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.

通过模型预测控制的全网下水道气味和腐蚀管理。该项目旨在通过实际实地研究开发和演示一种模型预测控制方法，以实现具有成本效益的全网硫化氢缓解。缺乏适当的方法来支持化学加药装置和污水泵站的控制设计，这使得整个网络的下水道腐蚀和气味管理成为一个问题。创新的控制方法将根据对污染源和整个网络的污水流量和特征的实时预测，同时操作化学加药装置(S)和选定的污水泵站(S)，以确保加药化学品的最佳输送，以缓解硫化氢。