Life cycle optimization oriented to sustainable waste management and circular economy: A review

被引:0
作者
Zhao, Dandan [1 ,2 ]
Chen, Yong [2 ]
Yuan, Haoran [2 ]
Chen, Dezhen [1 ]
机构
[1] Tongji Univ, Thermal & Environm Engn Inst, Sch Mech Engn, 1239 Siping Rd, Shanghai 200092, Peoples R China
[2] Chinese Acad Sci, Guangzhou Inst Energy Convers, Guangzhou 510640, Peoples R China
关键词
Life cycle optimization; Life cycle sustainability analysis; Multi-objective optimization; Waste management; Circular economy; Sustainable development; MUNICIPAL SOLID-WASTE; DECISION-SUPPORT TOOL; ACTIVATED CARBON PRODUCTION; MULTIOBJECTIVE OPTIMIZATION; ENVIRONMENTAL OPTIMIZATION; INDUSTRIAL NETWORKS; RESOURCE-MANAGEMENT; BIOMASS RESOURCES; TRADE-OFFS; ENERGY;
D O I
10.1016/j.wasman.2024.11.001
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Life cycle optimization (LCO) is an effective decision-making method combining life cycle assessment and optimization, which is capable of adjusting system configurations to meet specified sustainability goals. This study analyzed the status quo of LCO studies related to sustainable waste management and the circular economy. Most studies have focused on simultaneously optimizing environmental and economic objectives, whereas few have considered social impacts. Greenhouse gas emissions is the most commonly used environmental indicator in optimization, followed by the endpoint single-score indicator. A static deterministic model is often employed to formulate an LCO problem, while uncertainty and dynamic models are less frequently applied but cause concerns. To deal with multi-objective optimization, the epsilon-constraint method and non-dominated sorting genetic algorithm are popular. Waste LCO has been mainly applied to macro system planning, such as integrated municipal solid waste management systems, biowaste supply chains, waste-to-energy systems, and waste-to- resource networks, aiming to determine optimal waste allocation, facility capacity/location, technology choice, etc. It is occasionally used in optimizing process structure, operating conditions, blending ratio of feedstocks, and product development. Future research should focus on exploring the integration of more environmental and social indicators into multi-objective optimization, modeling under uncertainty, dynamic LCO, process and product optimization, and addressing the lack of multi-scale studies.
引用
收藏
页码:89 / 106
页数:18
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