Network-wide sewer odour and corrosion management by model predictive control

通过模型预测控制进行全网络下水道气味和腐蚀管理

• 批准号: LP160101040
• 负责人: Dr Keshab Sharma
• 金额: $ 30.58万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Linkage Projects
• 财政年份: 2017
• 资助国家: 澳大利亚
• 起止时间: 2017-09-16 至 2022-06-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/LP160101040
• 关键词: Network; wide; sewer; odour; corrosion

Network-wide sewer odour and corrosion management by model predictive control. This project aims to develop and demonstrate, through real-life field studies, a model predictive control approach that achieves cost-effective network-wide mitigation of hydrogen sulphide. The lack of suitable methodologies to support the control designs of chemical dosing units and sewage pumping stations makes network-wide sewer corrosion and odour management a problem. Innovative control methodology will simultaneously manipulate chemical dosing unit(s) and selected sewage pumping station(s), based on real-time prediction of sewage flows and characteristics both at sources and across the network, to ensure optimal delivery of dosed chemicals to mitigate hydrogen sulphide.

通过模型预测控制，网络范围内的下水道气味和腐蚀管理。该项目旨在通过现实生活中的现场研究开发和证明一种模型预测控制方法，该方法可实现硫化氢的成本效益范围的缓解范围。缺乏支持化学剂量单元和污水泵站的控制设计的合适方法，使网络范围内的下水道腐蚀和气味管理成为问题。创新的控制方法学将基于对污水流和跨网络的污水流和特征的实时预测，同时操纵化学剂量剂量单元和选定的污水泵泵站硫化物。