Integrated decision-support methodology for combined centralized-decentralized waste-to-energy management systems design.

Abstract The rapid expansion of urban populations and concomitant increase in the generation of municipal solid waste (MSW) exert considerable pressure on the conventional centralized MSW management system and are beginning to exceed disposal capacities. To tackle this issue, the conventional centralized MSW management system is more likely to evolve toward a more decentralized system with smaller capacity waste treatment facilities that are integrated at different levels of the urban environment, e.g., buildings, districts, and municipalities. In addition, MSW can become an important urban resource to address the rising energy consumption through waste-to-energy (WTE) technologies capable of generating electricity, heat, and biogas. This shift toward the combined centralized-decentralized waste-to-energy management system (WtEMS) requires an adapted decision-support methodology (DSM) that can assist decision-makers in analyzing MSW generation across large urban territories and designing optimal long-term WtEMS. The proposed integrated DSM for WtEMS planning relies on: i) an MSW segregation and prediction methodology, ii) an optimization methodology for the deployment of multi-level urban waste infrastructure combining centralized and decentralized facilities, and iii) a multi-criterion sustainability framework for WtEMS assessment. The proposed DSM was tested on a case study that was located in Singapore. The proposed WtEMS not only reduced the total operational expenses by about 50%, but also increased revenues from electricity recovery by two times in comparison with the conventional MSW management system. It also allowed more optimal land use (capacity-land fragmentation was reduced by 74.8%) and reduced the size of the required transportation fleet by 15.3% in comparison with the conventional MSW system. The Global Warming Potential (GWP) was improved by about 18.7%. Highlights • Integrated Decision Support Methodology for Waste-to-Energy Management System planning for Municipal Solid Waste segregation and prediction. • Integrates methodologies for segregation and prediction, facilities deployment optimization, and multi-criterion sustainability analysis. • Case study shows 50% total operational expenses reduction, twice revenue increase from electricity recovery in comparison with conventional MSW. • Results show 74.8% reduction in capacity-land fragmentation, 15.3% reduction in required transportation fleet compared with conventional systems. • Results also show improved Global Warming Potential by about 18.7%. [ABSTRACT FROM AUTHOR]