How Monitoring Treatment Processes Drives Efficiency And Confidence
By Amanda Tyndall
Climate change, consumer confidence, and emerging contaminants add to the challenges of water treatment. Rising costs of improved treatment, including both equipment and chemicals, make it even more important for plants to operate efficiently. How do you know if treatment is effective or when is best to replace or maintain equipment? New advancements in sensors, instrumentation, and digital enablement are providing more data. There are countless benefits of more data for better asset management, leak detection, and treatment control. Additionally, artificial intelligence is emerging with hopes of not only alerting but also predicting performance. Yet, even with the potential for more data, faster data, and predictive data, it remains true that the best data are data that bring confidence in treatment and compliance.
Data-Driven Results
A water utility in the Western U.S. treats groundwater and surface water across three facilities. All three facilities have highly variable seasonal demands and limited operational space, posing challenges related to treatment decisions across seasons or following events that impact water quality. One out of the three plants is conventional, using coagulation and filtration. It has a throughput capacity of 10 million gallons per day (MGD), with average summer flows of 5 MGD and 2.5 MGD in the winter. To better understand and optimize coagulant dosing, the facility decided to investigate how water quality data could be used to generate predictive algorithms for better control and deeper insights. Ultimately, the plant wanted its operators to feel confident to adjust treatment using data-driven decisions. This would lead to more efficient treatment and better environmental protection.
With large seasonal swings in temperature, flows, and water quality, the facility requires accurate data to drive decision-making. Using water quality data from parameters such as alkalinity, total organic carbon (TOC), and turbidity, the facility can ensure effluent compliance and confidence in processes. For example, when spring runoff starts, turbidity tends to spike, then return to base levels, but TOC spikes and remains high. At these points, the plant needs to decide whether to use its surface water or groundwater wells. By using TOC data, this decision is made quickly and with confidence. This confidence from the operators also ensures consumer confidence by providing compliant and quality water year-round, even in the case of sudden changes in source water from storms or seasonal changes.
Treatment includes the use of a variety of chemicals at each step of the process for pre-oxidation and coagulation, as well as a coagulant aid and polymer for prefiltration. These treatment steps maintain turbidity requirements per the plant’s original design, while the addition of TOC analysis confirms disinfectant/ disinfection byproduct rules (DBPRs) compliance. In the past, the plant conducted various jar tests to determine proper coagulant dosing. This placed extra pressure on operators and did not provide real-time information on raw or finished water quality. Moving to online analysis provided real-time data to immediately react to changes in source water and treatment performance. One example action is using TOC levels in the effluent to prompt a change in operations. When TOC increases during runoff, that signals the plant to reduce production and rely on groundwater sources.
Data were collected over the course of several years using raw and finished water quality. The data were used to optimize primary coagulant dose and identify correlations that would help predict performance. Results indicated a strong linear correlation between raw TOC and coagulant requirements. Weaker correlations were determined between alkalinity and turbidity with coagulant. Later studies improved the regression equations to solely relate dosage to incoming TOC. The plant still uses the regression that associates TOC, turbidity, and alkalinity. The variability with turbidity and alkalinity assists with coagulant dose adjustments during rapidly changing water quality events like rapid snowmelt and rainstorms. While the plant is not equipped for 100% automation, these water quality data allow operators to adjust using data-driven decisions.
Intakes are located on a river, so raw water quality can change drastically from hour to hour and requires multiple coagulant dose changes each day. Using raw TOC, alkalinity, and turbidity, the drinking water facility has been able to accurately adjust chemical doses for late summer turbidity spikes caused by rainstorms, as well as seasonal TOC and alkalinity changes from mountain runoff. Using data to drive decisions and confirm compliance enables more efficient treatment and ultimately better human and environmental protection. TOC analysis guides seasonal operations and delivers confidence in coagulant dosing decisions at this plant.
Conclusion
Drinking water plants must meet local and federal regulatory requirements, and they must now do so with emerging contaminants and variable source water quality requirements. Rivers, lakes, and aquifers used to be more predictable, but due to storm events and water scarcity, it is now more challenging for municipal treatment plants to achieve compliance. Ultimately, water quality monitoring can help utilities understand the changes in source water quality and what treatments are needed to adjust to the changes. The most useful data are often obtained when operators can use instrumentation data on water quality to not only optimize treatment and save costs but also to have confidence that they are making profitable decisions and staying in compliance.
About The Author
Amanda Tyndall is the vertical market manager for Industrial & environmental markets at Veolia Water Technologies & Solutions, focusing on the Sievers product line of analytical instruments. With 10 years’ experience in the water industry, Amanda and her team partner with industries and municipalities to solve water quality challenges through instrumentation solutions for ultrapure water to wastewater. Her background is in chemical engineering, with a bachelor’s degree from Vanderbilt University and a master’s degree from the University of Cambridge.