Solid Waste Optimization Life‐cycle Framework in Python (Swolfpy)

This paper describes a novel open-source life-cycle optimization framework for solid waste and sustainable materials management applications named solid waste optimization life-cycle framework in Python (SwolfPy). The current version includes life-cycle models for landfills, mass burn waste-to-energy, gasification, centralized composting, home composting, anaerobic digestion, material recovery facilities, refuse-derived fuel facilities, material recycling, transfer stations, and single-family collection. Compared to existing frameworks, SwolfPy streamlines data input/output processes, improves model integration and modularity, provides a wide variety of data visualization and customization, speeds up uncertainty analysis and optimization, and has a user-friendly graphical user interface (GUI). SwolfPy's GUI allows users to define solid waste management networks and scenarios as well as perform comparative life cycle assessments (LCAs), contribution analyses, uncertainty analyses, and optimization. SwolfPy is implemented in Python using Pandas, NumPy, and SciPy for computational tasks, PySide2 for creating the GUI, and Brightway2 for storing life-cycle inventory data and performing the LCA calculations. SwolfPy is modular and flexible, which enables it to be easily coupled with other packages and to facilitate the addition of new processes, materials, environmental flows and impacts, and methodologies. SwolfPy uses sequential least-squares programming for constrained nonlinear optimization to find systems and strategies that minimize cost or environmental emissions and impacts while meeting user-defined constraints. An illustrative case study with 44 materials, 4 collection processes, and 6 treatment processes is presented, and SwolfPy performs 10,000 Monte Carlo iterations in 16 min and finds optimal solutions in 10–25 min on a Windows 10 machine with a CPU speed of 3.60 GHz and 8 logical processors. This article met the requirements for a Gold-Gold Badge. JIE data openness badge described at http://jie.click/badges.