Hydraulic optimization simulation for reducing confluence and controlling the overflow pollution of storage ponds based on the Storm Water Management Model and Non-dominated Sorting Genetic Algorithm-II

IF 2.1 4区 环境科学与生态学 Q2 ENGINEERING, CIVIL
Cuntian Jin
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引用次数: 0

Abstract

The Storm Water Management Model (SWMM) was established to simulate rainfall–runoff dynamically, and the internal runoff component of the SWMM was used to simulate rainfall operation in each watershed, including rainfall–runoff and scour pollution load. Then, using the routing component in the SWMM, the properties of runoff into the tank system are calculated through pipelines and other facilities to obtain the optimal tank volume. The coupling optimization model was established, and the algebraic function of the storage capacity, total runoff, and total cost was established by using the multiple linear regression method, which was transformed into the optimization model aiming at the minimum total runoff and total cost. The NSGA-II is improved by using a reverse learning mechanism. By solving the optimization model, the non-dominant solution of the proxy model is obtained. The non-dominant solution was substituted into the SWMM, and the rationality of the optimization results was analyzed. The experimental results show that the reservoir volume determined by this method can effectively accept the pollutants brought by the initial rain, so as to reduce the hydraulic pollution caused by the confluence overflow and the overflow pollution of the urban integrated pipe network.
基于雨水管理模型和非支配排序遗传算法的蓄水池降合流控制溢流污染的水力优化仿真——ⅱ
建立了动态模拟降雨径流的暴雨水管理模型(SWMM),并利用SWMM的内部径流分量模拟各流域的降雨运行,包括降雨径流和冲刷污染负荷。然后,利用SWMM中的路由组件,通过管道和其他设施计算进入储罐系统的径流的特性,以获得最佳储罐容积。建立了耦合优化模型,利用多元线性回归方法建立了库容、总径流量和总成本的代数函数,并将其转化为以总径流量和总成本最小为目标的优化模型。NSGA-II通过使用反向学习机制得到改进。通过求解优化模型,得到了代理模型的非主导解。将非优势解代入SWMM,分析了优化结果的合理性。实验结果表明,该方法确定的水库容积能够有效地接受初雨带来的污染物,从而减少汇流溢流造成的水力污染和城市综合管网溢流污染。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.10
自引率
21.10%
发文量
0
审稿时长
20 weeks
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