Thompson, Kyle, Thompson

Thompson, Kyle

Kyle Thompson is a Senior Reuse Technologist and National PFAS Lead at Carollo Engineers. Kyle received his bachelor's in environmental engineering from Missouri S&T and PhD in Environmental Engineering from the University of Colorado Boulder. Kyle is a Professional Engineer in Texas.

Titles from this speaker

Description: Identification and Removal of Performance- and Health-Based Indicator Chemicals in a...

Identification and Removal of Performance- and Health-Based Indicator Chemicals in a Mobile, Carbon-Based Direct Potable Reuse Pilot

Abstract

1. INTRODUCTION
Potable reuse with reverse osmosis (RO) presents brine disposal challenges for inland communities. Carbon-based advanced treatment (CBAT) eliminates these brine disposal challenges. CBAT trains often include combinations of the following processes: ozonation, biologically activate carbon (BAC) filtration, granular activated carbon (GAC), coagulation, microfiltration (MF)/ultrafiltration (UF), UV disinfection, and/or UV advanced oxidation processes (UV/AOP). Much research has been done on chemical removal by the processes used in CBAT. Nevertheless, demonstration projects continue to provide meaningful information about new or overlooked chemicals and their occurrence or removal in wastewater effluent. The concentrations of chemical pollutants vary between wastewater treatment plants (WWTPs) based on industrial point sources or different consumer patterns. WWTPs also have site-specific differences in water quality parameters that impact treatment efficiency [e.g., nitrite consuming the ozone dose or total organic carbon (TOC) competing with trace organics for sorption sites on GAC]. Thus, piloting is necessary at planned reuse sites to (1) demonstrate the safety of reuse to the public and regulators, (2) fine-tune process design, and (3) identify key site-specific health- and performance-based indicators. In 2020, Colorado Springs Utilities (CSU), the Colorado School of Mines (CSM), and Carollo Engineers, Inc. collaborated to design and build a mobile Direct Potable Reuse (DPR) demonstration trailer. The PureWater Colorado demonstration trailer has served multiple purposes: public engagement, research, piloting for Colorado utilities considering reuse, and informing state-level reuse policy. The CBAT process in the trailer includes ozonation, BAC, GAC, MF, UV/H2O2, and chlorination. PureWater Colorado was commissioned in June 2021. Routine sampling is ongoing at multiple points through the multi-barrier, CBAT process. Several evaluations have been conducted as part of this project, but this presentation will focus on the identification and removal of health- and performance-based indicators.
2. METHODS Health-based indicators are defined as specific chemicals that occur at concentrations with human health relevance at the influent to the reuse system-regardless of their difficulty to remove or federal regulatory status (Drewes et al. 2018; Anderson et al. 2010). Health-based indicators specific to the CSU installation were selected using the procedure recommended by a Science Advisory Panel to the state of California to select which trace organics to monitor in potable reuse. Maximum concentrations of each chemical at the pilot influent were divided by Monitoring Threshold Levels (MTLs). These MTLs are based on drinking water guidelines or toxicity information from a variety of sources including the California Notification Levels and the Australian reuse guidelines. Since the focus of the above Science Advisory Panel was on organics, concentrations of metals and inorganics were divided by their USEPA Primary Maximum Contaminant Levels (MCLs). Chemicals with a maximum measured value to MTL ratio (max/MTL) would be necessary to remove to protect the public health, either through treatment or source control. Chemicals with max/MTL greater than 0.1 would be recommended for removal to maintain a desirable margin of safety. Performance-based indicators are defined as specific chemicals that are useful for monitoring whether a treatment process is performing as designed-regardless of their toxicity (NWRI 2019; Thompson and Dickenson 2020). Performance-based indicators specific to CSU were selected using the procedure recommended by a National Water Research Institute (NWRI) Independent Expert Advisory Panel to the state of Colorado. Chemicals were identified that would have 'sweet spot' moderate removal for each process. Ideally, the chemical would still be detectable after the process when the process is functioning properly, but the chemical's removal would drop noticeably during a malfunction. Among chemicals that met these criteria, the one with highest median concentration divided by its Method Reporting Limit (MRL) was selected, since this chemical could be most precisely quantified.
3. RESULTS Three health-based indicators with max/MTL > 1 were identified in the CBAT influent at CSU: the industrial chemical quinoline, the antibiotic amoxicillin, and the perfluoroalkyl substance (PFAS) perfluorooctanoic acid (PFOA) (Table 1). Fortunately, these three chemicals have so far been removed to below detection by the combination of treatment process, though this should continue to be monitored as the adsorptive capacity of the GAC diminishes over time. All measured chemicals were below their MTL or MCL in the CBAT purified water, through nitrate was closest to its limit. This demonstrates the importance of effective, consistent nutrient removal upstream of reuse (Hill et al. 2018). Based on the criteria described in Methods, the pharmaceutical primidone or the pesticide diuron would be suitable performance-based indicators for ozonation at CSU. Primidone would also be the best performance-based indicator for GAC (Figure 1). While no organic chemicals were consistently detected after GAC as of the time of this writing, N-nitrosodimethylamine (NDMA) could be an appropriate performance-based indicator for UV/H2O2, based on (1) its detection after BAF (Figure 1), (2) its known rapid breakthrough in GAC, (3) its resistance to oxidation, and (4) its moderately high removal by UV (Glover et al. 2019; Thompson and Dickenson 2020).
4. CONCLUSIONS AND NEXT STEPS Highest priority health-based indicators and ideal performance-based indicators identified in this study differed from those identified in technical reports or peer-reviewed literature conducted at other locations. For example, Drewes et al. (2018) recommended health-based indicators NDMA, NMOR, and 1,4-dioxane, and performance-based indicators gemfibrozil, sulfamethoxazole, iohexol, and sucralose. This exemplifies the importance of site-specific piloting and indicator selection. Fundamental to the mobile aspect of this project is that when piloting is complete at CSU, the PureWater Colorado trailer will become available for other Colorado utilities considering potable reuse.

