Researchers at North Carolina State University developed a user-friendly tool that uses multiple computational models to help solid refuse systems achieve their environmental goals the most cost-efficiently possible.
Solid waste management systems do more than just dump it into landfills. These systems must not only store or recycle solid waste safely, but also minimize any health risks and environmental risks associated waste.
The challenge is that there are a host of things waste management systems can do to accomplish these goals, says James Levis, co-author of a paper on the new tool and a research assistant professor of civil, construction and environmental engineering at NC State. Many of these actions are subject to trade-offs in terms cost, environmental impact, technical difficulties, and so forth.
We have created an open-source tool called Solid Waste optimization Life-cycle in Python (SwolfPy) that allows users to compare all options in one location. This can help users choose the best course for each situation. Open-source means that the solid waste community can add features to the tool over time to make it even more useful in decision-making.
SwolfPy is a dynamic tool, says Mojtaba Sardarmehni, corresponding author of the paper and a Ph.D. student at NC State. If someone creates a better model of one of its components, users can update SwolfPy via the open-source platform.
The SwolfPy framework has a set of process models and a user interface that allows users input data that is relevant to their situation. SwolfPy will then run the numbers, and do two things. It provides a quick snapshot of the current operations and how it impacts their environmental and cost goals. SwolfPy also helps users to determine the best combination of processes that will allow them reach their goals for cost and GHG emissions.
Users don’t have to use the standard models in SwolfPy. Users can create process models specific to their projects and connect these models to SwolfPy. Users can also use a combination or all of the standard models and customized models. SwolfPy lets users input their target numbers in the user interface. SwolfPy will then tell them which combination of models will help them get closer to their goals.
Sardarmehni clarifies that there isn’t always a best solution. One combination of processes may be the most cost-effective. A second option, however, is more cost-effective and does a better job of reducing GHG emission. SwolfPy helps users determine the best options, based on how they prioritize their goals.
Levis says that SwolfPy will prove to be a valuable tool for waste management companies, government decision-makers who deal with solid waste issues and state policymakers.
SwolfPy is available online already https://swolfpy-project.github.io/.
Levis states that we are open to hearing from the solid waste community if they have any ideas or questions about SwolfPy and what can be done to improve it as a practical tool.
The paper Solid Waste Optimization Life-cycle Framework in Python (“SwolfPy”)The publication of, is available in the Journal of Industrial Ecology. The paper was co-authored by Pedro Chagas Anchieta, a former graduate student at NC State.
The National Science Foundation granted grant 1437498 and the Environmental Research and Education Foundation supported the work.
Note to EditorsThe study abstract follows.
Solid Waste Optimization Life-cycle Framework in Python (“SwolfPy”)
Authors: Mojtaba Sardarmehni Pedro H. Chagas Anchieta James W. Levis, North Carolina State University
Published: Jan. 13, Journal of Industrial Ecology
Abstract:This paper discusses SwolfPy, a novel open-source lifecycle optimization framework for solid and sustainable waste management applications. The current version includes life cycle models for landfills and mass-burn waste-to-energy. It also includes home composting, anaerobic digesting, material recovery facilities. It also includes refuse-derived fuel facilities, material recycle, transfer stations, single-family collection. SwolfPy is faster than existing frameworks and offers a wide range of customization and data visualization. It also speeds up uncertainty analysis, optimization, and speed up modeling integration. SwolfPys GUI lets users create solid waste management scenarios and networks, perform comparative life cycle assessments (LCAs), contribute analyses, uncertainty analyses, optimization, and more. SwolfPy is implemented using Python. It uses Pandas, NumPy and SciPy to perform computational tasks, PySide2 to create the GUI, and Brightway2 to store life-cycle inventory data. LCA calculations are performed using Brightway2. SwolfPy is modular, flexible, and can be easily paired with other packages. This allows for the easy addition of new processes and materials, as well as methods and impacts. SwolfPy employs Sequential Least Squares Programming for constrained, non-linear optimization. This allows it to find strategies and systems that reduce cost, environmental emissions, and impacts while still meeting user-defined limitations. SwolfPy can perform 10,000 Monte Carlo iterations in just 16 minutes, and find optimal solutions in 10-25 minutes on a Windows 10 computer with a CPU speed up to 3.60GHz and 8 logical processing units. [Note: This article met the requirements for a Gold-Gold Badge. JIE data openness badge described at http://jie.click/badges.]