SWIMS: a dynamic life cycle-based optimisation and decision support tool for solid waste management
| Autor: | Keiron Roberts, David A. Turner, Ibrahim A. Bello, William Powrie, Jonathan Coello, Geoff Watson, Anne Stringfellow |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Decision support system
Process modeling Municipal solid waste optimisation Computer science 020209 energy Strategy and Management non-linear programming 02 engineering and technology 010501 environmental sciences 01 natural sciences EP/N017064/1 Industrial and Manufacturing Engineering Nonlinear programming life cycle assessment Economic cost Genetic algorithm 0202 electrical engineering electronic engineering information engineering Life-cycle assessment 0105 earth and related environmental sciences General Environmental Science EP/I01344X/1 Renewable Energy Sustainability and the Environment RCUK sustainability EPSRC infrastructure planning Risk analysis (engineering) Sustainability waste management |
| Zdroj: | Roberts, K, Turner, D A, Coello, J, Stringfellow, A M, Bello, I A, Powrie, W & Watson, G V R 2018, ' SWIMS: A dynamic life cycle-based optimisation and decision support tool for solid waste management ', Journal of Cleaner Production, vol. 196, pp. 547-563 . https://doi.org/10.1016/j.jclepro.2018.05.265 |
| DOI: | 10.1016/j.jclepro.2018.05.265 |
| Popis: | Solid waste management (SWM) decision makers are under increasing pressure to implement strategies that are both cost effective and environmentally sound. Consequently, SWM has developed into a highly complex systemic planning problem and analytical tools are needed to assist in the development of more sustainable SWM strategies. Here, we present the Solid Waste Infrastructure Modelling System (SWIMS) software, which is the first non-linear dynamic, LCA-based optimisation tool for SWM that optimises for both economic and environmental performance. The environmental and economic costs of treating generated wastes at available treatment facilities are calculated through a series of life cycle process models, based on non-linear expressions defined for each waste material and each treatment process type. Possible treatment paths for waste streams are identified using a depth first search algorithm and a sequential evolutionary genetic algorithm is used to prioritise the order of these paths, in lieu of user defined optimisation criteria and constraints. SWIMS calculates waste arisings into the future and determines if it is possible to treat generated waste, while considering present and future constraints (e.g. capacity). If additional capacity is required, SWIMS will identify the optimum infrastructure solution to meet this capacity demand. A demonstrative case study of MSW management in GB from 2010 to 2050 is presented. Results suggest that sufficient capacity is available in existing and planned infrastructure to cope with future demand for SWM and meet national regulatory and legislative requirements with relatively little capital investment beyond 2020. SWIMS can be used to provide valuable information for SWM decision makers, particularly when used to analyse the effects of possible future national or regional policies. |
| Databáze: | OpenAIRE |
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