Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.548380
Title: Improving the operation and maintenance of CSO structures
Author: Guo, Nan
ISNI:       0000 0004 2716 3598
Awarding Body: University of Sheffield
Current Institution: University of Sheffield
Date of Award: 2012
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Abstract:
Combined Sewer Overflow (CSO) structures are commonly used in combined sewer systems and serve as "safety valves" for the pipe system in that they act as a hydraulic control to prevent an overload of the sewer system to prevent surcharge and flooding. They also act to retain the pollution within the sewer system and to retain such pollution, particularly aesthetic solids it has been common practice to incorporate screens into CSO chambers. However, the UK water industry is faced with an insufficient understanding of the way in which these assets perform and of the way in which they may best be managed. To better understand such performance the UK industry has installed a large number of monitoring systems that provide data on the hydraulic performance of the CSO chambers and CSO chambers with screens. This data is currently being used to develop simulation tools with a view to better understanding and providing a more efficient operational strategy, especially in respect of the frequency of maintenance visits. The main objective of this research is to develop and validate novel mathematical techniques based on this hydraulic performance data to simulate, predict and provide a decision support system for CSO asset operation and maintenance. To achieve this objective, three steps were completed. Firstly, data was collected on the types of structure in common use (both CSO's and screens), their monitored hydraulic performance (chamber water depth), rainfall information and their maintenance requirements (number of pro-active and reactive visits and associated costs). Secondly to use this data to develop and validate a mathematical model, using artificial intelligence techniques in the form of an adaptive linear neural network approach, to predict the hydraulic performance of chambers, which installed with different types of screens in response to rainfall. Thirdly, based on predicted CSO hydraulic performance to utilise a fuzzy logic approach to describe the operational and pro-active maintenance requirements of the different types of CSO structures and screen arrangements. The models were successfully developed using data from one catchment and subsequently applied to a second catchment, again successfully, to test their validity and transferability. The final section of the thesis attempts to describe how the methodologies developed may be incorporated into industry standard and practical CSO asset management.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.548380  DOI: Not available
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