Big data in the water industry: How does it provide big value?

Data Science   |   
Published April 7, 2020   |   

Water treatment is highly resource-intensive and typically relies on equipment that can be expensive or difficult to maintain due to the harsh conditions in water treatment plants. As a result, any opportunity to improve maintenance strategies or cut back on resource use can provide significant advantages.
At the same time, the rise of Industry 4.0 has enabled data collection at higher speeds and greater volumes than ever before. This is also known as “big data.” There is a wide range of applications for these massive data sets — like the ability to find new efficiencies, optimize processes and create more accurate forecasting and predictive models. All of these applications can be leveraged to provide big value for utilities and water treatment businesses.
Here are some of the most significant benefits that big data can offer the water industry.

What Is Big Data?

Big data refers to data that is too big for traditional methods to capture, store and analyze. The data is often collected at such a high rate and volume — such as a network of sensors pulling real-time data about nitrate concentration — that it can’t be analyzed in the same way as a less regularly updated or static data set.
Big data requires special tools to capture and analyze, but can also provide much finer detail and insights you wouldn’t be able to find with other methods of data collection and analysis.

Improving Monitoring Performance and Boosting Efficiency

Utilities and water companies are increasingly using remote sensor technology to monitor pumping stations and water storage facilities. More than half of all utilities already rely on some kind of data collection solution at pumping stations, according to data from Black & Veatch. The higher volume of data that these sensors provide can be used to improve performance, detect problems and optimize plant resource usage.
In one example, a utility was able to leverage big data analysis to optimize its use of granular activated carbon (GAC) filters. By tracking and analyzing organic carbon levels over time, the utility identified a relationship between total trihalomethane (TTHM) production and effluent DOC (dissolved organic carbon). The utility was able to use this information to identify when the GAC filter needed to be replaced in order to maintain compliance.
As a result of changing its maintenance schedules based on this new data, the utility was able to reduce “the annual replacement costs by $100,000 at each WTP by being able to replace the GAC just in time, before the effluent from the water treatment plants exceeded the utility’s internal standard for TTHMs,” according to the report.
Boosted efficiency can also improve the lifespan of critical equipment. With better data, it’s possible for water treatment plants to purify water using fewer chemical additives and effectively manage water characteristics such as pH. When plants are able to cut back on their use of these additives, they can reduce or slow the degradation of critical water purification equipment.
As a result, water purification businesses can save money twice — once on the purchase of these chemical additives and then again on equipment repairs down the line.

Enabling Predictive Analytic Strategies

Another significant benefit of big data is its ability to improve a business’s forecasting techniques. By increasing the amount of data available and drawing correlations between data sets, it’s possible to create forecasting models that yield more accurate and insightful predictions.
One application of big data that is becoming more common in the water industry is predictive maintenance strategies. IoT sensors attached to essential equipment track information like operating temperature, timing and vibration. Big data analytics is then used to find correlations in that data and predict when a machine is likely to fail and needs maintenance, or is operating in a range that may cause excessive wear and tear.
With a predictive maintenance strategy in place, it’s possible to extend the lifespan of equipment, make maintenance schedules more efficient and prevent costly downtime by giving site staff advanced notice of potential equipment failure. Some advanced predictive systems can even automatically shut down equipment that may be on the verge of failure, possibly preventing damage to the machine.
According to George Gsell, the president of water purification solution provider Meco, big-data-enabled early detection of mechanical issues can help businesses in the water industry “avoid more costly unplanned interruptions later and extend the operational life until maintenance can be scheduled.”
Big data can also combine in-house data with information from third-party sources to improve the quality of a business’s predictions and forecasting models. For example, water utilities in the city of Lawrence, Kansas, pull together data from water and wastewater plants as well as information from NOAA and the USGS. This data is combined into one master set, allowing advanced analysis that can detect patterns and predict inlet flow rates and allowing plant operators to make more informed operating decisions.
While the tech isn’t quite there yet, big data may soon also be able to help operators automate critical plant functions. In the future, it may be common for big-data-enabled systems to make decisions automatically, based on events like flooding which can occur after regular operating hours.

The Benefits That Big Data Can Offer the Water Industry

Better collection and application of data can provide big benefits for the water industry. Water treatment plants often struggle with aging or difficult-to-maintain equipment that must operate under harsh conditions, as well as significant resource usage. Big data can help with both.
Predictive maintenance strategies can leverage real-time operational data to predict when a machine or piece of equipment will need maintenance. With the advanced notice that these strategies can provide, businesses can avoid costly downtime or machine failure. Other predictive models enabled by big data also provide significant benefits.
Big data can also be used to reduce resource consumption. This may include monitoring certain aspects of water quality to know when a filter ought to be replaced.
In the future, as the rise of Industry 4.0 continues and data collection solutions become more widespread, water treatment companies will have even more options for collecting and analyzing large amounts of data. As a result, the technology may become even more valuable to the industry over time.