Water & Wastewater Treatment

WWT June 2017

Water & Wastewater Treatment Magazine

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Page 15 of 47

16 | june 2017 | WWT | www.wwtonline.co.uk James Brockett ediTor WaTer & WasTeWaTer TreaTmenT Data scientists at Welsh Water have built an analysis tool that can predict when service reservoirs are at risk of bacterial water quality compliance failures, and shaped it into an easy-to-use dashboard for its operational staff. The Service Reservoir Bacterial Compliance Model uses data from all the factors which can contribute to water quality failures at service reservoirs – such as the level of chlorine in the water, the bacterial colony count from samples, temperature and asset condition – and calculates the risk of failure from this. This is then expressed as a single, colour coded figure that gives a spur to action for workers at the utility company to carry out remedial action and investigations to Data ● The large amount of data available to water companies on their operations is not always harnessed in a way that helps operational staff to take action ● Corporate legacy systems can store data in a way that is difficult to analyse and does not map across into more modern so ware tools ● In order to improve performance, data must help the business move from being reactive to proactive. Predictive models and machine learning can help facilitate this The Works protect against such a failure occurring. Bacterial compliance failures are caused by the detection of bacteria such as E.coli or coliforms in the regular sampling regime overseen by the DWI. Such incidents usually mean that a service reservoir must be drained in order to investigate the cause, leading to significant costs to the water company, reputational damage and potential disruption to customers' water supply. The predictive model can help Welsh Water reduce the risk of non-compliance by prompting the workforce to carry out proactive maintenance, raise the level of disinfection or undertake other interventions. Dwr Cymru Welsh Water has a data strategy programme called WISER (Welsh Water Information Strategy Enterprise Roadmap) which supports the predictive model and other such data initiatives acorss the business. This strategy (see column, right) provides an effective data governance framework that orchestrates people, process and technology to enable the leveraging of data as an enterprise asset. A huge amount of data is available to water companies on their operations, and increasingly, the challenge for utilities is how to bring this data to the attention of time-pressed operational staff in the most timely, succinct and digestible fashion so they can act upon it. If used well, today's data tools have the power to not only give information about what is happening and to analyse it for patterns, but also to use these patterns to predict the future and to prescribe actions to improve matters – this use of data is integral to the modern vision of a 'smart network' for water. For this reason, data has been described as water industry's biggest resource, with much attention being paid to the best way to harness this resource to the benefit of customers, employees and companies. Further reading on this topic is available at wwtonline.co.uk • CHALLENGE

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