project objectives

  • Compare ten legacy mine-waste archetypes across eight critical-mineral target groups.
  • Apply hard mineral-host, liberation and residue-stability gates before ranking.
  • Compare balanced, mineralogy-first, environmental and near-term deployment scenarios.
  • Map thirteen validation criteria across eleven investigation stages.
  • Quantify evidence-position uncertainty with 20,000 fixed-seed draws per material.
  • Retain complete results, figures, tests, references and editable documentation.

Project structure

Project components

01

Waste-material register

Compares sulphide tailings, porphyry tailings, bauxite residue, coal-derived waste, phosphogypsum, historic tin-tungsten tailings, uranium tailings, laterite residue and lithium tailings.

02

Mineral-host model

Separates bulk grade from host certainty, grain size, association, liberation and deportment.

03

Recovery screening

Maps eight target groups to plausible materials under physical, flotation and hydrometallurgical process boundaries.

04

Environmental model

Retains water, energy, acid drainage, contaminant mobility and post-treatment residue stability.

05

Workflow FMEA

Ranks 143 stage-criterion combinations from historical records and sampling through characterization, testwork and decision.

06

Verification pipeline

Regenerates the study, tests the model, validates the documents and reproduces the analysis in Docker.

Methodology

Project workflow

  1. 01
    Define the waste feature

    Establish ownership, access, history, geometry, material classes and environmental setting.

  2. 02
    Sample representatively

    Stratify by unit, depth, grain size, oxidation and production period with field quality control.

  3. 03
    Establish mineral hosts

    Combine chemistry, mineralogy, particle size, association, liberation and deportment.

  4. 04
    Test complete routes

    Measure products, recoveries, impurities, water, reagents, energy and every residual stream.

  5. 05
    Review uncertainty

    Inspect gates, scenario reversals, FMEA priorities, intervals and sensitivity before advancing.

Demonstration scenario

The retained analysis shows that historic tin-tungsten tailings can rank strongly when heavy-mineral liberation and mature physical separation align. Sulphide and reactive residues remain conditional on acid-drainage control and post-treatment residue evidence. Large tonnage or bulk grade cannot override a failed mineral-host gate.

Engineering

Tools and method

Tools
The project uses Python 3.12, NumPy, pandas, Matplotlib for subject analysis, simulation, and results.
Declared inputs
Python data structures retain every normalized evidence position, target-host link and decision boundary.
Deterministic analysis
NumPy and pandas generate all 1,280 material, target, context and evidence cases.
Risk analysis
Validation criteria, requirements crosswalk and FMEA expose sampling, mineralogical, process and residue risks.
Uncertainty
Fixed-seed Monte Carlo and one-factor sensitivity test stability of the declared evidence positions.
Evidence
CSV, JSON, PNG, PDF and Word files retain the complete analysis, source catalogue and report.
Release verification
Tests, linting, current package checks, vulnerability audit, document QA, repository validation and Docker verify the handover.

Testing

Evaluation

Evaluation measures

  • Screening score and hard-gate result by material and target
  • Mineral-host certainty, liberation potential and process compatibility
  • Water, energy, residue-stability and acid-drainage positions
  • Severity-weighted validation gaps and workflow FMEA priorities
  • Requirements crosswalk and scenario reversals
  • Monte Carlo intervals and evidence-position sensitivity

Project boundaries

  • All numerical inputs are literature-informed evidence positions rather than site measurements.
  • The study does not estimate a mineral resource or reserve and does not guarantee recovery.
  • It does not select a final flowsheet, establish product quality or provide a feasibility result.
  • It does not approve disturbance, remediation, closure, environmental acceptability or investment.
  • A real project requires representative site sampling, qualified mineralogy and processing testwork, complete residue assessment and current legal review.

Included

  1. 01Complete Python source and declared study configuration
  2. 021,280 material, target, liability-context and evidence cases
  3. 03Ten waste archetypes and eight critical-mineral target groups
  4. 04Thirteen validation criteria and a 143-cell workflow FMEA
  5. 05Twelve generated figures and one attributed public-domain literature figure
  6. 0677-page project report in PDF and editable Word formats
  7. 0715-page project and defence guide in PDF and editable Word formats
  8. 08Fifty annotated references with evidence boundaries and source matrix
  9. 09Automated tests, repository validation and Docker reproduction

Project record

No information is collected on this page.

Permanent project ID
GP-GE-187C9E4
Catalogued
21 Aug 2026
Completed
30 Aug 2026
Verified
30 Aug 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.