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Revised 23 October 2026 · v1.0.0 · what changed

Code: github.com/herrrickshaw/saf-monitoring-system

Watch the walkthrough

Watch on YouTube: youtu.be/7bqmJY5fiH0

Why it is useful

• Who benefits: SAF plant analysts, airline sustainability teams and students who want a working example of how feedstock, batches, certificates, blending and credits link together.
• What it replaces: stitching spreadsheets across feedstock logs, batch records and credit ledgers. The data model and the calculations are already laid out and tested.
• Evidence from the run: 39 tests passed in 3.95 seconds, the seeded database produced a dashboard summary, and one of three airports was flagged as below its mandate share.
• It keeps the regulatory numbers in editable files, and says in the README to confirm them against the current rules before real use.

What it is for

The backend (FastAPI with SQLite) models feedstock deliveries, production batches, ASTM D7566 pathways, SAF certificates, airline offtakes and deliveries, a CORSIA-style credit ledger and ReFuelEU blending records. A React frontend shows dashboards for each. On first start it seeds about twelve months of mock data.

This is a monitoring and learning tool. Not legal advice. Regulatory values in it are editable defaults, and the README tells you to confirm them against the official texts. All figures in this post are what the repository itself outputs on its mock data. The repository is archived, so it will not be updated when rules change.

Who it is for

• Students and analysts learning how SAF compliance data fits together
• Teams prototyping an internal SAF tracking tool
• Non-programmers who want an AI assistant to explain each calculation

What you need

Get the code and an AI assistant

You do not have to be a programmer. Get the repository, give it to an AI assistant, and ask in plain language. Any of these routes works:

git clone https://github.com/herrrickshaw/saf-monitoring-system.git
cd saf-monitoring-system
claude      # or: gemini

Install

1. Get the code (the repository is archived, so you can clone it but not push)

   git clone https://github.com/herrrickshaw/saf-monitoring-system.git
   cd saf-monitoring-system/backend

2. Create an environment and install

   python3 -m venv .venv && source .venv/bin/activate
   pip install -r requirements.txt

Quick start: commands and what you should see

Run the tests

pytest -q

39 passed.

Start the API (it seeds mock data on first run), then open the Swagger page

uvicorn app.main:app --port 8000

Interactive docs at http://localhost:8000/docs. The repo's run_app.sh also starts the React frontend on port 5173 (not run here).

Ask for the dashboard and the blending records

curl localhost:8000/api/dashboard/summary
curl localhost:8000/api/compliance/blending

JSON with totals and per-airport blending records.

Sample outputs

Real output from a run on the date shown in the sources line; trimmed to fit.

Worked CORSIA calculation (the repo's own function and default table)

corsia.default_core_lca_value, lcef, reduction_pct, tco2e_saved
core LCA (HEFA-SPK, used cooking oil) : 13.9 gCO2e/MJ
L_CEF = 13.9 + 0 - 0                  : 13.9 gCO2e/MJ
reduction vs 89 gCO2e/MJ baseline     : 84.38 %   eligible (>= 10 %): True
1,000,000 L delivered                 : 2478.3 tCO2e saved

The value 13.9 is the repository's default for this feedstock and pathway. I did not check it against the ICAO document.

Dashboard summary from the seeded mock database

GET /api/dashboard/summary
{"total_saf_liters": 42442105.0,
 "total_tco2e_credited": 63151.264,
 "active_contracts": 3,
 "current_year_blending_pct": 2.28,
 "current_year_mandate_pct": 2.0}

Mock data generated by the seeder, not a real plant.

Blending compliance flags (mock data)

GET /api/compliance/blending
EDDF 2026  SAF 17,000,000 L of 500,000,000 L  actual 3.4 %    mandate 2.0 %  compliant True
EGLL 2026  SAF  4,000,000 L of 420,000,000 L  actual 0.952 %  mandate 2.0 %  compliant False
LFPG 2026  SAF  9,500,000 L of 380,000,000 L  actual 2.5 %    mandate 2.0 %  compliant True

One airport is deliberately seeded below the mandate to demonstrate the flag, as the README says.

ASTM conformance rule and the repo's mandate schedule

astm_pathways.is_conformant, mandate_schedule
is_conformant(SIP max 10 %, blend 15 %, lab ref)  -> False
is_conformant(HEFA max 50 %, blend 30 %, lab ref) -> True
mandate_pct_for_year in the repo: 2025: 2.0   2030: 6.0   2035: 20.0   2050: 70.0   (percent, repo defaults)

Conformance means the blend is within the pathway maximum and a lab reference is recorded; the lab test itself is outside the system.

Ways to use it

Sample prompts for your AI assistant

Copy, paste and edit the parts in [brackets].

• Claude: Read backend/app/calculations/corsia.py and ghg.py and explain in plain language why the credit ledger uses a different baseline from the blending mandate.
• Gemini: Run the tests and summarise which regulatory frameworks each test file covers.
• ChatGPT: I pasted /api/compliance/blending. Which airport is below its mandate and by how much?
• GitHub (Copilot Chat): Add a new feedstock to DEFAULT_CORE_LCA_VALUES with a clear source comment and a test.
• Claude: List every regulatory constant in this repository and tell me which official document I should check each one against.

Limits and honest caveats

Sources: Cloned github.com/herrrickshaw/saf-monitoring-system (archived) into a clean folder, ran pytest, and called its calculation functions and API through the FastAPI test client against a temporary seeded database, in a throwaway virtualenv with Python 3.14 on 23 October 2026. All regulatory figures shown are the repository's own defaults, not checked against official sources. The repository was not modified.

About this article: Researched, written and edited by Umashankar Triplicane Dwarakanathan, with AI research assistance; every figure is meant to trace to the primary source cited. See the Editorial Policy for how sourcing, AI use and corrections work.