Water & Wastewater Treatment

In the know Technically speaking: smart networks engineers who then need to act quickly, also has to form part of the smart strategy. The data that is logged on a PRV or meter can be used to indicate the health of the water network asset. If the meter readings are erratic or at- lining, this could indicate a failing or a poorly sized meter. Smarter data gathering The tracking of the PRV pressures will give an indication of the behaviour of the PRV. The relationship between the ow through the PRV and the pressure across the valve could be used to make an assessment of the PRV health. For example when the PRV is oversized and becomes unstable at low ows, pressures can vary with little change in ow rate. There are loggers on the market that will now log and store data at 100 times a second. This data can easily spot pressure transients that might be masked by 15 minute data. This is, however, 90,000 logged points every 15 minutes, or 8.6million a day: an enormous amount compared to the standard 96 data points when data is reported at 15 minute intervals. Logger manufactures have already identi† ed this issue, and o‡ er numerous solutions. Bespoke loggers can be installed that log data for a period of days, that are then retrieved and the data analysed. Some in situ loggers can be programmed to do fast logging for short periods or fast logged data only stored on trigger events. One of the best compromises I believe is storing basic statistical data. If the sampling rate is 4 points every second, then it is possible to store one point to represent the 15min average and three extra points, the minimum (min), maximum (max) and standard deviation (SD). A little bit more data can indicate many more issues, as demonstrated in Figures 1 and 2. Each of the above data sets has the same 15 min average (±2%) so there would be no discernable di‡ erence looking at 15 minute data. However a high max but about normal SD will indicate a surge pressure, a large SD combined with extreme min/max will indicate transients, and slightly elevated recordings can indicate pressure variations. This requires 380 points a day, which is a manageable number and can trigger action or further investigation. Actionable intelligence In reality, what an engineer or a network operative wants to know is whether the network is working or not. If it is not, then what is wrong, and what has to be done to † x it? They do not want, or have time, to trawl through data to check. What is really needed is to quickly and easily assess whether the network is running as it should be or not. If a DMA is working † ne, then I do not need to be told. If there is an issue but it is not critical - a low battery in a logger or a small increase in expected ow – then this should be indicated, but not necessarily immediately. If I have a DMA with critical issues, such as a large burst, then I want to know now, and I do not want to have to search for the data. I want the system to tell me there is an issue, what that issue is likely to be and where it is. Engineers do not want more data; they want targeted intelligence that they can trust and act on. Conclusion When thinking about smart networks, don't start with the technology or with the question "what could we do?". Instead, stop and think about what it is that you are trying to achieve. Collecting data is easy - utilities are collecting lots of it - but making the step from data to actionable intelligence is where smart delivers real value. To make decisions on your network you need actionable intelligence, which as we have shown, means the right data, at the right time, with the right analysis. Fig 2: Using 15-minute pressure data can hide signifi cant variations. Potentially damaging transients (bottom) may be missed entirely 38 | JULY 2015 | WWT | www.wwtonline.co.uk Fig 3: A typical District Meter Area (DMA) with a meter and Pressure Regulating Valve (PRV). Pressure is measured at the Average Zone Point (AZP) and at the 'Critical Point', where pressure is lowest

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