This case study was implemented to illustrate the functionality of SwolfPy for SWM system analysis. The case study first evaluated the global warming potential (GWP) of the SWM system in a hypothetical but realistic city and then used the optimization functionality to minimize the GWP and meet a landfill diversion target of 40%. Monte Carlo simulation was used to explore the robustness of the results to the uncertainty in input data.
| Module | Version | Installation |
|---|---|---|
| swolfpy-inputdata | 
|
pip install swolfpy-inputdata==0.2.3 |
| swolfpy-processmodels | 
|
pip install swolfpy-processmodels==0.1.8 |
| swolfpy | 
|
pip install swolfpy==0.2.4 |
How to cite this article: Sardarmehni M, Anchieta PHC, Levis JW. Solid waste optimization life-cycle framework in Python (SwolfPy). JInd Ecol. 2022;1–15. https://doi.org/10.1111/jiec.13236