State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, China.; Industrial Energy Saving and Green Development Assessment Center, Tsinghua University, Beijing 100084, China.
State Key Joint Laboratory of Environment Simulation and Pollution Control (SKLESPC), School of Environment, Tsinghua University, Beijing 100084, China.; Industrial Energy Saving and Green Development Assessment Center, Tsinghua University, Beijing 100084, China.
Sci Total Environ. 2022 Feb 10;807(Pt 1):150664. doi: 10.1016/j.scitotenv.2021.150664. Epub 2021 Sep 29.
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.
环境影响、节能和经济成本是城市固体废物（MSW）管理的突出决策标准，由于污染物处理技术的不同特点，这些标准之间广泛存在权衡关系。因此，在设计 MSW 处理系统（MSWTS）中的技术组合时，应同时考虑这三个目标。此外，全面描述 MSWTS 的环境影响应涵盖 MSW 处理和后续二次污染中复杂污染物的跨介质代谢。本研究开发了一种多目标优化模型，以选择 MSWTS 中的最佳技术解决方案。通过第二代非支配排序遗传算法（NSGA-II），优化了总环境影响（从污染物跨介质代谢角度计算）、净能耗和总成本的最小化三个目标。通过 Vlsekriterijumska Optimizacija I Kompromisno Resenje（VIKOR）方法获得了在环境、能源和成本偏好下的最终 MSW 管理方案。本文以中国的 MSWTS 为例，发现与基线情景相比，帕累托最优解可以分别减少 24.2%和 7.4%的总环境影响和净能耗，同时平均增加 18.2%的总成本。推广 MSW 生物处理技术，特别是厌氧消化（AD），可以有效提高 MSWTS 的环境性能，而目前中国大力推广 MSW 焚烧是不可取的。在所有三种管理偏好下，都高度提倡在水泥窑中协同处理污泥。总之，所提出的方法可以为 MSWTS 中的技术解决方案的最优设计提供决策支持。