This presentation will summarize results from the PureWater Colorado Mobile Direct Potable Reuse project while it was hosted by Colorado Springs Utilities. This presentation will discuss the concepts of health-based indicators and performance-based indicators with examples from that reuse pilot project.

SpeakerThompson, Kyle

Presentation time

08:35:00

08:50:00

Session time

08:30:00

10:00:00

TopicIntermediate Level, Microconstituents and Contaminants of Emerging Concern (Non-PFAS), Potable Reuse, Research and Innovation, Water Reuse and Reclamation

Author(s)

Thompson, Kyle

Author(s)K. Thompson1; R. Yu2; J. Rosenblum3; T. Cath3; C. Bellona3; J.P. Rehring2; J. Assouline2

Author affiliation(s)Carollo Engineers, Austin, TX1; Carollo Engineers, Broomfield, CO2; Colorado School of Mines, Golden, CO3

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2022

DOI10.2175/193864718825158506

Volume / Issue

Content sourceWEFTEC

Word count18

Machine Learning Soft Sensors for Energy Efficient Potable Reuse

Abstract

1.Applicability Many water quality parameters important in potable reuse are expensive or slow to measure. 'Soft sensors' are one way of applying machine learning (ML) for water quality and treatment. By modeling challenging water quality variables, soft sensors can reduce monitoring costs or make treatment more adaptive. Reuse systems are engineered to remove a wide variety of chemicals and pathogens to a high degree. Reuse systems are then thoroughly monitored at site-specific critical control points for numerous chemicals and pathogens to ensure regulatory compliance and public acceptance. Most chemicals and pathogens do not have online sensors. The online sensors that are available for specific chemicals or pathogens are often expensive and not fully real-time, with sample frequencies on the order of hours not minutes. Because of the absence of real-time data for chemicals or pathogens that could inform process performance, processes within reuse systems are generally designed and operated with large safety factors. These large safety factors expend more energy than is truly necessary to purify the water. As examples: (1)N-nitrosodimethylamine (NDMA) can drive the UV dose in UV advanced oxidation processes (UV/AOP) downstream of reverse osmosis (RO). UV doses are set conservatively based on maximum historical NDMA concentrations. This consumes more energy than truly necessary to remove NDMA to regulatory targets if the NDMA concentration in the UV/AOP feed were known. (2)Ozone residual is monitored to calculate the concentration*time (CT) for ozone disinfection. However, ozone sensors commonly have greater than +/-20% error (Chen et al. 2020). This results in much random noise in the calculated CT, requiring large safety factors to maintain the desired ozone LRV at all times. This entails higher energy for unnecessarily high ozone doses. Total organic carbon (TOC) could represent a more accurate basis for ozone dosing, particularly at lower ozone doses. However, TOC instruments measure more slowly than ozone sensors, which would lead to less adaptive control. The water sector is developing innovations in real-time sensors which may one day solve these inefficiencies. In the meantime, advances in process control through machine learning could provide the next best solution. This presentation will cover two examples of soft sensors for potable reuse: (1) NDMA in RO permeate and (2) TOC in ozone influent. 2.Methodology All three soft sensors were developed in the programming language R with open-source packages using historical data already available at the utilities. Datasets were split into fully separate training and testing sets to develop and evaluate the machine learning models, respectively. Numerous types of ML models were screened and optimized with the best summarized below. 