The Sustainable Water Initiative for Tomorrow (SWIFT) program is the effort of the Hampton Roads Sanitation District to implement indirect potable reuse to recharge the depleted Potomac Aquifer. This initiative is being demonstrated at the 1‐MGD SWIFT Research Center with a treatment train including coagulation/flocculation/sedimentation (floc/sed), ozonation, biofiltration (BAF), granular activated carbon (GAC) adsorption, and UV disinfection, followed by managed aquifer recharge. Bulk total organic carbon (TOC) removal occurred via multiple treatment barriers including, floc/sed (26% removal), ozone/BAF (30% removal), and adsorption by GAC. BAF acclimation was observed during the first months of plant operation which coincided with the establishment of biological nitrification and dissolved metal removal. Bromate formation during ozonation was efficiently controlled below 10 µg/L using preformed monochloramine and preoxidation with free chlorine. N‐nitrosodimethylamine (NDMA) was formed at an average concentration of 53 ng/L post‐ozonation and was removed >70% by the BAFs after several months of operation. Contaminants of emerging concern were removed by multiple treatment barriers including oxidation, biological degradation, and adsorption. The breakthrough of these contaminants and bulk TOC will likely determine the replacement interval of GAC. The ozone/BAC/GAC treatment process was shown to meet all defined treatment goals for managed aquifer recharge.
Practitioner points
Floc/sed, biofiltration, and GAC adsorption provide important barriers in carbon‐based treatment trains for bulk TOC and trace organic contaminant removal.
Biofilter acclimation was observed during the first three months of operation in each operating period as evidenced by the establishment of nitrification.
Bromate was effectively controlled during ozonation of a high bromide water with monochloramine doses of 3–5 mg/L.
NDMA was formed at an average concentration of 53 ng/L by ozonation and complete removal was achieved by BAFs after several months of biological acclimation.
An average 25% removal of 1,4‐dioxane was achieved via oxidation by hydroxyl radicals during ozonation.
Key Takeaways
Water utilities are turning to “digital twins” to benefit their ongoing operations, improve planning, and enhance operator training.
As facility “flight simulators,” digital twins allow utilities to quickly investigate many solutions without putting equipment, public health, or the environment at risk.
Dynamic process simulations boost communication and collaboration, especially for teams with varied technical backgrounds.
Industries occasionally discharge slugs of concentrated pollutants to municipal sewers. These industrial discharges can cause challenges at wastewater treatment plants (WWTPs) and reuse systems. For example, elevated total organic carbon that is refractory through biological wastewater treatment increases the required ozone dose, or even exceeds the capacity of the ozone unit, resulting in a treatment pause or diversion. So, alert systems are necessary for potable reuse. Machine learning has many advantages for alert systems compared to the status quo, fixed thresholds on single variables. In this study, industrial discharges were detected using supervised machine learning and hourly data from sensors within a WWTP and downstream advanced treatment facility for aquifer recharge. Thirty-five different types of machine learning models were screened based on how well they detected an industrial discharge using default tuning parameters. Six models were selected for in-depth evaluation based in their training set accuracy, testing set accuracy, or event sensitivity: Boosted Tree, Cost-Sensitive C5.0, Oblique Random Forest with Support Vector Machines, penalized logistic regression, Random Forest Rule-Based Model, and Support Vector Machines with Radial Basis Function Kernel. After optimizing the tuning parameters and variable selection, Boosted Tree had the highest testing set accuracy, 99.2%. Over the 5-day testing set, it had zero false positives and would have detected the industrial discharge in 1 h. However, setting fixed thresholds based on the maximum normal datapoint within the training set resulted in nearly as good testing set accuracy, 98.3%. Overall, this study was a successful desktop proof-of-concept for a machine learning-based alert system for potable reuse.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.