Branch, Amos

Amos is a water reuse technologist at Carollo Engineers, Inc. Amos has worked on a number of research and piloting projects with membrane systems with the goal of efficient implementation and production of safe filtrate quality for reuse.

Titles from this speaker

Description: Assessment of Turbidity for Assurance of Log Reduction Values by Membrane...

Assessment of Turbidity for Assurance of Log Reduction Values by Membrane Bioreactors

Abstract

Sampling and pathogen analysis can demonstrate very high log reduction values (LRVs) with membrane bioreactor (MBR) systems. However, it has been difficult to gain regulatory approval for these high LRVs as intermittent sampling is not viewed as sufficient to continually assure acceptable water quality. Hazard and critical control point (HACCP) frameworks are increasingly applied to systematically control pathogen risks in water reuse. HACCP frameworks for water reuse require that a significant barrier to pathogens is identified and that this barrier can be monitored to ensure it is functioning nominally - hence pathogen removal is achieved. In this work, a method to correlate MBR filtrate turbidity and pathogen LRV in MBR was investigated and the extent of indicator microorganism relative to total suspended solids transfer was quantified. The relationship between filtrate turbidity and LRV was logarithmic, suggesting that significant increases in turbidity are required to result in marginal reductions in LRV.

Membrane bioreactors can achieve very high log reduction values (LRVs). However, it has been difficult to gain regulatory approval for these high LRVs without correlation with an online monitoring technique. In this work, a method to correlate MBR filtrate turbidity and pathogen LRV in MBR was investigated and the extent of indicator microorganism relative to total suspended solids transfer was quantified. The relationship between filtrate turbidity and LRV was logarithmic, suggesting that significant increases in turbidity are required to result in marginal reductions in LRV.

SpeakerBranch, Amos

Presentation time

11:25:00

11:45:00

Session time

11:00:00

12:30:00

SessionLatest on Gaining Log Removal Credits for MBRs

Session number422

TopicDisinfection and Public Health, Microconstituents, Policy and Regulation, Research and Innovation, Water Reuse and Reclamation

Author(s)

Amos Branch

Author(s)A. Branch1; N. Fontaine2; A.C. Gilmore3; A. Salveson1;

Author affiliation(s)Carollo Engineers Inc., Walnut Creek, CA1Carollo Engineers Inc, Sacramento, CA2Carollo Engineers Inc, Phoenix, AZ3

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2021

DOI10.2175/193864718825158120

Volume / Issue

Content sourceWEFTEC

Word count13

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)

Suresh, Samarth, Thompson, Kyle, Salveson, Andrew, Branch, Amos

Author(s)S. Suresh1, K. Thompson2, A. Salveson3, A. Branch4

Author affiliation(s)1Carollo Engineers Inc., GA, 2Carollo Engineers, Inc., TX, 3Carollo Engineers, CA, 4Carollo Engineers, Inc., CA

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159529

Volume / Issue

Content sourceWEFTEC

Word count15

National and International MBR Research Projects For Water Reuse

This paper outlines current research and practice on MBRs pathogen removal as well as integrity monitoring and permitting approaches. The focus of this paper is to provide context and focus questions to support a facilitated discussion scheduled for Weftec 2022. The wider intent is to use the panel discussion as an opportunity to solicit stakeholder feedback from the Weftec audience to better define industry research needs for MBRs in water reuse.

SpeakerBranch, Amos

Presentation time

10:35:00

10:45:00

Session time

10:30:00

12:00:00

TopicIntermediate Level, Disinfection and Public Health, Potable Reuse, Water Reuse and Reclamation

Author(s)

Branch, Amos

Author(s)Amos Branch1; Larry Morris2; Chris Morrow3; Stephen Katz4; Andrew Salveson1;Andrew Gilmore1

Author affiliation(s)Carollo Engineers Inc., Walnut Creek, CA1; Kubota Membrane USA Corporation, Canton, OH2; DuPont Water Solutions, Los Angeles, CA3; SUEZ Water Technologies & Solutions, Toronto, Canada4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2022

