机理和数据混合驱动的排水系统控制模型构建方法

王一茗; 马振华; 杨萌祺; 董欣; 曾思育

doi:10.13205/j.hjgc.202206026

机理和数据混合驱动的排水系统控制模型构建方法

doi: 10.13205/j.hjgc.202206026

王一茗¹,
马振华²,
杨萌祺¹,
董欣^1,3, ,,
曾思育^1,3

1. 清华大学环境学院, 北京 100084;
2. 昆明滇池投资有限责任公司, 昆明 650228;
3. 清华大学环境模拟与污染控制国家重点联合实验室, 北京 100084

基金项目:

国家自然科学基金项目(51778327)

详细信息

作者简介:
王一茗(1995-),男,研究生,主要研究方向为城市排水系统实时控制。wangym18@mails.tsinghua.edu.cn

通讯作者:
董欣(1982-),女,副教授,主要研究方向为城市水系统集成模拟、城市排水系统实时控制等。dongxin@tsinghua.edu.cn

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出版历程
- 收稿日期: 2021-12-14
- 网络出版日期: 2022-09-01
- 刊出日期: 2022-09-01

A HYBRID MODELING STRATEGY FOR CONTROL SIMULATOR OF URBAN DRAINAGE SYSTEMS BASED ON DATA-DRIVEN AND MECHANISM-DRIVEN METHOD

1. School of Environment, Tsinghua University, Beijing 100084, China;
2. Kunming Dianchi Investment Co., Ltd, Kunming 650228, China;
3. State Key Joint Laboratory of Environment Simulation and Pollution Control, Tsinghua University, Beijing 100084, China

摘要

摘要: 针对城市排水系统的优化控制,提出一种机理和数据混合驱动的控制模型构建方法。该方法根据与控制目标的相关程度划分排水系统,并利用长短时记忆神经网络模型和圣维南方程刻画不同分区,可为系统的模型预测控制提供工具支撑。为验证方法的有效性,选取A市某污水处理厂服务片区为研究对象,运用所提出方法,基于机理模型构建简化控制模型,并在实际降雨下将该模型与水库模型和纯数据驱动模型进行对比。结果表明:所构建的控制模型在2个溢流口的模拟准确性相对水库模型提升了3.85%和22.86%,相对纯数据驱动模型提升了5.66%和3.57%(以均方根误差的均值计);模拟效率较机理模型提高了98.7%。该控制模型构建方法可在一定程度上平衡模拟准确性和模拟效率的矛盾,从而可更好地支撑城市排水系统的优化运行。研究结果可为实施排水系统实时控制提供参考。
- 溢流污染 /
- 排水系统 /
- 模型预测控制 /
- 控制模型 /
- 模型简化
Abstract: A hybrid control modelling method was proposed for optimal control of urban drainage systems,which adopted both data-driven and mechanism-driven simplification strategies.The method divided urban drainage systems into different regions,according to connected degree to the control target.These regions were modeled by LSTM model and Saint Venant equation separately.The method was verified within the service area of a wastewater treatment plant in City A in China.The proposed method established a surrogate control model based on a detailed hydraulic model.Compared with tank model,simulation accuracy in two CSO outfalls was improved by 3.85% and 22.86%,and improved by 5.66% and 3.57% compared to the LSTM model (measured in an average root mean value).The simulation time could be reduced by 98.7% in comparison to the detailed hydraulic model.Due to the advantage in accuracy and efficiency,the modelling strategy can provide references for the implementation of real-time control in urban drainage systems.
- combined sewer overflows /
- urban drainage systems /
- model predictive control /
- control model /
- model simplification

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参考文献(13)

[1]	徐智伟.基于强化学习的城市排水系统实时控制策略研究[D].北京:清华大学,2021.
[2]	CEMBRANO G, QUEVEDO J, SALAMERO M, et al. Optimal control of urban drainage systems:a case study[J]. Control Engineering Practice, 2004, 12(1):1-9.
[3]	PLEAU M, COLAS H, LAVALLÉE P, et al. Global optimal real-time control of the Quebec urban drainage system[J]. Environmental Modelling&Software, 2005, 20(4):401-413.
[4]	VEZZARO L, CHRISTENSEN M L, THIRSING C, et al. Water quality-based real time control of integrated urban drainage systems:a preliminary study from Copenhagen, Denmark[J]. Procedia Engineering, 2014, 70:1707-1716.
[5]	MYO L N, RUTTEN M, TIAN X. Flood mitigation through the optimal operation of a multi-reservoir system by using model predictive control:a case study in Myanmar[J]. Water, 2018, 10(10):1371.
[6]	SUN C C, ROMERO L, JOSEPH-DURAN B, et al. Integrated pollution-based real-time control of sanitation systems[J]. Journal of Environmental Management, 2020, 269:110798.
[7]	席裕庚,李德伟,林姝.模型预测控制:现状与挑战[J].自动化学报,2013,39(3):222-236.
[8]	SADLER J M, GOODALL J L, BEHL M, et al. Leveraging open source software and parallel computing for model predictive control of urban drainage systems using EPA-SWMM5[J]. Environmental Modelling and Software, 2019, 120:104484.
[9]	LEITAO J P, SIMÕES N E, MAKSIMOVIĆ Č, et al. Real-time forecasting urban drainage models:full or simplified networks?[J]. Water Science and Technology, 2010, 62(9):2106-2114.
[10]	黄森辰.面向溢流污染削减的城市排水系统集成分层优化控制研究[D].北京:清华大学,2018.
[11]	ZHANG D, MARTINEZ N, LINDHOLM G, et al. Manage sewer in-line storage control using hydraulic model and recurrent neural network[J]. Water Resources Management, 2018, 32(6):2079-2098.
[12]	冯钧,潘飞.一种LSTM-BP多模型组合水文预报方法[J].计算机与现代化,2018(7):82-85.
[13]	殷兆凯,廖卫红,王若佳,等.基于长短时记忆神经网络(LSTM)的降雨径流模拟及预报[J].南水北调与水利科技,2019,17(6):1-9 ,27.