基于动态剪枝的城市供水管网优化调度算法

李俊禹; 刘书明; 吴雪; 谢涛; 金晔

doi:10.13205/j.hjgc.202206029

基于动态剪枝的城市供水管网优化调度算法

doi: 10.13205/j.hjgc.202206029

李俊禹¹,
刘书明^1, ,,
吴雪¹,
谢涛²,
金晔³

1. 清华大学环境学院, 北京 100084;
2. 北京市自来水集团石景山区自来水有限责任公司, 北京 100043;
3. 北京市自来水集团有限责任公司, 北京 100031

基金项目:

基于西部地区地形特征的供水管网优化调度研究(20202001440)

详细信息

作者简介:
李俊禹(1993-),男,研究生,主要研究方向为供水管网优化调度。lijunyu16@mails.tsinghua.edu.cn

通讯作者:
刘书明(1976-),男,教授,主要研究方向为供水管网领域产销差管理、供水管网优化设计与管理等。shumingliu@tsinghua.edu.cn

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- 文章访问数: 333
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出版历程
- 收稿日期: 2022-02-07
- 网络出版日期: 2022-09-01
- 刊出日期: 2022-09-01

OPTIMIZATION OF URBAN WATER SUPPLY NETWORK BASED ON DYNAMIC PRUNING MODEL

LI Junyu¹,
LIU Shuming^{1
, ,},
WU Xue¹,
XIE Tao²,
JIN Ye³

1. School of Environment, Tsinghua University, Beijing 100084, China;
2. Beijing Waterworks Group Shijingshan District Co., Ltd, Beijing 100043, China;
3. Beijing Waterworks Group Co., Ltd, Beijing 100031, China

摘要

摘要: 城市供水管网的优化调度模型,需要反复调度水力学模型来计算目标函数和约束条件,从而产生巨大的计算和时间成本。在优化迭代过程中,为了获得更优秀的优化结果,通常在搜索空间中约束条件的边界进行搜索,产生了大量不满足约束的样本和较低的优化效率。为解决这一问题,提出了一种高效的基于约束条件的动态剪枝方法,在运行水力学模型前,利用优化计算过程中积累的数据来判断样本是否符合约束条件,从而剔除不满足约束的样本,提高优化效率。模型分别在1个案例管网和1个真实管网上进行测试,结果表明:使用朴素贝叶斯、决策树和支持向量机作为动态剪枝算法,在获得和原始优化模型几乎相同结果的同时,分别减少56.4%、58.5%和56.8%的计算次数。
- 供水管网 /
- 优化调度 /
- 剪枝 /
- 约束条件 /
- 分类器
Abstract: The optimal scheduling model for urban water supply networks often requires repeated scheduling of the hydraulics model to calculate the objective function and constraints,which leads to high computational and time cost.In the optimization iteration process,searching is usually performed at the boundaries of the constraints in the search space to obtain better optimization results,which leads to a large number of samples that do not satisfy the constraints and optimization efficiency.To solve this problem,an efficient constraint-based dynamic pruning method was proposed,which used the data accumulated during the optimization calculation to determine whether the samples met the constraints before running the hydraulics model,to eliminate the samples that do not satisfy the constraints and improve the optimization efficiency.The model was tested on a case network and a real network,and the results showed that the use of Naïve Bayes,decision trees,and support vector machines as dynamic pruning models reduced the number of computation by 56.4%,58.5%,and 56.8%,respectively,while obtaining the similar results as the original optimization model.
- water supply network /
- optimal scheduling /
- pruning /
- constraint /
- classifier

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参考文献(17)

[1]	PRICE E, OSTFELD A. Discrete pump scheduling and leakage control using linear programming for optimal operation of water distribution systems[J]. Journal of Hydraulic Engineering, 2014, 140(6):16.
[2]	BONVIN G, DEMASSEY S, LODI A. Pump scheduling in drinking water distribution networks with an LP/NLP-based branch and bound[J]. Optimization and Engineering, 2021.
[3]	田完红.供水系统中水泵调度方法的比较[J].中国水能及电气化, 2021(6):10-13,34.
[4]	叶露,郑雪婷.基于城市供水管网优化调度研究[J].建筑与预算, 2020(5):56-61.
[5]	LUNA T, RIBAU J, FIGUEIREDO D,et al. Improving energy efficiency in water supply systems with pump scheduling optimization[J]. Journal of Cleaner Production, 2019, 213:342-356.
[6]	ZHENG F F, et al. Comparison of the searching behavior of NSGA-Ⅱ, SAMODE, and Borg MOEAs applied to water distribution system design problems[J]. Journal of Water Resources Planning and Management, 2016, 142(7).
[7]	张聪,李文竹,刘心.基于ABC-PSO的园区供水泵组优化调度[J].电脑知识与技术, 2020, 16(9):270-273.
[8]	LOPEZ-IBANEZ M, PRASAD T D, PAECHTER B. Ant colony optimization for optimal control of pumps in water distribution networks[J]. Journal of Water Resources Planning and Management, 2008, 134(4):337-346.
[9]	BROAD D R, DANDY G C, MAIER H R. Water distribution system optimization using metamodels[J]. Journal of Water Resources Planning and Management-Asce, 2005, 131(3):172-180.
[10]	吴胜华,刘哲,游庆元,等.管网宏观模型的完善及节能减漏应用[J].城镇供水, 2014(2):14-19.
[11]	宫永伟,刘伟勋,于磊,等.合流制溢流控制系统的多目标优化[J].环境工程, 2020, 38(4):128-133.
[12]	周佳雯,陈盛达,李树平,等.供水增压泵站改造的优化计算[J].净水技术, 2020, 39(4):74-79.
[13]	陈之敏,李斌,龙志宏,等.基于贝叶斯理论的供水管道健康状态评价模型研究[J].给水排水, 2021, 57(4):137-143.
[14]	MOKHTAR E A, LAGGOUNE R, CHATEAUNEUF A. Utility-based maintenance optimization for complex water-distribution systems using bayesian networks[J]. Water Resources Management, 2016, 30(12):4153-4170.
[15]	陈京钰,肖诗云,冯新.一种供水管网泄漏区域定位的机器学习方法[J].中国给水排水, 2021, 37(7):58-65.
[16]	YANG Y Y, HU Y, ZHENG J C. A Decision tree approach to the risk evaluation of urban water distribution network pipes[J]. Safety, 2020, 6(3):36.
[17]	薛同来,赵冬晖,韩菲.基于GA优化的SVR水质预测模型研究[J].环境工程, 2020, 38(3):123-127.