SWIMS: A dynamic life cycle-based optimisation and decision support tool for solid waste management. (20th September 2018)

• Record Type: Journal Article
• Authors: Roberts, Keiron P.; Turner, David A.; Coello, Jonathan; Stringfellow, Anne M.; Bello, Ibrahim A.; Powrie, William; Watson, Geoff V.R.
• Is Part Of: Journal of cleaner production. Volume 196(2018)
• Page Start: 547
• Page End: 563
• Publication Date: 2018-09-20
• Subjects: Life cycle assessment -- Optimisation -- Infrastructure planning -- Waste management -- Non-linear programming -- Sustainability; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2018.05.265
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: 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. Graphical abstract: Image 1