DOI10.2175/193864718825158557

Volume / Issue

Content sourceWEFTEC

Word count10

Precision in Practice: Water Quality Optimization for UF Plant Efficiency

Abstract

Applicability A 52 MGD Water Treatment Plant was upgraded to use submerged ultrafiltration (UF) membrane to enhance energy efficiency. The plant consists of 12 trains of Veolia ZeeWeed 1000 membranes. The plant utilizes a pre-treatment system before membrane application, incorporates a Biological Treatment Unit (BTU), coagulation with alum (below 200 mg/L) and lime for pH adjustment, and Powered Activated Carbon (PAC) addition as necessary for taste and odor control. Additionally, flocculation and sedimentation precede the membrane system, with polymer addition to aid settling post-rapid mix. Clarified water undergoes two sets of four travelling band screens, directing flow to UF Trains 01-06 and Trains 07-12, respectively. The facility identified suboptimal performance in UF Trains 1-6 (fed from basins A1 and A2) compared to Trains 7-12 (fed from basin B). To address this, Carollo Engineers conducted a bench-scale study investigating the potential stabilization of UF hydraulic performance across the entire facility through adjustments in water quality. This objective was subdivided into three specific goals: enhancing the filterability of settled water from Pretreatment Basins A1 and A2, formulating an optimal dosing strategy for pretreatment chemicals, and validating proposed changes through benchtop membrane filtration studies. Our presentation will cover the results and learnings from the study and provide a practical and applicable lessons for water quality optimization. Methodology The testing methodology encompassed four distinct assessments. Firstly, Jar Testing simulated rapid mix conditions in a 6 x 2 L gator jar-style unit, generating supernatant aligned with operational conditions in Basins A1 and A2 for subsequent membrane filterability tests. Zeta Potential Testing and Titration measured zeta potential using the Malvern Zeta Sizer, incorporating incremental coagulant doses in a 2L beaker of test water, with coagulant demand calculated at zero zeta potential. Benchtop Pencil Membrane Filtration utilized water from post-treatment basins and jar test supernatant, applying preset standardized filtration and backwash conditions to the pencil module, with resistance determined through pressure transducers and flow monitoring. Finally, Water Quality Parameters, detailed in Table 1, outlined the parameters and testing methods for both plant water and test samples, utilizing water sourced from basins A1, A2, and B for jar testing and benchtop membrane apparatus. Results The pencil module filtration tests comprised 15 samples (7 from the plant, 7 from jar tests, 1 raw water). Out of the 7 plant samples, 3 were used to establish baseline filterability, 2 were used to study the effect of pH and 2 for the effect of free chlorine. Jar test samples investigated polymer dosing effects (4 samples) and optimal parameter combinations (3 samples). Each test included 30 runs, with backwashing after each 4-minute run, collecting filtrate volume (m3/m2) and resistance (m-1) data every 4 seconds. Data from each of the 30 runs per test underwent Inter Quartile Range (IQR) outlier removal. The Resistance vs. Filtrate Volume graphs for the 15 tests revealed two fouling types. Total Fouling, representing the slope of Resistance vs. Filtrate Volume for a single run, was determined by taking the median of total fouling values (see Figure 2) from all runs within a test. This value signifies the fouling removable through backwash or chemical cleans. Hydraulically Irreversible fouling, calculated as the slope of Resistance vs. Filtrate Volume across all runs for a test, denotes the fouling requiring chemical cleans for removal. A summary of the results is available in the table provided. The Resistance vs Filtrate volume graph (see Figure 1) can help visualize how the two parameters are calculated. Comparing the fouling results with the dosing strategy provided various insights. It showed that pH elevation has limited relevance to fouling. Increasing pH requires a proportional rise in coagulant to counter adverse effects on TOC removal. Chlorine, up to 1.5 mg/L, effectively reduces total fouling, but surpassing this threshold has minimal additional impact. The optimal polymer dose is around 0.05 mg/L; exceeding it leads to a marked increase in total fouling, highlighting the delicate balance needed for effective water treatment. Conclusion The research underscores the importance of tailored water quality optimization in membrane systems. It reveals that each system may behave differently depending on hydraulics, feed water quality, and end goal of treatment. The study's pivotal takeaway lies in recognizing the intricate balance required for effective water treatment, shaping a roadmap for enhanced efficiency in membrane-based water treatment plants. Relevance to Audience This presentation is tailored for operators and design engineers invested in advancing membrane filtration technologies, particularly within large-scale water treatment plants. Attendees will gain valuable insights into practical strategies for enhancing filterability, formulating optimal dosing strategies, and validating changes through membrane filtration studies. The relevance lies in the application of these innovative approaches, applicable not only to resolving hydraulic differentials but also to the broader context of optimizing membrane systems in water treatment plants.

A bench scale study was conducted to determine optimized coagulant and polymer doses for a facility that had been upgraded from conventional to membrane filtration. Bench studies included zeta potential determination of coagulant demand as well as jar testing coupled with standardized membrane filterability evaluation and water quality testing. It was recognized that there was a low optimum dose for polymer, beyond which, fouling of the membranes would worsen without any improvement in filtrate.

