F. Babalola, A. O. Adelopo, Boluwatito M. Aiyepola, A. T. Nubi
{"title":"Cost minimization and operation optimization model for strategic waste management decision plans: A case study","authors":"F. Babalola, A. O. Adelopo, Boluwatito M. Aiyepola, A. T. Nubi","doi":"10.5897/AJEST2020.2908","DOIUrl":null,"url":null,"abstract":"The design of a sustainable waste management system is pivoted on the ability to generate and compute real-time operational data for a strategic developmental decision plan. The real-time waste data generated at the University of Lagos Campus waste management system was used to develop a mathematical model with three operational indicators, namely, Total Cost Indicator (TCI), to show the overall daily cost for managing one metric ton of mixed municipal waste in the system from collection to final disposal, Lost Revenue Indicator (LRI), to show revenue loss for each metric ton of residual waste landfilled, and Material Recovery Indicator (MRI), to show the fraction of recyclable materials recovered from collected mixed waste. All three indicators were calculated at different recyclable materials recovery efficiencies to determine the cost implication on the system’s operational parameters. The model revealed that the present municipal solid waste (MSW) system operates at a recyclables recovery efficiency rate of approximately 3% but can be increased at optimum capacity to 18%. This performance improvement will result in a cost reduction of $0.32/ton when daily sorters’ capacity, material revenue potential and result-based financing recycling operations are determined using this model as a strategic management planning tool. The model provides an adaptable framework for the development of MSW management decision plans for cities in a developing nation. \n \n \n \n Key words: Material recovery, municipal solid waste, cost minimization model.","PeriodicalId":7483,"journal":{"name":"African Journal of Environmental Science and Technology","volume":"2004 1","pages":"98-108"},"PeriodicalIF":0.0000,"publicationDate":"2021-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"African Journal of Environmental Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5897/AJEST2020.2908","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The design of a sustainable waste management system is pivoted on the ability to generate and compute real-time operational data for a strategic developmental decision plan. The real-time waste data generated at the University of Lagos Campus waste management system was used to develop a mathematical model with three operational indicators, namely, Total Cost Indicator (TCI), to show the overall daily cost for managing one metric ton of mixed municipal waste in the system from collection to final disposal, Lost Revenue Indicator (LRI), to show revenue loss for each metric ton of residual waste landfilled, and Material Recovery Indicator (MRI), to show the fraction of recyclable materials recovered from collected mixed waste. All three indicators were calculated at different recyclable materials recovery efficiencies to determine the cost implication on the system’s operational parameters. The model revealed that the present municipal solid waste (MSW) system operates at a recyclables recovery efficiency rate of approximately 3% but can be increased at optimum capacity to 18%. This performance improvement will result in a cost reduction of $0.32/ton when daily sorters’ capacity, material revenue potential and result-based financing recycling operations are determined using this model as a strategic management planning tool. The model provides an adaptable framework for the development of MSW management decision plans for cities in a developing nation.
Key words: Material recovery, municipal solid waste, cost minimization model.