2.1NDMA Case Study A soft sensor was developed for NDMA in RO permeate. One hundred sixty-two NDMA datapoints, measured every three hours over three weeks, were provided by Orange County Water District from their Groundwater Replenishment System. Features (i.e., inputs or independent variables) included ammonia, pH, turbidity, total chlorine, and pressure. 2.2TOC Case Study A soft sensor was developed for TOC upstream of ozone in a carbon-based reuse system. Hampton Roads Sanitation District provided data from their SWIFT Research Center, a 1 MGD carbon-based reuse demonstration facility. Data was provided at 5-minute intervals over three months. Features included UV transmittance, pH, and ammonia. TOC was actually measured at approximately 2.5-hour intervals, so the true TOC measurements were extracted from the interpolated data. 3.Results 3.1NDMA Case Study A random forest ML model predicted the RO permeate NDMA with a root mean square error (RMSE) of 3.8 ng/L. Basing the UV fluence on the predicted NDMA could save 26% UV energy. Even dosing the UV fluence higher based on a 95% confidence interval around the prediction would result in 13% energy savings for a facility targeting 0.69 ng/L NDMA for surface water augmentation reuse. 3.2TOC Case Study Boosted trees (bstTree) predicted TOC before ozone with a RMSE of 0.35 mg/L (Figure 1). This was much more accurate than a linear model based on UV transmittance (RMSE = 0.709 mg/L) or assuming the last known value of TOC (RMSE = 0.528 mg/L), which would essentially be the status quo. The TOC ML models performed their best when using the last known TOC measurements as one of the inputs, which could be the case when using the model to fill in gaps between the TOC measured every 2.5 hours. Nevertheless, even without the last known TOC as an one of the inputs, ML predicted TOC more accurately than simply assume it would be equal to the last known TOC. This outcome raises the prospective of replacing the TOC instrument with ML, instead of using ML only to fill in gaps. Conclusions/Outcomes Both soft sensors succeeded in predicting the modeled contaminants more accurately than simpler approaches like linear models. Furthermore, these soft sensors outperformed the simpler methods by margins that could have meaningful consequences in terms of saving cost or energy by basing operation on the predicted values. Further research is needed to scale-up these soft sensors by applying them in real-time or transferring them to similar reuse facilities.

Many water quality parameters important in potable reuse are expensive or slow to measure. 'Soft Sensors' are one way of applying machine learning for water quality and treatment. By modeling challenging water quality variables, soft sensors can reduce monitoring costs or make treatment more adaptive. This adaptiveness saves energy during times of favorable water quality.

SpeakerThompson, Kyle

Presentation time

13:30:00

14:00:00

Session time

13:30:00

15:00:00

SessionImproving Disinfection Processes through Machine Learning

Session number203

Session locationRoom 346

TopicDisinfection and Public Health, Intermediate Level, Research and Innovation, Water Reuse and Reclamation

Author(s)

Thompson, Kyle, Newhart, Kathryn, Koyama, Yoko, Branch, Amos, Salveson, Andrew

Author(s)K. Thompson1, K.B. Newhart2, Y. Koyama3, A. Branch4, A. Salveson5

Author affiliation(s)1Carollo Engineers, Inc., TX, 2Oregon State University, NY, 3Carollo Engineers, TX, 4Carollo Engineers, Inc., CA, 5Carollo Engineers, CA

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159604

Volume / Issue

Content sourceWEFTEC

Word count10

Machine Learning to Predict Pathogen Removal in Potable Reuse MBRs Based on Microbial Surrogates