SpeakerBranch, Amos

Presentation time

15:30:00

15:50:00

Session time

15:30:00

17:00:00

SessionOptimizing Membrane Efficiency for Water Reuse and Disinfection

Session number421

Session locationRoom 349

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

Author(s)

Branch, Amos, Hugaboom, Dan

Author(s)A. Branch1, D. Hugaboom2

Author affiliation(s)1Carollo Engineers, Inc., CA, 2Carollo Engineers, ID

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159590

Volume / Issue

Content sourceWEFTEC

Word count11

Reclaimed Water Disinfection: Challenges and Solutions at Brightwater

Abstract

APPLICABILITY Owners and operators of water reuse systems with variable customer demand and/or an expansive distribution system face challenges when free chlorine is used for disinfection and distribution system residual. Free chlorine decays relatively quickly, making it difficult to select the correct dosage. Low customer demand can result in non-compliant low residual concentration (high system water age), and high customer demand can result in a detrimentally high residual concentration (low system water age). Process control based on a consistent free chlorine dosage is not suitable for the range of operating conditions. Free chlorine's preferential reactions with inorganic species that can be intermittently present, such as ammonia or nitrite, further complicate process control. This presentation describes a two-track approach that the King County, WA Wastewater Treatment Division (WTD) is pursuing to implement an alternative disinfection process for the Brightwater Reclaimed Water (RW) System. The first track is new ultraviolet (UV) disinfection with new chloramine for distribution residual. The second track is an innovative virus removal crediting via the existing membrane bioreactor (following Water Research Foundation guidelines) with chloramine for additional virus removal credit and distribution residual. Owners, operators, and designers of reuse systems would find this presentation valuable as disinfection is a critical process and disinfection crediting for membrane bioreactor (MBR) systems and maintenance of chlorine and chloramine residual are important topics for non-potable and potable reuse systems. DEMONSTRATED RESULTS AND OUTCOMES The Brightwater RW System produces unrestricted non-potable reuse water. Operation of the system is frequently interrupted from outages due to a non-compliant distribution chlorine residual. The minimum total chlorine concentration required is 0.5 mg/L and the maximum is 4.5 mg/L. An alternatives analysis determined a preferred approach for improving system reliability. The methods and results of the analysis and initial implementation steps are described below. Outage Root Cause Analysis: The unreliability of the RW system frustrated WTD and customers. Analysis determined that the root causes of outages were flow variations leading to a wide range of water ages, the chlorine dose, the metering pump turn-down, ammonia spikes, and treatment plant low flow periods as summarized in Figure 1. Several issues were linked to the free chlorine disinfection system. When the demand was low, the total chlorine residual fell below the minimum. If the demand was high and water moved quickly through the system, the dosed chlorine concentration was too high to decay (and thus exceeded required limits) before reaching the compliance point. Disinfection Alternatives Analysis: The project team conducted a high-level analysis of disinfection process alternatives as summarized in Figure 2. The disinfection process requirement is to achieve 4-log virus removal per Washington regulations. UV for primary disinfection with chloramine for residual, free chlorine primary with chloramine for residual, and chloramine for both primary disinfection and residual were short listed. Ozone, peracetic acid, and pasteurization were eliminated due to implementation cost and less application experience. Free chlorine was eventually eliminated due to process variability caused by ammonia and nitrite spikes. Due to chloramine's limited effectiveness on viruses, the project team pursued further investigation of chloramine disinfection combined with virus removal credits for the upstream MBR system. Full Scale MBR Virus Removal and Chloramination Testing: A protocol for conducting exploratory testing of the MBR system was developed following guidance outlined in WRF report 4997 (Salveson et al, 2021), which provides a validation testing protocol for MBR systems producing potable reuse water. Five sample pairs of the MBR feed and filtrate were collected and analyzed for bacterial coliphage removal. The results demonstrated that a minimum of 3.4-log virus removal occurred. Removal data is summarized in Figure 3. Bench top testing determined chloramination of bacteriophage MS2 could provide the remaining log removal. Outcome: The project team is pursuing both approaches, UV disinfection with chloramination and MBR virus crediting with chloramination, to implement a solution as quickly as possible without forgoing the potential to reduce project cost and simplify the process. Design of UV and chloramination facilities are underway while concurrently planning the Tier 2 testing needed to demonstrate virus removal through the MBR system. If MBR virus removal crediting is successful, the design can accommodate removal of the UV facility. RELEVANCE TO AUDIENCE This presentation would pique the interest of water reuse system owners and owners of wastewater MBR systems. This work details challenges with water age and maintaining distribution residual, while presenting the benefits of chloramination. MBR pathogen removal crediting is a novel approach to effluent disinfection that has the potential to reduce disinfection process requirements thus minimizing chemical and energy use. The MBR validation testing protocol per WRF guidance applied under this project will be of particular interest to engineers and owners pursuing MBR pathogen removal credits.

