lca-sachet-pet

Scripts README

Files created

• brightway_prepare.py: scans installed Brightway2 databases for matching background processes using openlca_premap.csv search strings and writes brightway_premap_results.csv with top matches.
• run_lcia.py: creates foreground processes from inventory_template.csv and runs deterministic LCIA for Sachet_fu and PET_fu. It expects brightway_premap_results.csv to be completed with chosen process IDs.
• montecarlo.py: samples input parameters within uncertainty bounds and writes sampled parameter tables for Monte Carlo runs.

Usage overview

1. Install python and dependencies:

   python -m pip install -r requirements.txt
   

2. Ensure Brightway2 background databases (ELCD/USLCI or similar) are installed and available to Brightway2.
3. Run brightway_prepare.py to search for candidate background processes and produce brightway_premap_results.csv.
4. Review brightway_premap_results.csv and edit the file to select the preferred (db_name, process name, process code) for each flow. Add an explicit nexus_process_id or ensure the top_match is the correct one.
5. Run run_lcia.py to build foreground processes and run the LCIA. If needed, adapt run_lcia.py to accept a custom inventory file for Monte Carlo.

Notes & limitations

• These scripts are designed to be a reproducible starting point. They do not fully automate every mapping decision — you must review and confirm background matches, and ensure appropriate LCIA methods are installed in Brightway2 (e.g., ReCiPe or ELCD methods).
• If you prefer a fully-GUI OpenLCA workflow instead of Brightway2, use openlca_import_template.csv and openlca_premap.csv plus openlca_mapping.md which contain guidance for import.