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 centralizeddecentralized waste-to-energy management system (WtEMS) requires an adapted decisionsupport 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 multicriterion 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%. Sets |T|,t ∈T -Life span period of a WTF [year]|I|,i ∈ I -Number of waste generators [unit] |J|, j ∈ J -Number of candidate sites where decentralized (on-site) and centralized (offsite) treatment facilities can be installed [unit] |A|, a ∈ A -Set of technologies available for the deployment |L|, l∈ L -Possible number of units of each technology that can be deployed at each candidate site [unit] Parameters and variables q i , t -Amount of waste generated at each time step, t, by each waste generator, i [tons of waste/year] k a 0 -Unit transformation capacity of treatment facility [tons of processed waste/day] K a , j -Limitation of land space represented by the maximum number of units of technology, a, to be installed at candidate site, j [units] λ a ,r 0 -Amount of recovered resource per ton of treated waste of technology, a [Amount of recovered energy/material/ton of processed waste] − +, u t ❑ u t ❑-Additional continuous variables to determine the smaller value between the quantity of waste, q i , t , generated at WGS i at time step t is greater than or equal to the system capacity, x j ,l , t , installed at candidate site, j v t -Binary decision ...

A research agenda on systems approaches to infrastructure

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