Preliminary testing of the King County WTD Brightwater membrane bioreactor (MBR) system has demonstrated a portion of the required 4-log virus removal for Class A RW is achieved through the MBR. Testing was conducted per the Tier 2 protocol outlined in Water Research Foundation Report 4997. Results showed that 3.3+ log removal value (LRV) of viruses occurred. The remaining LRV could be achieved by an appropriately sized downstream disinfection process that also provides a distribution residual.

SpeakerPayauys, Alison

Presentation time

08:30:00

08:45:00

Session time

08:30:00

10:00:00

SessionEnhancing Water Safety: MBR & RO Technologies for Achieving LRV Credits

Session number315

Session locationRoom 349

TopicAdvanced Level, Disinfection and Public Health, Microconstituents and Contaminants of Emerging Concern (Non-PFAS), Municipal Wastewater Treatment Design, Water Reuse and Reclamation

Author(s)

Payauys, Alison, Seymour, David, Salveson, Andrew, Branch, Amos

Author(s)A.M. Payauys1, D. Seymour2, A. Salveson3, A. Branch4

Author affiliation(s)1Carollo Engineers, Inc., WA, 2Kennedy Jenks Consultants, WA, 3Carollo Engineers, CA, 4Carollo Engineers, Inc., CA

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159585

Volume / Issue

Content sourceWEFTEC

Word count9

Sustainable Membrane Performance for Refinery Reuse of Non-nitrified Wastewater

Abstract

Central San owns and operates a wastewater treatment plant with a low solids retention time (SRT) activated sludge secondary treatment process. Low SRT secondary effluent can be challenging to filter and MF/UF vendors typically specify operation on a higher SRT fully nitrified secondary effluent. The goals of the pilot were to establish sustainable operating conditions for both MF/UF and RO as well as demonstrate that the product water would be fit for purpose for non-potable industrial reuse (low TDS, ammonia, and metals) at two nearby refineries. The pilot provided critical information to confirm that non-potable refinery reuse is achievable without upstream secondary treatment upgrades. Sustainable MF/UF fluxes up to 40 gfd with 2.5 mg/L ferric chloride as Fe and up to 30 gfd with 10 mg/L aluminum chlorohydrate coagulants were observed. The RO operated sustainably for almost three months and did not require a CIP.

To augment regional water supplies with recycled water, Central San completed parallel piloting of three different ultrafiltration membranes and reverse osmosis to define performance on non-nitrified secondary effluent. The challenging effluent was successfully treated, and sustainable design fluxes were achieved with low coagulant dosages. Preliminary water quality targets were met while operating at typical municipal flux targets.

SpeakerCauble, Amanda

Presentation time

16:30:00

16:50:00

Session time

15:30:00

17:00:00

SessionRefining Wastewater Effluent for Purification: Reuse-nutrient Removal-nexus II

Session locationRoom S404a - Level 4

TopicIntermediate Level, Municipal Wastewater Treatment Design, Nutrients, Potable Reuse, Water Reuse and Reclamation

Author(s)

Cauble Amanda

Author(s)C. Amanda 1; A. Branch 2 ; C. Cauble Amanda 1; D. Frost 3; S. Reddy 4;

Author affiliation(s)Central Contra Costa Sanitary District 1; Carollo Engineers 2 ; Central Contra Costa Sanitary District, Martinez, CA 1; Central Contra Costa Sanitary District 3; Carollo Engineers 4;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159135

Volume / Issue

Content sourceWEFTEC

Word count10

Description: Validating Membrane Bioreactors to Remove Pathogens During Water Reuse:...

Validating Membrane Bioreactors to Remove Pathogens During Water Reuse: Understanding Key Operational Parameters

Abstract

Operational parameters that significantly influence log reduction values (LRV) of indicator microorganisms by membrane bioreactors (MBRs) were identified based on analysis of a data set comprised of over 200 site visits to 11 different full scale facilities. High LRV coincided with operation at lower flux, TMP and MLSS and higher operating values of pH, temperature, solids retention time (SRT) and hydraulic retention time (HRT). Higher LRV for virus indicators was favored at lower permeability (i.e. more fouling). Therefore, test plans to determine worst case LRV for MBRs should be conducted under high flux, with clean membranes (low TMP), at short SRT and HRT and under the coldest expected operational conditions. Re-analysis of the Australian validation data and incorporation of new Californian work has suggested that default LRVs of 1.0 for virus and 2.5 for protozoa should be achieved by an MBR, provided filtrate turbidity is maintained below 0.2 NTU.

SpeakerBranch, Amos

Presentation time

14:10:00

14:30:00

Session time

13:30:00

15:30:00

SessionMBR for Water Reuse: Giving Credit Where Credit is Deserved

Session number227

TopicWater Reuse and Reclamation

Author(s)

A. SalvesonA. BranchA. GilmoreN. Fontaine