Modelling and flow conditioning to manage discolouration in trunk mains

This paper presents predictive discolouration modelling and subsequent field trial results for a cast iron trunk main network. This enabled a UK water company to propose an ‘operational flow conditioning’ maintenance plan that reduces discolouration risk, improves network resilience and asset condition and yet does not require the trunk main to be decommissioned for invasive cleaning. This represents substantial time and cost benefits. Pre-and-post trial turbidity monitoring data is also presented which identified a daily flux of material, a factor in the regeneration of material layers that have been shown to cause discolouration when mobilised. Additional data detecting the occurrence of pressure transients is also presented, a possible cause of contaminant ingress and asset failure

Impact of hydraulic interventions on chronic and acute material loading and discolouration risk in drinking water distribution systems

This paper presents results from an intensive long term investigation in three comparable trunk mains and downstream impact of non-invasive, in-service flow conditioning to manage discolouration risk. Findings show that flow conditioning, the careful regular increase in flows to mobilise small amounts of material from cohesive layers formed at the pipe wall, provides immediate risk mitigation and system resilience benefits. Evidence is presented showing longer term risk reduction in the trunk mains and a 25% discolouration risk reduction in the downstream networks. Whilst the flow conditioning produced an acute but short duration controlled mobilisation of material from the trunk main, longer term downstream monitoring showed reduced chronic or background material loading. It is proposed this change is due to altering the material exchange behaviour and volumes bound within cohesive layers that develop on bulk water/infrastructure interfaces. The paper provides evidence that flow conditioning is an efficient strategy to manage discolouration risk and improve consumer water quality throughout water distribution systems

Operational management of trunk main discolouration risk

Despite significant on-going investment, water companies continue to receive an unacceptable number of discolouration related customer contacts. In this paper, data from intensive distribution system turbidity monitoring and cluster analysis of discolouration customer contacts indicate that a significant proportion of these contacts are due to material mobilising from the trunk main system, and operational flow increases are shown to have a higher discolouration risk than burst incidents. A trunk main discolouration incident highlighting this risk is discussed, demonstrating the need for pro-active trunk main risk assessments. To identify the source of the material event flow rates were modelled using the PODDS (prediction of discolouration in distribution systems) discolouration model. Best practice pro-active management is demonstrated in a case study where the PODDS model is used to implement managed incremental flow changes on a main with known discolouration risk with no discolouration impact to customers and significant cost savings

Multivariate data mining for estimating the rate of discolouration material accumulation in drinking water distribution systems

Particulate material accumulates over time as cohesive layers on internal pipeline surfaces in water distribution systems (WDS). When mobilised, this material can cause discolouration. This paper explores factors expected to be involved in this accumulation process. Two complementary machine learning methodologies are applied to significant amounts of real world field data from both a qualitative and a quantitative perspective. First, Kohonen self-organising maps were used for integrative and interpretative multivariate data mining of potential factors affecting accumulation. Second, evolutionary polynomial regression (EPR), a hybrid data-driven technique, was applied that combines genetic algorithms with numerical regression for developing easily interpretable mathematical model expressions. EPR was used to explore producing novel simple expressions to highlight important accumulation factors. Three case studies are presented: UK national and two Dutch local studies. The results highlight bulk water iron concentration, pipe material and looped network areas as key descriptive parameters for the UK study. At the local level, a significantly increased third data set allowed K-fold cross validation. The mean cross validation coefficient of determination was 0.945 for training data and 0.930 for testing data for an equation utilising amount of material mobilised and soil temperature for estimating daily regeneration rate. The approach shows promise for developing transferable expressions usable for pro-active WDS management

Predicting turbidity in water distribution trunk mains using nonlinear autoregressive exogenous artificial neural networks

A nonlinear autoregressive exogenous artificial neural network model was developed to predict turbidity response in two different trunk mains with measured flow and turbidity data. Models were initially established to prepare the data and automatically select the appropriate events for model training. Then, an autoregressive exogenous network model was developed and applied to predict turbidity responses based on past events in the time series. A per site continual data driven calculation of turbidity event risk was included as an additional input to capture the effect of temporal distance between the selected events as well as increasing the accuracy of the predictions. The calculated normalised mean square error and mean absolute error showed that the developed model combined with the data preparation and pre- processing models provides good regressions on a future event with a period of 7 to 10 hours for a multi-step ahead prediction. Furthermore, the result of the autoregressive exogenous network was compared with the output of a feed-forward network where the former significantly outperformed the latter (R value of approximately 0.97 compared to 0.66)