Iron and manganese accumulation potential in water distribution networks
, PhD thesis, University of Salford.
The occurrence of discoloured drinking water at customers’ taps, which is mainly caused by the deposition and release of iron (Fe) and manganese (Mn) in water distribution networks (WDNs), is a major concern for both customers and water companies. Increased concentrations of Fe and Mn in WDNs can lead to penalisation by the Drinking Water Inspectorate (DWI) and Water Services Regulation Authority in England and Wales (Ofwat). These high concentration levels can cause aesthetic problems such as giving water an unpleasant metallic taste and staining of laundry. It has also been found that increased Mn concentrations in drinking water can reduce intellectual function of children.
Despite efforts by water companies to comply with standards for drinking water, they continue to receive customer complaints related to water discolouration. Currently, most water companies identify high-discolouration-risk regions in WDNs by either selecting areas in the network with high concentrations of Fe and Mn from their routine sampling, or using data obtained from customer complaints related to discolouration. However, these risk assessment methods are imprecise, because only few selected nodes are sampled and not all customers who experience water discolouration complain. Moreover, considering that the water mains in England and Wales span approximately 315,000 km, monitoring Fe and Mn concentrations will always be a difficult and expensive task. It is therefore imperative for water companies to gain a practical understanding of the processes and mechanisms that lead to water discolouration, and to develop a model to identify the high-risk areas in WDNs so that remedial measures can be effectively implemented.
The factors that influence Fe and Mn accumulation from post-treatment to customers’ taps through WDNs can be categorised into physical, chemical and biological. However, to date, researchers have only studied these factors partially or separately, but never in combination. None of the current models are able to predict discolouration/Fe and Mn accumulation potential for every node in WSZs using chemical, biological, and hydraulic/physical variables. This study took a holistic approach in investigating these factors. A five-year data set comprising of 36 water quality, hydraulic, and pipe-related variables covering 176 different district metered areas (DMAs) were analysed to identify relevant variables that influence Fe and Mn accumulation potential. Customer complaint data were also investigated for seasonal trends. Majority of the DMAs (67.44%) showed significant peaks in customer complaints during summer. These spikes may be attributed to increased water consumption and warmer water temperatures during this period. An artificial neural network (ANN) model was developed using relevant variables identified through the data analysis. The model could predict Fe and Mn accumulation potential values for every node in a given water supply zone (WSZ). From the risk maps generated by the ANN model, it was observed that most of the regions in the network with high Fe and Mn accumulation potential also had high levels of customer complaints related to discolouration. Although the ANN model could predict Fe and Mn accumulation potential failures in WSZs, its black-box nature made it difficult to explain the causes of the failures, unless they were manually investigated.
To overcome the limitation in the ANN model, a fuzzy inference system (FIS) was developed to predict Fe and Mn accumulation potential for every node in WDNs and also capture the chemical, biological and physical processes as water travels through the network. The rules and weights of the rules for the FIS were calibrated using a genetic algorithm. The FIS is also able to determine the causes of the Fe and Mn accumulation potential failures. The ability of the developed models in this research to predict and indicate the causes of high Fe and Mn accumulation potential at the node level make them a unique and practical tool to detect high risk nodes in all regions in WDNs, including regions which have not been sampled. Both models could be of great benefit to water resource engineers and drinking water supply companies in managing water discolouration. They could also be used to investigate variables that influence physical, chemical and biological processes in WDNs.
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