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MULTI-OBJECTIVE OPTIMIZATION OF COMBINEDSEWER OVERFLOW CONTROL SYSTEM
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摘要: 在城镇合流制溢流（CSO）污染控制系统的规划建设过程中，提高CSO削减率的同时降低建设成本一直受到规划建设决策者的广泛关注。针对CSO控制系统优化问题，利用Matlab、Python和SWMM模型建立了CSO控制系统建设成本与CSO削减率的多目标优化模型，借助非支配排序遗传算法（NSGA-Ⅱ）对实例求解得出一系列Pareto解集，并进一步为决策者提供多套选择方案。分析结果表明：在3年一遇设计降雨条件下，优化结果中源头LID设施、末端合流制调蓄池与中途截流管网改造的建设成本占比依次降低，其中末端合流制调蓄池的CSO削减率贡献最高，源头LID设施次之，中途截流管网改造最低。Abstract: How to reduce the construction cost while increasing the reduction rate of combined sewer overflow (CSO) control system has been widely concerned by the planning & decision makers. In this study, the multi-objective optimization model of CSO system construction cost and CSO reduction rate was established by using Matlab, Python and SWMM. A series of Pareto solution sets were obtained using non-dominant sorting genetic algorithm (NSGA-Ⅱ), so different options were put forward to planning decision makers. For the 3-year storm events, the optimization results showed that the proportion of the construction cost was highest for constructing source low impact development (LID) facilities, followed by constructing the detention tanks and retrofitting the midway pipes. Among the three types of facilities, the contribution of the CSO reduction rate of the detention tanks was the highest, followed by the source LID facilities and the midway pipes.
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Key words:
- combined sewer overflow /
- multi-objective optimization /
- NSGA-Ⅱ /
- low impact development
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