Water Online

MAR 2017

Water Innovations gives Water and Wastewater Engineers and end-users a venue to find project solutions and source valuable product information. We aim to educate the engineering and operations community on important issues and trends.

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Page 20 of 34

utilities have to be "MAD" to be smart. As they explain, M is for MEASURE, because we have to focus on having good measurements in the right place; A is for ANALYTICS, because we have to understand and analyze the data we collect; and D pertains to the DECISION-making process. Using what we know to make good decisions can be an automated process in some cases. It can be helpful to split Big Data into these three parts. In terms of accuracy, the instrumentation that we have now is better than ever, whereas sensors were a weak point in the past. People generally understand the need to clean and calibrate instruments, but it can still be an important starting point. Improved analytics are more the focus today, with the benefits and needs explained above. Decisions will be the next focus, and fairly soon, as evidenced by research now underway. Smart analytics — called Smart Integrated Infrastructure (SII) at my company — have been applied to power stations for many years. In SII, a driving question is "How efficient is the plant as a whole?" With the ability to zoom in on specific pieces and ask questions such as "How many dollars per hour does it cost us not to have this part of the plant operating as well as it could?" utilities and cities can use smart analytics to make smarter decisions by proactively identifying and prioritizing improvements. Black & Veatch has developed tools specifically for combined heat and power (CHP), membranes, and activated sludge. We are working with the city of Lawrence, KS, to refine tools to enable the city's plant managers to optimize operations. Initially these tools will be used at the wastewater treatment plant, but eventually they will also be extended to the city's water treatment facility. Plant operators are already seeing the benefit of being able to visualize information by pulling all operations data together in a consolidated database. Big Data Basics Despite the current focus for many on improving analytics and/ or decisions, there's also a lot to be said for making sure our foundations are sound. Below are five keys to making Big Data work and avoiding the pitfalls of bad data. Focus on data quality rather than quantity. Not even the most sophisticated analytics can overcome measurement errors, whether that's noise, drift, or interferences. If you aren't confident in your primary sensors and analyzers, you could have a lot of bad data that is worthless, no matter what you do with it. For example, a Water Environment Research Foundation (now the Water Environment & Reuse Foundation) decision support system (DSS) project required a research team member to perform data analytics to pick out anomalies that might indicate toxins in plant influent, but distinguishing anomalies due to toxins from anomalies due to measurement problems proved to be a major hurdle. Confidence in sensors and analyzers can be gained by: 1. Cleaning them. Wastewater treatment is an especially fouling environment and not the best place to put scientific equipment. Operators frequently underestimate how quickly sensors become fouled. Go for autocleaning whenever possible and avoid installing anything in raw sewage or primary effluent unless you really need the measurement because both areas are particularly prone to fouling. Mixed liquor is an easier place to take measurements, and final effluent is the easiest place of all. Water treatment systems usually are less fouling, but sensors still need periodic cleaning. 2. Calibrating them. This is generally understood, although the frequency of calibration, particularly for sensors that tend to drift, typically is shorter than ideal. 3. Validating them. This may be the action overlooked by most instrumentation suppliers. Analytics to validate the measurements, particularly during calibration, frequently need more attention. 18 wateronline.com n Water Innovations DATAMANAGEMENT In Lawrence, KS, where the Black & Veatch ASSET360 system is used to track operating costs, data shows the impacts of wet-weather events on treatment costs. The total cost per 1,000 gallons is shown in purple, the secondary treatment flow rate is shown in blue, and the flow rate to the Actiflo system is shown in green. There is a jump in treatment costs during a storm event due to the extra costs associated with operating the Actiflo process.

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