Lesnik, Keaton

Dr. Keaton Lesnik, PhD, is the Co-Founder and Principal Engineer at MAIA Analytica, where he spearheads the research, development, and implementation...

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About Lesnik, Keaton

Dr. Keaton Lesnik, PhD, is the Co-Founder and Principal Engineer at MAIA Analytica, where he spearheads the research, development, and implementation of AI software aimed at enhancing operational decision-making and workforce training in the water and wastewater industries. Dr. Lesnik obtained his PhD from Oregon State University, where he began his work on applying machine learning to model complex environmental biotechnologies. With over 12 years of experience as a technical expert in wastewater projects, Dr. Lesnik has successfully deployed a number of AI pilots across the country,

Lesnik, Keaton

Titles from this speaker

Description: Dance of the Machines at AlexRenew WRRF: Piloting Experience of Machine Learning...

Dance of the Machines at AlexRenew WRRF: Piloting Experience of Machine Learning Hybrid Nutrient Controller

Abstract

A full-scale pilot of a hybrid (machine learning + mechanistic modelling) based nutrient controller is being implemented at Alexandria Renew Enterprises (AlexRenew), VA as part of The Water Research Foundation project 5121. The hybrid nutrient controller developed for this project has been named ODIN (Operational Decision-making Information Network). At AlexRenew, ODIN is used to recommend equalization discharge and return pumping setpoints with the goal of minimizing overall energy and chemical usage at the facility while maintaining a very low effluent total nitrogen. Recent historical and simulated nutrient removal performance is presented along with the metrics selected to evaluate the control period with ODIN suggestions being considered.

This paper was presented at WEFTEC 2023 in Chicago, IL.

SpeakerOristian, Monica

Presentation time

16:25:00

16:40:00

Session time

15:30:00

17:00:00

SessionCase Studies of Machine Learning in Full-Scale Nutrient Management Part II

Session locationRoom S505b - Level 5

TopicAdvanced Level, Facility Operations and Maintenance, Intelligent Water, Nutrients

Author(s)

Oristian, Monica

Author(s)M. Oristian 1; M. Oristian 1 ; B. Johnson ; C. Yang 3; J. Registe 5; H Stewart 6; K. Lesnik 7; A. Menniti 8;

Author affiliation(s)Alexandira Renew Enterprises (AlexRenew) 1; Alexandira Renew Enterprises (AlexRenew) 1 ; Jacobs ; Jacobs 3; Jacobs 5; Jacobs 6; Jacobs 7; Clean Water Services 8;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159118

Volume / Issue

Content sourceWEFTEC

Word count16

Development of Innovative Predictive Control Strategies for Nutrient Removal

Abstract

This paper describes the development and deployment of an innovative hybrid nutrient removal controller that integrates machine learning with mechanistic modeling. Automated data pipelines facilitate the ingestion of SCADA, laboratory (LIMS), and meteorological data. Rigorous data cleaning and imputation processes ensure the high quality of input data. A soft sensor, using an auto-calibrated SUMO model, estimates dynamic influent flow and concentration profiles. Machine learning forecasters extend these profiles by 24 hours, while emulators swiftly simulate responses for optimization. The controller was deployed at four facilities, including Clean Water Services, where primary effluent orthophosphate loads were predicted with a mean average percent error of 12.20%. The flexible and modular structure of the controller, which integrates mechanistic and machine learning elements, exemplifies its impressive predictive optimization capabilities.

SpeakerLesnik, Keaton

Presentation time

14:00:00

14:15:00

Session time

13:30:00

15:00:00

SessionApplications of Machine Learning in Full-Scale Nutrient Management Part I

Session locationRoom S504c - Level 5

TopicFacility Operations and Maintenance, Intelligent Water, Intermediate Level, Nutrients

Author(s)

Lesnik, Keaton

Author(s)K. Lesnik 1; K. Lesnik 1 ; J. Registe 2; K. Lesnik 3; B. Johnson 4; C. Yang 5; D Pienta 6;

Author affiliation(s)Maia Analytica 1; Maia Analytica 1 ; Jacobs 2; Maia Analytica 3; Jacobs 4; Jacobs 5; Jacobs 6;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159079

Volume / Issue

Content sourceWEFTEC

Word count10

Description: Genome-centric Insights into Full-scale EBPR Captured During a Biomonitoring...

Genome-centric Insights into Full-scale EBPR Captured During a Biomonitoring Campaign

Abstract

Phosphorus removal from wastewater remains a core component of modern wastewater treatment strategies critical for protecting aquatic ecosystems and capturing valuable nutrients. Enhanced biological phosphorus removal (EBPR) is attractive since it reduces the need for chemical precipitating agents and facilitates phosphorus recovery. However, instability can limit the reliable application of EBPR for meeting stringent effluent phosphorus limits. As part of an ongoing biomonitoring campaign, samples were collected before and during an EBPR process stability episode and sequenced for paired metagenomes and metatranscriptomes. Analysis revealed that shifts in carbon and phosphorus loads coupled with changes in the population sizes of non-fermenting polyphosphate accumulating organisms (PAOs; Candidatus Accumulibacter spp.), as identified in the metagenomic data, were associated with poor P-uptake. Metatranscriptomic data from these PAO populations revealed they were indeed substrate limited, as hypothesized from process data, with key genes related to carbon storage appearing down regulated during the instability event with respect to the stable control despite having a greater cell load during the instability period. Accordingly, several gene families related to carbon uptake, VFA metabolism, and PHA synthesis were found to be sensitive to the underlying carbon-limited conditions of the process instability. Our findings illustrate how combined metagenomic and metatranscriptomic sequencing can facilitate interpretation of process data and aid in evaluating the root cause of instability. These data also indicate potential gene targets suitable for developing EBPR performance markers as part of longer term or more frequent biomonitoring efforts.

