Water & Wastewater Treatment

22 | december 2014 | WWT | www.wwtonline.co.uk duction costs and keep down costs by maximis- ing the use of the cheapest treated water sources. Similarly, Thames Water has undertaken an ex- tensive pilot project making use of real time data, coupled with statistical models to ensure the most resilient and lowest cost water is produced and supplied into the water grid. Measuring asset condition: asset component data which measures vibration, heat or power consumption can be used to measure the asset condition allowing proper condition-based monitoring. Taking that further allows us to un- derstand what operating factors create increased stress on components, impacting on failure rates. By avoiding these periods we can understand how to use them effectively. Predictive weather data: wastewater transport and treatment is heavily impacted by weather. It can make unusual and seemingly unpredictable changes to how a network or treatment plant behaves. In September 2014, we saw unusually low wastewater flows across the UK, which led to some interesting impacts like reduced carbon loads being received at treatment plants. Using predictive weather data, with flow and inlet flow data we can set up changes to operating regimes which help manage risks caused by environmental impacts. Energy management is a key area for big data. Energy monitoring can be used to help understand and quickly intervene when energy consumption at plant or component level Digging deeper Riding the data revolution A s an engineer, data is something I crave, as it helps me get better at what I do. But it's not just me – there's a global data revolution hap- pening around us, and every day we hear phrases such as "Internet of Things", "Big Data", "Predictive Analytics" and "The Cloud" and are told that those who take advantage of these data revolutions will become stronger in the business world. So what is this major shi' that is getting everyone so excited? Rohit Banerji, leading Data Analyst at Accenture, says the rapidly emerging cloud-based technologies have disrupted the way information is managed. Our new capabili- ties to efficiently source, integrate, and process information at scale means less capital-hungry systems integration investment. Plus, computing power has reached the tip- ping point, storage is no longer an issue and the ability to extract data in real time, using wireless technology, makes for exciting new possibilities. So the big questions for me are, how can this new landscape help address the issues in the water industry, today and tomorrow? We already collect huge swathes of data, across networks, treatment plants, customers and catchments. Most treatment plants have SCADA systems which retrieve information on plant operation and SAP systems to collect maintenance and financial records. The problem is this data is isolated and sometimes we have multiple versions, making it hard to handle. Big Data approaches now mean we can use this information for: Predicting pipe failure: Using historical failure, pressure, material, age and condition data, we have been able to develop predictive models showing where future pipe bursts are likely to happen, enabling replacement works to be made prior to any occurrence, reducing undetected leakage and reducing the cost to serve (planned maintenance is always cheaper than reactive). Managing water costs: The Australian water- stressed city of Adelaide uses real time water network and consumption data to manage pro- Technological advancements mean we can now access, store, analyse and use vastly more data than was previously thought possible. How can the water industry benefit from this data revolution? AjAy NAir Technical direcTor, mWh

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