Abstract

Applicability Potable reuse treatment requires advanced processes to significantly reduce pathogen concentrations. A novel approach includes membrane bioreactors (MBRs), reverse osmosis (RO), and ultra-violet advanced oxidation processes (UV/AOP). This is an improvement to the conventional approach, often referred to as the 'gold' standard for reuse consisting of activated sludge (CAS), ultrafiltration (UF), RO, UV/AOP. MBRs offer benefits like smaller footprint, reduced sludge, and more reliable effluent quality. Yet, MBR-based reuse raises concerns about pathogen passage due to potential membrane damage, necessitating vigilant pathogen reduction monitoring. Models for pathogen removal in MBR systems have several practical applications, such as an early alert system for damaged membranes or to adjust downstream disinfection. Utilizing online sensors or microbial surrogates is quicker and more cost-effective than direct monitoring of pathogens like Cryptosporidium and Giardia. Our goal was to develop a sufficiently accurate model using online sensors or lower cost surrogates to reduce the frequency of Cryptosporidium and Giardia sampling while ensuring public protection. While the focus of this study was Giardia in the context of potable reuse, the methods discussed in this presentation would also be applicable to E. coli or total coliforms for NPDES compliance. Demonstrated Results and Outcomes Methodology We developed machine learning (ML) models to predict post-MBR treatment pathogen levels, focusing on Cryptosporidium and Giardia. Data was collected from a demo-scale MBR treating secondary effluent, referred to as a 'tertiary MBR' due to its polishing biological treatment of secondary effluent. Some MBR fibers were intentionally cut during the pilot study to allow microbe passage. This helped assess the effectiveness of surrogates and techniques for monitoring and predicting Cryptosporidium and Giardia levels across various MBR effluent turbidity and operational setpoints. In the preliminary dataset, 76 Cryptosporidium and Giardia samples were analyzed, along with paired surrogates. We used regression ML to predict Cryptosporidium or Giardia concentration and classification ML to predict if they exceeded 4 or 4.5 log reduction values (LRVs) alert thresholds, respectively. Six microbial surrogates (aerobic endospheres, C. perfringens, total coliforms, E. coli, male-specific coliphage, and somatic coliphage), pressure decay rates, and turbidity served as input variables (Figure 1). The data was split 80:20 with 61 datapoints for model development and 15 for model evaluation as shown in Figure 2. Results In terms of whether LRVs would be below or over the alert thresholds, conventional multiple linear models achieved 80% testing set classification accuracy for Cryptosporidium and 87% for Giardia. Classification ML algorithms like ada and svmLinearWeights2 reached 87% accuracy for Cryptosporidium, while pcaNNet and loclda models achieved 93% accuracy for Giardia, all better than the linear models (Table 1). Regression supervised ML models, xgbTree and ridge quantitatively predicted the Giardia LRVs with testing set R2 of 94.4% and 91.9%. However, linear models performed similarly for Giardia in this context. Conclusions Depending on the data analysis task (classification or quantitative prediction) and target pathogen (Giardia or Cryptosporidium), ML performance was marginally above or comparable to linear models. This may have been due to collinearity among the features, or the limited sample size to support ML. Nonetheless, our findings so far are a proof-of-concept for the efficacy of ML in developing pathogen monitoring and alert systems for MBRs. As part of 2024 schedule for this project, we will incorporate additional microbial sample size from the partner utility. We also plan to scale this project by making the ML models available as an interactive webtool that facilitates real-time pathogen prediction. This study was part of a US Bureau of Reclamation funded project including an Independent Expert Panel facilitated by the National Water Research Institute. This interdisciplinary collaboration brought together insights from environmental engineers, microbiologists, and data scientists, enriching the project's depth and applicability. Our methodology could be extended to MBR-based treatment plants discharging to surface water with microbial or disinfection parameters in their NPDES compliance regulation, offering a valuable approach for addressing wastewater concerns in diverse contexts. Relevance to Audience Attendees of this presentation will leave with a comprehensive understanding of an innovative approach to potable water reuse treatment, the integration of ML models, cost-effective monitoring techniques using surrogates, and the potential ML applications in various advanced treatment settings. The insights shared can disseminate innovation in efficient wastewater treatment monitoring for public safety and regulatory compliance.

Supervised Machine Learning (SML) models were developed to predict Giardia removal in a potable reuse MBR treatment system with intentionally damaged fibers. Analyzing 76 samples with turbidity and microbial surrogate data, the sdwd classification SML predicted Giardia reductions with 80% accuracy at 4.5 log reduction values. The regression SML, particularly bstTree, achieved an R2 of 91%, outperforming linear models. These models could act as early warning systems for membrane damage.

SpeakerSuresh, Samarth

Presentation time

14:00:00

14:30:00

Session time

13:30:00

15:00:00

SessionImproving Disinfection Processes through Machine Learning

Session number203

Session locationRoom 346

TopicDisinfection and Public Health, Intermediate Level, Research and Innovation, Water Reuse and Reclamation

Author(s)