📍 Overview

PULPO is a Python package for Life Cycle Optimization (LCO) based on life cycle inventories. It is designed to serve as a platform for optimization tasks of varying complexity.

The package builds on top of the Brightway LCA framework and the Pyomo optimization modeling framework.

✨ Capabilities

Applying optimization is recommended when the system of study has (1) many degrees of freedom that would otherwise prompt the manual assessment of a large number of scenarios, or (2) any of the following capabilities is relevant to the goal and scope of the study:

• Specify technology and regional choices throughout the entire supply chain (fore- and background), such as the production technology of electricity or the origin of metal resources. Consistently accounting for background changes in large-scale decisions can be significant.
• Specify constraints on any activity in the life cycle inventories, interpreted as tangible limitations such as raw material availability, production capacity, or environmental regulations.
• Optimize for or constrain any impact category for which characterization factors are available.
• Specify supply values instead of final demands, which is relevant when only production volumes are known (e.g. here).
• Optimize under uncertainty via a dedicated pipeline: import and filter uncertain LCI parameters, apply uncertainty strategies, run Global Sensitivity Analysis (Sobol), perform Monte Carlo sampling, or solve Chance-Constrained programs to obtain Pareto-optimal solutions at user-defined probability levels.

Features recently completed:

• ℹ️ Optimization under uncertainty [chance-constraints, Monte Carlo, global sensitivity analysis]
• ℹ️ Development of a GUI for simple optimization tasks Link
• ℹ️ Enable PULPO to work on both bw2 and bw25 projects
• ℹ️ Thorough documentation hosted on flechtenberg.github.io/pulpo/

Features currently under development:

• ℹ️ Multi-objective optimization [bi-objective epsilon constrained, goal programming ...]
• ℹ️ Integration of economic and social indicators in the optimization problem formulation

Feature requests are more than welcome!

🔧 Installation

PULPO is available on PyPI. Depending on the version of Brightway you want to work with, install either the bw2 or bw25 variant:

pip install "pulpo-dev[bw2]"

or

pip install "pulpo-dev[bw25]"

🤖 Running PULPO

Use this link to start a cloud session and test PULPO right away:

The main reference is the PULPO showcase notebook, which revolves around methanol production and covers both the core optimization features (Sections 1–9) and the full workflow of the pulpo_unc module (Section 10): uncertainty data import and filtering, gap-filling strategies, Monte Carlo from prepared distributions, Chance-Constrained optimization, and Global Sensitivity Analysis.

Additional example notebooks are available for a hydrogen case, an electricity case, and a plastic case.

There is also a workshop repository (here) created for the Brightcon 2024 conference, with guided notebooks and exercises.

🧪 Tests

Run from the package folder:

python -m unittest discover -s tests

What's new in 1.6.1?

• Windows solve-hang fix — Unspecified limits are now truly infinite instead of ±1e20/±1e24, removing HiGHS bound warnings that deadlocked pyomo ≥ 6.6's output capture during model construction.
• Python 3.13 & numpy 2 — numpy 2 is unpinned (the <2 cap remains only in the legacy bw2 extra) and pyomo is relaxed to >=6.8,<7.
• From 1.6.0: time-dependent optimization — The pulpo.pulpo_time module (PulpoOptimizerTime) adds a time-indexed formulation with inter-timestep storage / carry-over, alongside several-times-faster model instantiation.

See the changelog for the full details and earlier releases.

🤝 Contributing

Contributions are very welcome. To request a feature or report a bug, please open an Issue. If you are confident in your coding skills, feel free to implement your suggestions and send a Pull Request.

📄 License

This project is licensed under the ℹ️ BSD 3-Clause License. See the LICENSE file for additional info.
Copyright (c) 2026, Fabian Lechtenberg. All rights reserved.

👏 Acknowledgments

We would like to express our gratitude to the authors and contributors of the following packages that PULPO builds upon:

• pyomo
• brightway2

We also acknowledge the pioneering ideas and contributions from the following works:

• Computational Structure of LCA
• Technology Choice Model
• Modular LCA

The development of PULPO culminated in the following publication, which details the approach and outlines its implementation:

Fabian Lechtenberg, Robert Istrate, Victor Tulus, Antonio Espuña, Moisès Graells, and Gonzalo Guillén‐Gosálbez.
“PULPO: A Framework for Efficient Integration of Life Cycle Inventory Models into Life Cycle Product Optimization.”
Journal of Industrial Ecology, October 10, 2024.
https://doi.org/10.1111/jiec.13561

Please cite this article if PULPO is used to produce results for a publication or project.

Authors

• @flechtenberg
• @robyistrate
• @vtulus
• Bartolomeus Haeussling Loewgren

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