BLEECAM™

Benchmarking Life Cycle Environmental, Economic, and Social Metrics for Critical and Advanced Minerals and Materials

BLEECAM™ is a multi-objective optimization framework that benchmarks global critical-mineral supply chains across economic, environmental, and social dimensions — it evaluates and optimizes whole supply-chain scenarios, not single products.

📖 Documentation: https://bleecam.readthedocs.io

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Overview

BLEECAM is an optimization-driven benchmarking and evaluation framework for global critical-mineral and material (CMM) supply chains, developed at the National Laboratory of the Rockies (NLR) with funding from the U.S. Department of Energy's Advanced Materials and Manufacturing Technologies Office (DOE AMMTO).

Given a scenario — baseline, disruption, or policy — BLEECAM solves for the supply-chain configuration that best meets demand under real-world constraints (capacity, trade topology, tariffs, yields), and reports integrated economic (TEA/LCC), environmental (LCA-derived), and social (S-LCA) metrics for it, including the multi-objective trade-off frontier. It benchmarks technologies, routes, and countries against each other and against state-of-the-art references — giving DOE/AMMTO a prescriptive, quantitative basis for RD&D investment and supply-chain-resilience decisions.

Why "optimization-driven benchmarking"

Most benchmarking is retrospective: you hand it fixed configurations and it grades them. BLEECAM benchmarks prescriptively — its engine is optimization, so it finds the best-achievable configuration under constraints and quantifies the trade-offs between objectives (for example, what buying down emissions costs in dollars, and vice versa). The optimization is the differentiator; the benchmarking is what it delivers.

What BLEECAM is — and is not

BLEECAM is LCA-integrating, not an LCA tool. It consumes life-cycle impact factors as one of its three metric dimensions; it performs no inventory modeling and no impact characterization. Those factors come from a dedicated LCA engine — the first-party LiAISON engine, or any tool that emits to BLEECAM's documented emission-factor contract (e.g., openLCA). The goal, from the original proposal, was never to build another LCA tool, but to apply LCA to critical minerals and establish defensible methods for LCA of CMMs — alongside techno-economic and supply-chain analysis — to inform DOE/AMMTO.

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Key capabilities

• Multi-objective optimization across economic, environmental (any of ~25 ReCiPe / TRACI impact categories, GWP by default), and social (S-LCA: child labor, forced labor, injury) dimensions — single-objective corners and full multi-objective (AUGMECON2) Pareto trade-off frontiers.
• Two demonstrated case studies on structurally different supply chains: rare-earth permanent magnets (Nd / Dy) and gallium semiconductor wafers (GaN / GaAs).
• Engine-agnostic LCA integration via a documented emission-factor contract, with a reproducible first-party LiAISON adapter that carries full provenance.
• Scenario analysis — baseline, supply-shock (e.g., export restrictions), and policy scenarios.
• Reproducible by design — golden-output regression tests for both cases; results traceable to versioned inputs.

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Current scope

Two critical-material supply chains essential to U.S. national security and clean-energy sectors:

Case	Materials	Application
Rare earths	Neodymium (Nd), Dysprosium (Dy)	NdFeB permanent magnets
Gallium	Gallium (Ga)	GaN / GaAs semiconductor wafers

Each is modeled end to end — primary acquisition, refining / separation, metal and specialty-alloy processing, subcomponent manufacturing, use phase, and end-of-life recycling.

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Add a new critical mineral

BLEECAM is material-agnostic — the two cases above are examples, not limits. To benchmark a different mineral (copper, nickel, lithium, cobalt, …), you describe its supply chain in a declarative case file and supply its data; you inherit the whole engine — the optimization, the no-code criticality constraint library, multi-objective analysis, and LCA integration.

See ADDING_A_MATERIAL.md — evaluate a different critical mineral, as simple as 1‑2‑3.

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Architecture

src/bleecam/
  core/     # shared engine: solver selection, data-contract schema,
            # objective primitives, multi-objective methods, LCA import
  cases/
    rare_earth/   # REE supply-chain model + AUGMECON Pareto
    gallium/      # Gallium supply-chain model + AUGMECON Pareto
  shared/   # generic reporting / visualization helpers

BLEECAM uses a ProMMiS-style src/ layout on a Pyomo optimization core. Cases supply data and case-specific structure; the shared core/ provides the optimization, objective, and LCA-contract machinery.

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Installation

Two steps: install the package, then make sure a math-programming solver is available (BLEECAM cannot solve a model without one).

