辐射防护通讯 ›› 2025, Vol. 45 ›› Issue (2): 1-5.

• 进展与评述 • 上一篇    下一篇

基于多目标决策的核电厂气载流出物排放优化研究进展

武翡翡, 康晶, 陈海龙, 廉冰   

  1. 中国辐射防护研究院,生态环境部辐射环境与健康重点实验室,太原 030006
  • 收稿日期:2024-03-19 出版日期:2025-04-20 发布日期:2025-04-29
  • 通讯作者: 廉冰。E-mail: lianbing00@sina.com
  • 作者简介:武翡翡(1987—),女,2009年毕业于北京交通大学环境工程专业(本科),2012年毕业于北京师范大学环境科学专业(硕士),2021年毕业于北京师范大学环境科学专业(博士),副研究员,主要从事辐射环境影响评价研究。E-mail: wff_bnu@163.com

Research progress on optimization of airborne effluent discharges from nuclearpower plants based on multi-objective decision-making

WU Feifei, KANG Jing, CHEN Hailong, LIAN Bing   

  1. China Institute for Radiation Protection, Key Laboratory of Radiation Environment & Health of the Ministry of Ecology and Environment, Taiyuan 030006
  • Received:2024-03-19 Online:2025-04-20 Published:2025-04-29

摘要: 流出物排放优化是放射性废物管理的重要基础性工作,开展流出物排放优化研究可为核能安全有序发展以获取最佳的经济、环境和社会效益提供决策依据。本文基于辐射防护三原则和多目标优化决策的理论基础,系统分析了流出物排放优化的剂量、经济和社会等多重影响因素,对比分析了代价-效益、代价-利益、多属性效用分析、第二代非支配排序遗传算法等传统优化方法和智能优化算法的优缺点,提出未来研究展望与启示,为提高流出物排放优化管理与决策水平提供科学参考。

关键词: 气载流出物, 多目标优化, 智能优化算法

Abstract: Effluent discharge optimization is an important basic work of radioactive waste management. The study on effluent discharge optimization can provide a decision-making basis for the safe and orderly development of nuclear energy, with the aim to obtain best economic, environmental and social benefits. Based on the three principles of radiation protection and the theoretical basis of multi-objective optimization decision-making, this study systematically analyzes the multiple influencing factors such as dose, economic and social effect of effluent discharge optimization, compares and analyzes the advantages and disadvantages of traditional optimization methods and intelligent optimization algorithms, such as cost-effectiveness, cost-benefit, multi-attribute utility analysis, and second-generation non-dominant sequencing genetic algorithm, and puts forward future research prospects and enlightenment, with aim to to provide scientific reference for improving the level of optimal management and decision-making of effluent discharges.

Key words: airborne effluent, multi-objective optimization, intelligent optimization algorithms

中图分类号: 

  • TL75+2