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SWIMS: a dynamic life cycle-based optimisation and decision support tool for solid waste management

SWIMS: a dynamic life cycle-based optimisation and decision support tool for solid waste management
SWIMS: a dynamic life cycle-based optimisation and decision support tool for solid waste management
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.
life cycle assessment, Optimisation, infrastructure planning, waste management, non-linear programming, Sustainability
0959-6526
Roberts, Keiron
0422e8bc-1823-4a2c-bc80-98d6d2f9c166
Turner, David A
0542a602-16ce-4aa8-9ca4-9e8d2c72d3c5
Coello, Jonathan
479afe88-fc76-410a-839c-8baba3efec11
Stringfellow, Anne
024efba8-7ffc-441e-a268-be43240990a9
Bello, Ibrahim
b9ef7b75-b7eb-435d-a4e5-186f48f328ea
Powrie, William
600c3f02-00f8-4486-ae4b-b4fc8ec77c3c
Watson, Geoff
a7b86a0a-9a2c-44d2-99ed-a6c02b2a356d
Roberts, Keiron
0422e8bc-1823-4a2c-bc80-98d6d2f9c166
Turner, David A
0542a602-16ce-4aa8-9ca4-9e8d2c72d3c5
Coello, Jonathan
479afe88-fc76-410a-839c-8baba3efec11
Stringfellow, Anne
024efba8-7ffc-441e-a268-be43240990a9
Bello, Ibrahim
b9ef7b75-b7eb-435d-a4e5-186f48f328ea
Powrie, William
600c3f02-00f8-4486-ae4b-b4fc8ec77c3c
Watson, Geoff
a7b86a0a-9a2c-44d2-99ed-a6c02b2a356d

Roberts, Keiron, Turner, David A, Coello, Jonathan, Stringfellow, Anne, Bello, Ibrahim, Powrie, William and Watson, Geoff (2018) SWIMS: a dynamic life cycle-based optimisation and decision support tool for solid waste management. Journal of Cleaner Production. (doi:10.1016/j.jclepro.2018.05.265).

Record type: Article

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.

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SWIMS: a dynamic life cycle-based optimisation and decision support tool for solid waste management - Accepted Manuscript
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Accepted/In Press date: 29 May 2018
e-pub ahead of print date: 7 June 2018
Keywords: life cycle assessment, Optimisation, infrastructure planning, waste management, non-linear programming, Sustainability

Identifiers

Local EPrints ID: 421414
URI: http://eprints.soton.ac.uk/id/eprint/421414
ISSN: 0959-6526
PURE UUID: 4a5c8073-3c65-40b0-8860-1d615a86d144
ORCID for Anne Stringfellow: ORCID iD orcid.org/0000-0002-8873-0010
ORCID for William Powrie: ORCID iD orcid.org/0000-0002-2271-0826
ORCID for Geoff Watson: ORCID iD orcid.org/0000-0003-3074-5196

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Date deposited: 11 Jun 2018 16:30
Last modified: 27 Jan 2020 13:39

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