Pimentel, Anthony

Anthony Pimentel was a speaker at the WEFTEC 2024 conference in New Orleans, LA October 5-9, 2024.

Titles from this speaker

Machine Learning In The Dark: Disinfection With <55% UV Transmittance

Abstract

Introduction: UV disinfection systems have gained popularity in wastewater disinfection applications as they can inactivate microorganisms while avoiding the injection of a chemical and any risk of a high disinfectant demand or disinfection byproducts. UV disinfection process design must account for UV Transmittance (UVT) which is a critical parameter that characterizes the ability of UV photons to pass through water and reach a microbiological target. Monitoring of UVT is achieved by online or benchtop readings, but online data can be skewed by sensor drifting or water quality impacts. Black & Veatch worked with two wastewater facilities where UVTs <55% were observed and employed machine learning to analyze correlations between measured UVT and various water quality parameters including carbonaceous biological oxygen demand (CBOD) and total organic carbon (TOC). UVT prediction allows for historical data to be verified whether the sensor was reading correctly or for optimization of UV system operation. Materials and methods: This study collected UVT and additional water quality data from each respective treatment facility secondary effluent at various sample collections intervals over the last 5 years. UVT was collected with online and benchtop instruments while other water quality data were collected via analytical methods accepted by 40 CFR criteria. Data cleaning and analysis was performed in R using various open-source packages and algorithms. Results and discussion: Review of Industry Guidance. Although there is no EPA requirement of a minimum design UVT for wastewater disinfection, guidelines have been followed in various states. The 2012 NWRI Guidelines set minimum allowable UVTs for wastewater applications based on the type of upstream treatment with a 55% minimum for applications with media filtration (REF2). In addition, 10 States standards recommends UV disinfection in wastewater be limited to effluent UVTs greater than 65% (REF3). However, UV disinfection has been implemented in states across the US with UVTs of 55% or lower, where various benchtop and full-scale data supports viable inactivation of microbes relevant to each projects operating permit. This study considered two facilities which are known to produce lower UVTs than conventional nitrifying treatment, Pure Oxygen Activated Sludge and Trickling Filter, from projects in Michigan and California, respectively. Machine Learning Techniques. Machine learning consists of algorithms and statistical models to analyze and draw inferences from patterns in data and can be utilized for various applications relevant to water treatment. This study reviews industry accepted techniques for employing machine learning for numerical predictions models based on multivariate regression and provides an in-depth review of the two algorithms employed in this work, Random Forests (RF) and Support Vector Regression (SVR). Case Study 1. A pure oxygen activated sludge treatment facility in Michigan undergoing a disinfection system evaluation was analyzed to consider UV disinfection to replace previous disinfection systems using sodium hypochlorite and ozone. Black & Veatch implemented an online UVT probe for a period of 12 months where the plant experienced various low UVT trends (<50%) that were impacted by ferric chloride addition, expected industrial source discharges, and sensor drifting. RF and SVR machine learning algorithms were employed to predict UVT based on CBOD, Solids and Temperature. Model results were successful in predicting UVT with an R mean squared value (RMSE) of 0.84 and 0.83, respectively (Figures 2 and 3). To mitigate the impact of excessive drifting of the online UVT probe, the model was further refined to predict periods where the UVT probe may have drifted out of calibration. Outcomes of these predictions could help promote instrument calibration or ensure historical data is accurate and whether or not it should be included in further analysis for process design of a UV disinfection system. Case Study 2. A regional wastewater facility in California undergoing a plant upgrade to employ UV disinfection for Title 22 non-potable reuse was also evaluated. As a part of process design and commissioning of the new Title 22 UV disinfection system, Black & Veatch analyzed 2+ years of water quality data where UVTs less than 50% were observed, which has been documented for trickling filter secondary effluent (REF4). Upstream trickling filter backwashing events were thought to have especially produced organic loads which impacted the UVT sensor and may have altered data collection after the system upset had already passed. TOC and TSS data were used to train the RF and SVR models to predict UVT across the monitoring period, with the SVR achieving a higher success in prediction with an RSME of 0.86 (Figures 5 and 6). Conclusions: 1.Applications where UVT is <55% can be managed contrary to some guidance and provide effective disinfection. 2.Machine Learning can verify analyzer trends to identify correct/incorrect historical data and ensure appropriate process design. 3.Machine Learning can be integrated with control approaches to accelerate response of UV disinfection systems based on anticipated water quality. 4.Multivariate water quality constituent regression could be paired with UV validation equations to predict performance and improve understanding of disinfection operations.

UV disinfection systems rely on the real-time monitoring of UV Transmittance to appropriately inactivate microorganisms and protect public and environmental health. This study demonstrated the use of machine learning to predict UV Transmittance in applications where potential analyzer error or disadvantageous water quality impacts the ease of operations or system cost, further validating process design assumptions and improving UV system control and operations.

SpeakerPimentel, Anthony

Presentation time

10:30:00

11:00:00

Session time

10:30:00

12:00:00

SessionWhat's New in UV?

Session number518

Session locationRoom 238

TopicDisinfection and Public Health, Intelligent Water, Intermediate Level

Author(s)

Pimentel, Anthony, Hunter, Gary, Torkzadeh, Hamed

Author(s)A. Pimentel1, G.L. Hunter2, H. Torkzadeh3

Author affiliation(s)1Black & Veatch, CA, 2Black & Veatch, SC, 3Black & Veatch, GA

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159549

Volume / Issue

Content sourceWEFTEC

Word count11

Resilience, Sustainability, and Risk of Intermittent UV LP And LED Systems

Abstract

UV LP and LED systems were compared in their engineering viability for an intermittent flow, 1 MGD average flow UV wastewater system at 65% UVT regarding resilience, sustainability, and risk. Resiliency to system cycling, sustainability via a life-cycle cost comparison, and risk of maintaining system performance guarantees were evaluated for LP, current LED, and future LED systems. Overall, this assessment demonstrated the feasibility and future optimization of these systems in wastewater disinfection. As UV LEDs continue to develop and enter the wastewater market, full-scale UV LED systems could help wastewater treatment systems reduce energy cost, labor requirements, and monetary risk. Advancement of UV LP and LED systems in treating intermittent flows with minimal energy requirements will help the wastewater industry meet climate change and sustainability initiatives.

UV LP and LED systems were compared in their engineering viability for an intermittent flow, 1 MGD UV wastewater system at 65% UVT regarding resilience, sustainability, and risk. Resiliency to system cycling, sustainability in a life-cycle cost comparison, and risk of maintaining system performance guarantees were evaluated for LP, current LED, and future LED systems. Overall, this assessment will demonstrate the feasibility and future optimization of these systems in wastewater disinfection.

SpeakerPimentel, Anthony

Presentation time

09:00:00

09:30:00

Session time

08:30:00

10:00:00

SessionUV Disinfection: Where are we? Where are we going?

Session locationRoom S404c - Level 4

TopicDisinfection and Public Health, Intermediate Level, Municipal Wastewater Treatment Design, Research and Innovation

Author(s)

Pimentel, Anthony

Author(s)A. Pimentel 1; G. Hunter 2 ; A. Pimentel 1; H. Mettlan 3; R. Osborne 4; J. Garrison 6; S DeBoses 6;

Author affiliation(s)Black & Veatch 1; Black & Veatch 2 ; Black & Veatch, Los Angeles, CA 1; Black & Veatch 3; Black & Veatch 4; Clemson Wastewater Treatment Facility 6; Clemson Wastewater Treatment Facility 6;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159162

Volume / Issue

Content sourceWEFTEC

Word count12