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SwolfPy - New modelling tools to assist solid waste management

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New modelling tools to assist solid waste systems in meeting their environmental targets

SwolfPy - New modelling tools to assist solid waste management


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North Carolina State University researchers have created a free, user-friendly tool that uses multiple computational models to help solid waste systems achieve environmental goals in the most cost-effective manner possible.

A new open-source life-cycle optimization framework for solid waste and sustainable materials management applications (SwolfPy). 

Landfills, mass burn waste-to-energy, gasification, centralised composting, home composting, anaerobic digestion, material recovery facilities, refuse-derived fuel plants, material recycling, transfer stations, and single-family collection are all included in the current version. 

SwolfPy increases model integration and modularity, provides a broad variety of data visualisation and customisation, speeds up uncertainty analysis and optimization, and has a user-friendly graphical user interface when compared to previous frameworks (GUI). Users can define solid waste management networks and scenarios, as well as do comparative life cycle assessments (LCAs), contribution studies, uncertainty analysis, and optimization, using SwolfPy's GUI.

Users can define solid waste management networks and scenarios, as well as perform comparative LCAs, contribution analyses, uncertainty analyses, and optimization, using the SwolfPy GUI. SwolfPy is written in Python and employs Pandas, NumPy, and SciPy for computational tasks, PySide2 for GUI development, and Brightway2 for life-cycle inventory data storage and LCA calculations. SwolfPy is modular and adaptable, allowing for simple integration with other packages as well as the addition of new processes, materials, environmental flows and impacts, and methodologies.

SwolfPy uses restricted nonlinear optimization with sequential least-squares programming to develop systems and methods that minimise costs, emissions, and effects while fulfilling user-defined restrictions. SwolfPy runs 10,000 Monte Carlo iterations in 16 minutes on a Windows 10 computer with a CPU speed of 3.60 GHz and 8 logical processors, and discovers optimal solutions in 10–25 minutes using an exemplary case study with 44 materials, 4 collecting operations, and 6 treatment processes.

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