1. Install the package (Python ≥ 3.10):

python -m pip install bleecam==0.1.0b1        # current beta (pinned)
# or the latest release:  python -m pip install bleecam
# extras:  python -m pip install "bleecam[viz,test]"   # plotting + tests

This includes the bundled HiGHS solver (highspy), so the linear cases (e.g. gallium) work out of the box.

2. Install a solver. HiGHS (above) covers the linear/LP cases. The nonlinear rare-earth case and Pareto runs require ipopt, installed separately:

conda install -c conda-forge ipopt

Any Pyomo-visible solver on your PATH (GLPK, CBC, …) is also auto-selected. For development, install editable from a clone instead: git clone … && python -m pip install -e ".[viz,test]". Full details in the installation guide.

Quickstart

# Rare-earth and gallium cost-optimized baselines
bleecam-ree  --data src/bleecam/cases/rare_earth/data
bleecam-ga   --input-dir src/bleecam/cases/gallium/data/gallium --solver auto

# Gallium 3-objective Pareto (cost x GWP x child labor), needs ipopt + pyaugmecon
bleecam-ga-pareto --data src/bleecam/cases/gallium/data/gallium --out outputs/gallium_pareto --grid 50

# View the criticality-constraint library and run a scenario — no code required
bleecam-lib list
bleecam-run scenarios/gallium_china_shutdown.yaml

Run the regression suite with pytest.

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Methods & provenance

• Positioning — docs/positioning.md
• Multi-objective methods (the degeneracy argument; lexicographic vs. epsilon-constraint / AUGMECON) — docs/methods_multiobjective.md
• Roadmap and core-refactor scoping — docs/

Data status. Environmental and social factors are supplied through the emission-factor contract. The bundled LCIA / S-LCA data is under active refinement and should be treated as provisional pending finalization; the optimization machinery is reproducible independent of the specific factor values.

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Research questions (DOE AMMTO)

1. Technology benchmarking — what are the environmental, economic, and social metrics of a new technology relative to established benchmarks?
2. Material onshoring — how much material flow is onshored into the U.S. over time through new technology investments (mining, refining, circularity)?
3. Supply-chain disruption recovery — under what conditions, at what cost, and with what configuration can U.S. demand be met through a disruption?

These evolve with DOE AMMTO feedback.

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Contributors

Sherif Khalifa, Tapajyoti Ghosh, Julien Walzberg, Luca Brown

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Citation

BLEECAM is registered in DOE CODE with a citable DOI: 10.11578/dc.20260831.2 (Code ID 191130). If you use BLEECAM in your work, please cite it. A machine-readable CITATION.cff is included.

Khalifa Hassan, Sherif, Ghosh, Tapajyoti, and Walzberg, Julien. BLEECAM™ (Benchmarking Life Cycle Environmental, Economic, and Social Metrics for Critical and Advanced Minerals and Materials) [SWR-25-125]. Computer Software. USDOE Office of Critical Minerals and Energy Innovation (CMEI); Advanced Materials and Manufacturing Technologies Office (AMMTO). 29 Apr. 2026. https://doi.org/10.11578/dc.20260831.2

@misc{bleecam_2026,
  title        = {{BLEECAM™ (Benchmarking Life Cycle Environmental, Economic, and Social Metrics for Critical and Advanced Minerals and Materials) [SWR-25-125]}},
  author       = {Khalifa Hassan, Sherif and Ghosh, Tapajyoti and Walzberg, Julien},
  doi          = {10.11578/dc.20260831.2},
  url          = {https://github.com/NatLabRockies/bleecam},
  howpublished = {Computer software, DOE CODE (Code ID 191130)},
  year         = {2026},
  month        = apr,
  note         = {USDOE Office of Critical Minerals and Energy Innovation (CMEI); AMMTO},
  license      = {AGPL-3.0-or-later}
}

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License

BLEECAM is distributed under the GNU Affero General Public License v3.0 or later (AGPL-3.0-or-later) — see LICENSE.

Copyright © 2026 Alliance for Energy Innovation, LLC. All rights reserved. Developed at the National Laboratory of the Rockies (NLR) with funding from DOE AMMTO. NLR Software Record: SWR 25-125.

Contributing

Contributions are welcome. By opening a pull request you license your contribution under AGPL-3.0-or-later (GitHub inbound = outbound). We also ask contributors to submit a short Contributor License Agreement — see CONTRIBUTING.md and CLA.txt.

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Trademark

BLEECAM™ is a trademark of the Alliance for Energy Innovation, LLC / National Laboratory of the Rockies. Use the ™ symbol on the first written reference to the tool.

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Acknowledgments

This work is led by the National Laboratory of the Rockies (NLR) with support from DOE AMMTO. Data and insights from the U.S. Geological Survey (USGS) and DOE stakeholders inform BLEECAM's development.