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Ecology","sub_category":"Life Cycle Assessment","monthly_downloads":70,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\u003cdiv align=\"center\"\u003e\n\n\u003cimg src=\"https://github.com/flechtenberg/flechtenberg_images/blob/main/Pulpo-Logo_INKSCAPE.png?raw=true\" width=\"300\" /\u003e\n\n\u003ch3\u003ePython-based User-defined Lifecycle Production Optimization\u003c/h3\u003e\n\n\u003c!-- Development Tools --\u003e\n[![Jupyter](https://img.shields.io/badge/Jupyter-F37626.svg?style=flat\u0026logo=Jupyter\u0026logoColor=white)](https://jupyter.org/)\n[![Python](https://img.shields.io/badge/Python-3776AB.svg?style=flat\u0026logo=Python\u0026logoColor=white)](https://www.python.org/)\n[![Markdown](https://img.shields.io/badge/Markdown-000000.svg?style=flat\u0026logo=Markdown\u0026logoColor=white)](https://www.markdownguide.org/)\n\n\u003c!-- Project Metadata --\u003e\n[![License](https://img.shields.io/github/license/flechtenberg/pulpo?style=flat\u0026color=5D6D7E)](https://github.com/flechtenberg/pulpo/blob/main/LICENSE)\n[![Last Commit](https://img.shields.io/github/last-commit/flechtenberg/pulpo?style=flat\u0026color=5D6D7E)](https://github.com/flechtenberg/pulpo/commits/main)\n[![Commit Activity](https://img.shields.io/github/commit-activity/m/flechtenberg/pulpo?style=flat\u0026color=5D6D7E)](https://github.com/flechtenberg/pulpo/pulse)\n\n\u003c!-- Additional --\u003e\n[![PyPI - Version](https://img.shields.io/pypi/v/pulpo-dev?color=%2300549f)](https://pypi.org/project/pulpo-dev/)\n[![GitHub Stars](https://img.shields.io/github/stars/flechtenberg/pulpo?style=flat\u0026color=FFD700)](https://github.com/flechtenberg/pulpo/stargazers)\n[![launch - renku](https://renkulab.io/renku-badge.svg)](https://renkulab.io/v2/projects/fabian/pulpo-test/sessions/01JRM54S4NKMS84Y6BAYT832WH/start)\n\n\u003c/div\u003e\n\n---\n\n## 📍 Overview\n\n**PULPO** is a Python package for **[Life Cycle Optimization (LCO)](https://onlinelibrary.wiley.com/doi/full/10.1111/jiec.13561)** based on life cycle inventories. It is designed to serve as a platform for optimization tasks of varying complexity.\n\nThe package builds on top of the **[Brightway LCA framework](https://docs.brightway.dev/en/latest)** and the **[Pyomo optimization modeling framework](https://www.pyomo.org/)**.\n\n---\n\n## ✨ Capabilities\n\nApplying 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:\n\n- **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](https://www.sciencedirect.com/science/article/pii/S2352550924002422).\n- **Specify constraints** on any activity in the life cycle inventories, interpreted as tangible limitations such as raw material availability, production capacity, or environmental regulations.\n- **Optimize for or constrain any impact category** for which characterization factors are available.\n- **Specify supply values** instead of final demands, which is relevant when only production volumes are known (e.g. [here](https://www.pnas.org/doi/10.1073/pnas.1821029116)).\n- **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.\n\n**Features recently completed:**\n\n\u003e - [X] `ℹ️  Optimization under uncertainty [chance-constraints, Monte Carlo, global sensitivity analysis]`\n\u003e - [X] `ℹ️  Development of a GUI for simple optimization tasks` [Link](https://github.com/flechtenberg/pulpo-gui)\n\u003e - [X] `ℹ️  Enable PULPO to work on both bw2 and bw25 projects`\n\u003e - [X] `ℹ️  Thorough documentation hosted on flechtenberg.github.io/pulpo/`\n\n**Features currently under development:**\n\n\u003e - [ ] `ℹ️  Multi-objective optimization [bi-objective epsilon constrained, goal programming ...]`\n\u003e - [ ] `ℹ️  Integration of economic and social indicators in the optimization problem formulation`\n\nFeature requests are more than welcome!