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袁志国-Sewer management in a ditigal era.pdf

上传人: bu****ng 编号:714526 2025-06-18 28页 3.88MB

1、Professor Zhiguo Yuan AMChair Professor of Urban Water Management,Global STEM ProfessorDirector,JC STEM Lab of Sustainable Urban Water ManagementSchool of Energy and EnvironmentDigitalising sewer management21.1.Sewer operation Sewer operation ensures effective use of the existing hydraulic capacity

2、for safe wastewater conveyance,so that overflows and flooding are minimised,in both wet and dry weather.2.2.Sewer maintenanceSewer maintenance preserves the integrity and hydraulic capacity through sewer cleaning,pipe repair,equipment replacement/repair,and implementation of corrosion and odour cont

3、rol strategies.3.3.Sewer upgradingSewer upgrading extends/augments the hydraulic capacity,through capital works,to meet the predicted future needs in response to population/urban growth and climatic changes.Sewer management1.to minimise sewer overflow via real-time control of sewer flows.2.to quanti

4、fy,locate and predict sewer inflow and infiltration to support real-time sewer operation and capital program development.3.to detect/predict sewer sedimentation/blockage and equipment failures to enable proactive sewer maintenance.4.to enhance the capacity of the overall wastewater system through in

5、tegrated control of sewer networks and the wastewater treatment plants.5.to reduce fugitive emissions of hydrogen sulfide and methane,thus extending the asset life and reducing the carbon footprint of wastewater conveyance.Digital sewer management some key interlinked objectivesDIGITAL SEWER MANAGEM

6、ENTSEWER NETWORKDIGITAL TWINSAn overarching framework for digital An overarching framework for digital sewer managementsewer managementDigital twinsA digital twin is a virtual representation a virtual representation of a physical object,structure,or system that incorporates realincorporates real-tim

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本文主要探讨了数字化排水管理的框架与关键目标。核心内容包括:1. 排水管理的五大目标,如最小化溢流、量化流入和渗透、预测堵塞和设备故障等。2. 数字孪生技术——物理对象的虚拟表示,整合实时数据,用于监测和控制。3. 监测设计,包括接触式和非接触式传感器,以及软传感器技术。4. 数据分析,如使用ARX模型进行流量预测,以及量化污水、流入和渗透的贡献。5. 实时控制技术,如模型预测控制(MPC)和深度强化学习。文章强调,数字化水管理不仅是视觉效果,而是需要跨学科合作和系统框架的持续发展。 关键点: - 数字孪生技术用于实时监测与控制。 - 使用传感器和数据分析预测排水系统状态。 - 模型预测控制(MPC)减少了化学药剂消耗量,例如H2S(g)减少了62%,化学成本降低50%。 - 强调多学科合作和系统框架的必要性。
"数字化如何让污水更清洁?" "虚拟双胞胎如何助力污水管理?" "智能监测如何降低污水处理成本?"
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