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Application of MCAT to provide multi-objective optimization model for municipal waste management system
- Source :
- J Environ Health Sci Eng
- Publication Year :
- 2021
-
Abstract
- Choosing an appropriate municipal waste management method is a very complicated environmental problem in cities. This research introduces an optimization model for waste management in the southwest region of Tehran province. It was developed by a metaheuristic algorithm that was used to minimize the economic and environmental costs. Incineration, composting, recycling and landfilling waste management methods were considered. Three scenarios were developed to determine the optimum allocation of waste to each method such to fulfill the objective of overall minimum of environmental burdens and costs. A multi-objective scenario selection model was implemented by the compromise programming method in MCAT software. Considering the budget limitation and available facilities on site, optimum allocations to recycling, composting, incineration and landfilling methods were obtained as 115,486, 132,094, 71,905 and 45,516 tons/year, respectively. The results of this study indicated that the metaheuristic algorithm in MCAT software was an efficient tool in decision making about waste management systems and thus, it was suggested to municipality managers and regional planning authorities.
- Subjects :
- Environmental Engineering
Operations research
business.industry
Computer science
Health, Toxicology and Mutagenesis
Public Health, Environmental and Occupational Health
Pollution
Applied Microbiology and Biotechnology
Multi-objective optimization
Incineration
Software
Regional planning
Optimal allocation
Compromise programming
business
Waste Management and Disposal
Municipal waste management
Metaheuristic
Water Science and Technology
Research Article
Subjects
Details
- ISSN :
- 2052336X
- Volume :
- 19
- Issue :
- 2
- Database :
- OpenAIRE
- Journal :
- Journal of environmental health scienceengineering
- Accession number :
- edsair.doi.dedup.....cd67d5363a49f4d0d861ab48fd9e7024