\n\n---\n\n### 🔧 Installation\nPULPO is available on PyPI. Depending on the version of Brightway you want to work with, install either the `bw2` or `bw25` variant:\n\n```sh\npip install \"pulpo-dev[bw2]\"\n```\nor\n```sh\npip install \"pulpo-dev[bw25]\"\n```\n\n### 🤖 Running PULPO\n\nUse this link to start a cloud session and test PULPO right away:\n\n[![launch - renku](https://renkulab.io/renku-badge.svg)](https://renkulab.io/v2/projects/fabian/pulpo-test/sessions/01JRM54S4NKMS84Y6BAYT832WH/start)\n\nThe main reference is the [PULPO showcase notebook](https://github.com/flechtenberg/pulpo/blob/master/notebooks/pulpo_showcase.ipynb), 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.\n\nAdditional example notebooks are available for a [hydrogen case](https://github.com/flechtenberg/pulpo/blob/master/notebooks/showcases/hydrogen_showcase.ipynb), an [electricity case](https://github.com/flechtenberg/pulpo/blob/master/notebooks/showcases/electricity_showcase.ipynb), and a [plastic case](https://github.com/flechtenberg/pulpo/blob/master/notebooks/showcases/plastic_showcase.ipynb).\n\nThere is also a workshop repository ([here](https://github.com/flechtenberg/pulpo_workshop)) created for the Brightcon 2024 conference, with guided notebooks and exercises.\n\n### 🧪 Tests\n\nRun from the package folder:\n\n```sh\npython -m unittest discover -s tests\n```\n\n---\n## What's new in 1.6.1?\n- **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.\n- **Python 3.13 \u0026 numpy 2** — numpy 2 is unpinned (the `\u003c2` cap remains only in the legacy `bw2` extra) and pyomo is relaxed to `\u003e=6.8,\u003c7`.\n- **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.\n\nSee the [changelog](CHANGES.md) for the full details and earlier releases.\n\n---\n\n## 🤝 Contributing\nContributions are very welcome. To request a feature or report a bug, please [open an Issue](https://github.com/flechtenberg/pulpo/issues). If you are confident in your coding skills, feel free to implement your suggestions and [send a Pull Request](https://github.com/flechtenberg/pulpo/pulls).\n\n---\n\n## 📄 License\n\nThis project is licensed under the `ℹ️  BSD 3-Clause` License. See the [LICENSE](LICENSE) file for additional info.  \nCopyright (c) 2026, Fabian Lechtenberg. All rights reserved.\n\n\n---\n\n## 👏 Acknowledgments\n\nWe would like to express our gratitude to the authors and contributors of the following packages that **PULPO** builds upon:\n\n- [**pyomo**](https://github.com/Pyomo/pyomo)\n- [**brightway2**](https://github.com/brightway-lca/brightway2)\n\nWe also acknowledge the pioneering ideas and contributions from the following works:\n\n- **[Computational Structure of LCA](http://link.springer.com/10.1007/978-94-015-9900-9)**\n- **[Technology Choice Model](https://pubs.acs.org/doi/10.1021/acs.est.6b04270)**\n- **[Modular LCA](http://link.springer.com/10.1007/s11367-015-1015-3)**\n\nThe development of PULPO culminated in the following publication, which details the approach and outlines its implementation:\n\n\u003e **Fabian Lechtenberg, Robert Istrate, Victor Tulus, Antonio Espuña, Moisès Graells, and Gonzalo Guillén‐Gosálbez.**  \n\u003e “PULPO: A Framework for Efficient Integration of Life Cycle Inventory Models into Life Cycle Product Optimization.”  \n\u003e *Journal of Industrial Ecology*, October 10, 2024.  \n\u003e [https://doi.org/10.1111/jiec.13561](https://doi.org/10.1111/jiec.13561)\n\n\nPlease cite this article if PULPO is used to produce results for a publication or project.\n\n---\n\n## Authors\n- [@flechtenberg](https://www.github.com/flechtenberg)\n- [@robyistrate](https://www.github.com/robyistrate)\n- [@vtulus](https://www.github.com/vtulus)\n- Bartolomeus Haeussling Loewgren\n---\n[↑ Return](#Top)","funding_links":[],"readme_doi_urls":["https://doi.org/10.1111/jiec.13561"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":[],"project_url":"https://ost.ecosyste.ms/api/v1/projects/111142","html_url":"https://ost.ecosyste.ms/projects/111142"}