Water & Wastewater Treatment

In the know Digging deeper: data 28 | JANUARY 2016 | WWT | www.wwtonline.co.uk relationships between the seemingly unrelated data sets. The key is in understanding how data sources can be meaningfully linked. There are already a number of good examples where such solutions have been successfully deployed. For example, an Australian water utility needed to improve water management and availability. It invested in a real- time demand and response system comprising of a hydrodynamic model to predict water production, demand and planning against targeted and actual usage. This incorporated weather data to predict water demand and usage to zone level. It also created 'what if ' scenarios based on predictive modelling. In a country known for extreme hot temperatures and limited rainfall that can hamper water supply, this use of limited, but targeted, data has ensured that water resources have been available through testing climatic conditions. It also contributed to delivering reduced energy costs by limiting the unnecessary operation of resources at certain periods. The result is more predictability of supply and cost for both the water utility and the end customer. Another utility required a complete solution to optimise its inspection and maintenance plans, and provide condition monitoring of the supply delivery network to predict failures and reduce cost. It deployed a web-based real-time dashboard to remotely monitor 100+ key points on the network, along with managing field service personnel, using a hand-held mobile solution for field service personnel to enter and upload data while on route inspections. This has been based on limited datasets (e.g. load, weather etc.), and amongst other benefits saved an estimated £20m in infrastructure costs. Similar techniques can be used for work management, as where a UK water utility used an automated data management and operational predictive analytics tool for improved workload and resource planning. This was based on the creation of a near real-time Operational Data Store (ODS) collating operational and business data sets for use in reporting and forecasting. This solution delivered a 20% increase in planned work completion leading to improvement in customer satisfaction and Service Incentive Mechanism (SIM) SIM, backlog reduced by up to 95%, a 10% productivity improvement, and increased accuracy and predictability. Similar techniques have been used to look at infrastructure interdependencies. The UK Infrastructure Transitions Research Consortium (www.itrc.org.uk) has developed a new generation of infrastructure system simulation models and tools to inform the analysis, planning and design of national infrastructure. The National Infrastructure Model (NISMOD) simulations provide new methods for analysing performance, risks and interdependencies, and should prove very helpful for cross-infrastructure planning (the official launch of the models took place at the IRTC conference in October 2015). It is interesting to note however that, even for these higher level models, the developers needed to work closely with the various organisations involved to build suitable datasets, as there was not sufficient data available in the public domain at the outset. The ability to share and expose data for the purpose of modelling is another consideration for the industry, which will need to balance confidentiality and competitive advantage with the benefits that wide-scale Big Data analytics and modelling can offer. As can be seen from above, large business information systems may be of limited value to utilities in terms of managing their assets. Of more value is the effective and consistent linking of dispersed data sources, coupled with an easily configurable analytics engine. Such tools have already been used to answer many asset related questions, such as the viability of rainwater harvesting in differing regions and climates. It is indeed possible to answer a high percentage of the work and asset management related questions posed by utilities, even with the limited asset data many hold. A few examples include: • Reducing pollution events through effective use of data from the level sensors • Production planning across areas and regions, based on telemetry and climate data • Reducing blockage and related Other Cause flooding • Tracking leakage • Reducing energy use • Improving compliance monitoring at small Treatment Works Each question is however individual to the specific situation, so only those who are able to understand both the engineering and system elements will be able to successfully deliver benefi- cial results. For insights and show news visit utilityweeklive.co.uk INNOVATION CONTENT STREAM: For insights and show news visit utilityweeklive.co.uk DATA & ANALYTICS CONTENT STREAM: For insights visit utilityweeklive.co.uk For insights visit utilityweeklive.co.uk

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