4.7 Article

Implementing Circular Economy in municipal solid waste treatment system using P-graph

Journal

SCIENCE OF THE TOTAL ENVIRONMENT
Volume 701, Issue -, Pages -

Publisher

ELSEVIER
DOI: 10.1016/j.scitotenv.2019.134652

Keywords

Waste to resources; Circular Economy; GHG emission; P-graph

Funding

  1. EU by Czech Republic Operational Programme Research and Development, Education, Priority 1: Strengthening capacity for quality research [CZ.02.1.01/0.0/0.0/15_003/000 0456]

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Municipal solid waste (MSW) is one of the issues associated with the growth of economic and urban population. The aim of this study is to develop an integrated design of waste management systems in support of a Circular Economy by P-graph (a bipartite graphical optimisation tool) as an effective decision support tool. The case study considers four MSW compositions based on different country income levels. Solving the P-graph model identifies the most suitable treatment approaches, considering the economic balance between the main operating cost, type, yield, quality of products, as well as the GHG emission (externality cost). The identification of near-optimal solutions by P-graph is useful in dealing with the trade-offs between conflicting objectives, e.g. local policy and practical implementation, that are difficult to monetise. For a lower-income country, the optimal solution includes a combination of at source separation, recycling, incineration (heat, electricity), anaerobic digestion (biofuel, digestate) and the landfill. It avoids an estimated 411 kg CO2eq/t of processed MSW and achieves a potential profit of 42 SIC/t of processed MSW. The optimisation generally favours mechanical biological treatment as the country income level rises, which affects the composition of the MSW. The relative prices of biofuel, electricity and heat (>20%) cause a significant impact on the highest-ranking treatment structure and overall profit. This study shows that the developed framework by P-graph is an effective tool for MSW systems planning. For future study, localised data inputs can be fed into the proposed framework for a customised solution and economic feasibility assessment. (C) 2019 Elsevier B.V. All rights reserved.

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