Multi-objective optimization of technology solutions in municipal solid waste treatment system coupled with pollutants cross-media metabolism issues.

The environmental impact, energy conservation, and economic cost are prominent decision criteria in municipal solid waste (MSW) management, among which trade-off relationships widely exist because of different features of pollutant treatment technologies. These three objectives should thereby be simultaneously considered in the design of technology combinations in MSW treatment system (MSWTS). In addition, comprehensive characterization of environmental impact of the whole MSWTS should cover the complex pollutants cross-media metabolism in the treatment of both MSW and subsequent secondary pollution. This study developed a multi-objective optimization model to select optimal technology solutions in MSWTS. Three objectives, the minimizations of total environmental impact calculated from pollutants cross-media metabolism perspective, net energy consumption, and total cost are optimized through the second generation of the Non-dominated Sorting Genetic Algorithm (NSGA-II). Final MSW management schemes under environment, energy, and cost preferences are obtained through Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method. This paper uses China's MSWTS as a case study and finds that Pareto optimal solutions can reduce the total environmental impact and the net energy consumption by 24.2% and 7.4% respectively, while increase the total cost by 18.2% in average, compared with the baseline scenario. The promotion of MSW biological treatment technologies, especially anaerobic digestion (AD), can effectively improve the environmental performance of MSWTS, while the current vigorous promotion of MSW incineration in China is not recommended. Sludge co-processing in cement kiln is highly promoted under all three types of management preferences. In summary, the proposed methodology can provide decision support for the optimal design of technology solutions in MSWTS.