pulpo-dev 1.4.2

Pulpo package for optimization in LCI databases

Python-based User-defined Lifecycle Production Optimization

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📍 Overview

This is a python package for Life Cycle Optimization (LCO) based on life cycle inventories. pulpo is intended to serve as a platform for optimization tasks of varying complexity.

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

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✨ Capabilities

Applying optimization is recommended when the system of study has (1) many degrees of freedoms which would prompt the manual assessment of a manifold of scenarios, although only the "optimal" one is of interest and/or (2) any of the following capabilities makes sense within the goal and scope of the study:

• Specify technology and regional choices throughout the entire supply chain (i.e. fore- and background), such as choices for the production technology of electricity or origin of metal resources. Consistently accounting for changes in the background in "large scale" decisions can be significant.
• Specify constraints on any activity in the life cycle inventories, which can be interpreted as tangible limitations such as raw material availability, production capacity, or environmental regulations.
• Optimize for or constrain any impact category for which the characterization factors are available.
• Specify supply values instead of final demands, which can become relevant if only production values are available (e.g. here).

The following features are currently under development:

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

• [X] ℹ️ Development of a GUI for simple optimization tasks Link
• [X] ℹ️ Enable PULPO to work on both bw2 and bw25 projects
• [X] ℹ️ Thorough documentation hosted on flechtenberg.github.io/pulpo/

Feature requests are more than welcome!

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🔧 Installation

PULPO has been deployed to the pypi index. Depending on the version of brightway projects you want to work on, install either the bw2 or bw25 version via:

pip install pulpo-dev[bw2]

or

pip install pulpo-dev[bw25]

🤖 Running pulpo

Use this link to start a session and test PULPO

Find further example notebooks for a hydrogen case, an electricity case, and a plastic case here.

There is also a workshop repository (here), which has been created for the Brightcon 2024 conference. It contains several notebooks that guide you through the PULPO package and its functionalities, as well as an exercise.

🧪 Tests

Calling from the package folder:

python -m unittest discover -s tests

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What's new in 1.4.2?

• Enable the use of gurobi solver

What's new in 1.4.0?

• Enable the use of NEOS solver (commercial solvers without license)
• Enable Monte-Carlo sampling feature
• Retrieve uncertainty information to lci_data for future use

What's new in 1.3.0?

• Switch packaging logic from setup.py to pyproject.toml and align pypi with Github versioning number

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🤝 Contributing

Contributions are very welcome. If you would like to request a feature or report a bug please open an Issue. If you are confident in your coding skills don't hesitate to implement your suggestions and send a Pull Request.

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📄 License

This project is licensed under the ℹ️ BSD 3-Clause License. See the LICENSE file for additional info. Copyright (c) 2025, Fabian Lechtenberg. All rights reserved.

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👏 Acknowledgments

We would like to express our gratitude to the authors and contributors of the following main packages that PULPO is based on:

• pyomo
• brightway2

In addition, we acknowledge the pioneering ideas and contributions from the following works:

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

Follow-up work, incorporating features such as top-down matrix construction for the use of entire life cycle inventory databases and supply specification, was implemented in PULPO and 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

This article is to be cited / referred to if PULPO is used to derive results of a publication or project.

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Authors

• @flechtenberg
• @robyistrate
• @vtulus

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