New Tool to Help Solid Waste Systems Meet Cost and Environmental Goals

Researchers at North Carolina State University have developed a free, user-friendly tool that uses multiple calculation models to help solid waste management systems meet their environmental goals in the most cost-effective way possible.

Waste management systems do more than just put solid waste in landfills. These systems must not only store or recycle solid waste safely, but must also minimize the health risks associated with the waste, minimize the environmental risks associated with air or water pollution, and minimize the emissions of greenhouse gases (GHGs) that can be produced as solid waste is processed or decomposes.

“The challenge is that there are a myriad of things waste management systems can do to achieve these goals,” says James Levis, co-author of a paper on the new tool and assistant research professor of civil engineering. , construction and environment in NC. State. “And many of these actions have trade-offs, in terms of cost, environmental impact, technical challenges, etc.

“To solve this problem, we created an open source tool called Solid Waste Optimization Life-cycle in Python (SwolfPy), which allows users to evaluate all of these options in one place. This can help users determine the best course of action for a specific set of circumstances. And, because it’s open-source, the solid waste community can develop additional features over time to make the tool even more useful in guiding decision-making.

“SwolfPy is a dynamic tool,” says Mojtaba Sardarmehni, corresponding author of the paper and Ph.D. student at NC State. “For example, if someone develops a better model for one of its components, the open-source platform will allow users to update SwolfPy.”

The SwolfPy framework includes a collection of process models and a user interface that allows users to plug in data relevant to their situation. SwolfPy will then run the numbers and do two things. First, it gives users a concise overview of their current global operations and what that means for their costs and environmental goals. Second, SwolfPy gives users the best combination – or combinations – of processes that would allow them to meet their goals for cost, GHG emissions, and more.

But users don’t have to use the default templates included in SwolfPy. Users can also choose to develop process models tailored to their specific projects and connect these models to SwolfPy; or users can use a combination of default templates and custom templates. Whichever suite of templates they choose, SwolfPy lets users plug their target numbers into the UI, and SwolfPy will let them know which combination of processes will get them closest to their goals.

“To be clear, there isn’t always a best solution,” says Sardarmehni. “For example, there may be one process combination that is the most cost-effective, while a second option is less cost-effective but does a better job of reducing GHG emissions. What SwolfPy does is identify the range of best possible options for users, based on how they prioritize their goals.

“We believe SwolfPy will be a useful tool for waste management companies, government policymakers dealing with solid waste issues, state policymakers, and the research community,” says Levis.

SwolfPy is already available for free online at https://swolfpy-project.github.io/.

“We are open to hearing from people in the solid waste community who have ideas or questions about how SwolfPy can be used, as well as what can be done to continue to refine it as a practical tool. “, says Levis.

The article, “Solid Waste Optimization Life-cycle Framework in Python (SwolfPy)”, is published in the Industrial Ecology Journal. The article was co-authored by Pedro Chagas Anchieta, a former NC State graduate student.

The work was done with support from the National Science Foundation under grant 1437498 and the Environmental Research and Education Foundation.

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Note to Editors: The summary of the study follows.

“Solid Waste Optimization Lifecycle Framework in Python (SwolfPy)”

Authors: Mojtaba Sardarmehni, Pedro H. Chagas Anchieta and James W. Levis, North Carolina State University

Posted: Jan 13 Industrial Ecology Journal

DO I: 10.1111/jiec.13236

Summary: This article describes a new open source lifecycle optimization framework for solid waste and sustainable materials management applications named SwolfPy. The current version includes life cycle models for landfills, mass combustion of waste to energy, gasification, centralized composting, backyard composting, anaerobic digestion, material recovery facilities, waste-to-energy facilities. waste, material recycling, transfer stations and single-family homes. 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 analysis and optimization of uncertainties and has a user-friendly graphical user interface (GUI). SwolfPy’s graphical interface allows users to define solid waste management networks and scenarios, as well as perform comparative life cycle assessments (LCAs), contribution analyses, uncertainty analyzes and optimizations. SwolfPy is implemented in Python using Pandas, NumPy, and SciPy for computational tasks, PySide2 to create the GUI, and Brightway2 to store lifecycle inventory data and perform ACL calculations. SwolfPy is modular and flexible, allowing it to be easily coupled with other packages and making it easy to add new processes, materials, flows and environmental impacts and methodologies. SwolfPy uses sequential least-squares programming for constrained nonlinear optimization to find systems and strategies that minimize costs or emissions and environmental impacts while meeting user-defined constraints. An illustrative case study with 44 materials, four collection processes and six processing processes is presented, and SwolfPy performs 10,000 Monte Carlo iterations in 16 minutes and finds optimal solutions in 10-25 minutes on a Windows 10 machine with a 3.60 GHz processor speed and 8 logical processors. [Note: This article met the requirements for a Gold-Gold Badge. JIE data openness badge described at http://jie.click/badges.]

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