Solid Waste Optimization Life-Cycle Framework
About SWOLF
The Solid Waste Optimization Life-Cycle Framework (SWOLF) model is being developed by researchers in the Department of Civil, Construction and Environmental Engineering at North Carolina State University (NCSU).
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Background
In 2011, U.S. municipal solid waste (MSW) systems processed approximately 250 million tons of waste, resulting in over 120 Tg of CO2e emissions. MSW contains significant quantities of recoverable materials and can be used to generate renewable energy, making the solid waste management (SWM) system a potentially high-impact target for enhancing environmental sustainability. SWM systems consist of long-lived infrastructure and must adapt to changes in population as well as waste generation and composition. SWM systems must also proactively adapt to the rapidly changing energy system since it will affect the costs and environmental performance of SWM systems. Systematic analysis of SWM—from unit processes to integrated systems is necessary to identify opportunities to cost effectively maximize environmental sustainability. SWOLF is a stage-wise optimization framework for SWM life-cycle assessment (LCA), capable of minimizing cost while accounting for environmental emissions and impacts, while considering changes to waste generation, composition and the energy system over time.
Approach
The foundation of SWOLF is independent LCA models for the various processes that comprise SWM systems (e.g. collection, landfilling, waste-to-energy, recycling, composting, etc.). These modules can be used as stand-alone models to assess individual technologies, but their real power is realized when they are systematically connected in an integrated multi-stage modeling framework (Fig. 1). The integrated modeling framework optimizes future SWM infrastructure decisions in stages (e.g., 1, 5, 10 year increments) over a user-specified decision horizon (e.g., 20, 30, 50 years) while considering projected changes to the energy mix and prices under various GHG mitigation policies (Fig. 2). The SWM process models estimate the unit costs and emissions associated with processing solid waste through each process as a function of waste composition and quantity. These coefficients are developed for each stage of the analysis, and a mixed-integer linear programming model is then used to determine the optimal SWM facility decisions (e.g., building, expanding, decommissioning) and waste mass flows over the model decision horizon, while considering policy targets (e.g., landfill diversion, GHG targets, budget constraints) and economic or environmental objectives. The results can then inform the development of cost effective sustainable SWM systems.
Fig 1. A sample waste management system potential mass flow diagram. Mixed waste collection collects all of the generated waste, while residual collection collects the remaining waste after recyclable and organics collection. Separated material from the MRFs can either be recycled or treated by WTE combustion. Bottom ash can be recycled as aggregate in concrete, and the aluminum and ferrous in the bottom ash can be separated and recycled. The distinction between bottom and fly ash has been removed for simplicity.
Fig 2. Generalized modeling framework showing how energy system modeling is connected to LCA models for a SWM system, and how the outputs of these models are then used as inputs into an optimizable LCA framework to systematically analyze future SWM.