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Environmental and Economic Modelling for MSW Management Strategies and Reverse Logistic System

Xu, Zonghua
http://orcid.org/0000-0002-0047-2863
http://rave.ohiolink.edu/etdc/view?acc_num=toledo1607130490764957

Abstract Details

2020, Doctor of Philosophy, University of Toledo, Engineering.
This dissertation includes two research studies regarding economic and environmental modeling for Municipal Solid Waste (MSW) management strategies and reverse logistic system. Management strategies for municipal solid waste (MSW) have been studied and conducted concerning GHG emissions related to MSW treatment options for cities and large geographical regions, but few have been conducted related to social cost of GHG emissions from a single facility’s waste treatment management strategy, specifically as it relates to a zero waste to landfill management policy. The first study fills that gap by evaluating economic cost, and social cost of CO2e for the treatment of MSW streams for a single large facility in Ohio, USA moving towards a zero waste to landfill strategy. A total of eight scenarios related to varying IWM strategies at the facility were studied. Life cycle assessment (LCA) was conducted using the Integrated Waste Management Model (IWM) to find the best potential integrated MSW management strategy that minimizes GHG emissions for the facility based on MSW amounts and compositions. A tool was built to analyze the environmental and economic cost for those scenarios that applied different MSW management strategies. The annual operation cost of the MSW management in the facility is applied in the economic modelling, and the GHG emissions from annual MSW generated in the facility is applied in the environmental modelling. MSW treatment options that were applied at the facility including landfill, recycling, Waste to Energy, composting, Anaerobic Digestion (AD). Scenarios were ranked based on the social cost of carbon and economic cost of the MSW management in the facility. The IWM modelling results shows that the facility generated 580 t CO2e from disposing 1377 tons of MSW in 2015, of which 90% was disposed of by landfilling, mostly was from methane emissions. The net GHG emissions were estimated to be -374.4 tons due to the virgin material displacement credit from current recycling. From the 9 scenarios proposed in IWM model, the main options for the facility’s MSW to contribute less GHG emissions are through source reduction, recycling, AD, composting, and Waste to Energy. The results indicated that the economic cost and social cost of CO2e decreased by 49.05% and 2.25 times respectively compared with the current scenario by increasing the recycling rate for the facility by 50%, diverting 30% of food waste to AD processing (from 0%), incinerating the rest of the waste for energy, and sending little to zero waste to landfills. The second research study involved optimizing reverse logistic for the Lucas County District to reduce total economic and environmental cost, mainly by reducing the vehicle traveling miles and GHG gas emissions. The period vehicle routing problem (PVRP) Mathematic model was built for the recycles collection reverse logistic system in Lucas County. The first goal of this research study is to minimize the traveling miles of the current routes under current schedule using ArcGIS network Analyst, which can be used as a baseline case for the following new proposed vehicle routes. The second Goal is solving the mathematical model for a periodic vehicle routing problem (PVRP) with time window and find the optimal solution. The PVRP problem is a complex optimization problem with strong constraints and nonlinearity, its computational complexity makes it difficult to be solved by any optimization software in a reasonably computational time, especially for large-sized problems. Thus, we propose a meta-heuristic method based on Tabu search algorithm. The PVRP problem is divided into vehicle scheduling selection problem and vehicle routing problem, which can effectively reduce the complexity of the problem. At first, the vehicle routing problem based on current routes is optimized in ArcMap -VRP-1 and the vehicle routing problem based on current schedule is optimized -VRP-2, and then the PVRP mathematical model is validated via solving some test problems by GAMS/CPLEX, of which the solutions are further used to compare and analyze the solutions obtained using Tabu search Algorithm. The solutions from the Heuristic search Algorithm is very near to the exact solutions obtained from Gams/Cplex in the sample testing. The results show that the optimized solution using VRP-1 based on current routes and VRP-2 based on current schedule using GIS decreased the total traveling miles by a rate of 12% and 16% respectively, and the TSA optimal solution has a 29% improvement rate compared with the current scenario. And the TSA solution has a 14% improvement compared with the VRP-2 optimized solution from GIS. It shows the proposed algorithm is valid and can be applied to other fields of reverse logistic system.
Matthew Franchetti (Advisor)
230 p.
Industrial Engineering
MSW, LCA, Social cost of CO2e, Reverse Logistic, period vehicle routing, GHG emissions

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Citations

  • Xu, Z. (2020). Environmental and Economic Modelling for MSW Management Strategies and Reverse Logistic System [Doctoral dissertation, University of Toledo]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1607130490764957

    APA Style (7th edition)

  • Xu, Zonghua. Environmental and Economic Modelling for MSW Management Strategies and Reverse Logistic System. 2020. University of Toledo, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=toledo1607130490764957.

    MLA Style (8th edition)

  • Xu, Zonghua. "Environmental and Economic Modelling for MSW Management Strategies and Reverse Logistic System." Doctoral dissertation, University of Toledo, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1607130490764957

    Chicago Manual of Style (17th edition)

toledo1607130490764957
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This open access ETD is published by University of Toledo and OhioLINK.