As part of an ongoing biomonitoring campaign, samples were collected before and during an EBPR process stability episode and sequenced for paired metagenomes and metatranscriptomes. Analysis revealed that shifts in carbon and phosphorus loads coupled with changes in the population sizes and gene transcription of PAOs were associated with poor P-uptake. Our findings illustrate how sequence data can facilitate interpretation of process data and aid in evaluating the root cause of instability.

SpeakerLindner, Blake

Presentation time

14:00:00

14:30:00

Session time

13:30:00

15:00:00

SessionConnecting Molecular Biology Sensors to EBPR Performance

Session locationRoom S502 - Level 5

TopicAdvanced Level, Facility Operations and Maintenance, Municipal Wastewater Treatment Design, Nutrients

Author(s)

Lindner, Blake

Author(s)B. Lindner 1; B. Layton 2 ; B. Lindner 1; B. Layton 2; R. Golda 2; K. Lesnik 3; K. Konstantinidis 4; A. Menniti 2; P. Schauer 2;

Author affiliation(s)School of Civil and Environmental Engineering, Georgia Institute of Technology 1; Clean Water Services 2 ; School of Civil and Environmental Engineering, Georgia Institute of Technology 1; Clean Water Services 2; Clean Water Services 2; Maia Analytica 3; School of Civil and Environmental Engineering, Georgia Institute of Technology 4; Clean Water Services 2; Clean Water Services 2;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159085

Volume / Issue

Content sourceWEFTEC

Word count11

WRF 5121 Final Report Overview: Long-Term Evaluation of Hybrid Digital Twins at Three Full-Scale WRRFs

Abstract

INTRODUCTION The Water Research Foundation (WRF) project 5121, Development of Innovative Predictive Control Strategies for Nutrient Removal, aims at developing full-scale hybrid nutrient management controllers (named here as Hybrid Optimizer, HO) by employing a hybrid approach that combines both machine learning and mechanistic models. Three full-scale pilots are deployed and evaluated for this project to demonstrate long-term controller abilities. The concept, design and preliminary results have been shared previously [1-7], which triggered wide interest and heated discussion. Over the past year, refinements were made to improve performance and evaluation approaches were standardized. Currently, all three pilots are fully deployed and operational. This paper provides an overview of three case studies. The control for three pilots is advisory only with no direct control functions due to cybersecurity concerns. Instead, operators receive daily email notifications outlining recommended operations and/or access a web-based user interface (UI), guiding their actions. The full-scale pilots are: 1. Clean Water Services Durham WRRF (CWS): Primary clarifier alum addition setpoints once a day, aiming to stabilize orthophosphate load to the downstream biological treatment system. 2. Agua Nueva WRF (AN): Dissolved oxygen setpoints twice daily for the bioreactor systems, aiming to minimize overall aeration energy consumption. 3.AlexRenew WRF (AR): HO recommends three equalization setpoints once a day to equalize the influent loadings to the bioreactor to minimize energy, chemicals, and blower staging. METHODOLOGY As shown in Figure 1, Hybrid Optimizer consists of seven major components. Descriptions of each component can be found in [1-3]. In this paper, the performance of Soft Sensor, Influent Forecaster and emulator/optimizer is mainly presented and discussed. Four different types of standard graphs are used to evaluate the performance of the HO. (1)Control Chart. Directly conveys the validity of the model, based on which warnings and alarms are derived. It integrates the measured and the modelled values into a dimensionless metric (t_score) and allows its comparison with predefined bounds. (2)Process Plot. The process plot presents the measured and the modelled values. (3)Unscaled Error Plot. The Error plot presents the residuals between the modelled and the measured. (4)XY Plot. The XY Plot presents the Modelled v. s. the Measured and the prediction intervals. (not shown here for brevity) RESULTS AND DISCUSSION The components and timelines of the three pilots are listed in Table 1. The Graphic User Interface (GUI) for two pilots are in progress. Evaluation results are available for all three, however this abstract presents only partial results. The soft sensor can be considered a full digital twin of the modelled components. Thus, it provides dynamic 15-minute frequency data on all wastewater components in the model scope. For CWS the primary influent and effluent ortho-phosphate data was used to support primary alum addition recommendations, without an actual sensor. For AN the focus was ammonia. The soft sensor demonstrates alignment with measured data (Figure 2).

*In the Control Chart, the t score falls within the warning and alarm bounds (set at 95% and 99% confidence intervals), which indicates soft sensor performance. The t-score is the normalized metric of a given set of measurements.

*The Process and Residual Plots are more intuitive for process engineers to evaluate. The modelled and measured values align closely, and their parallel trends indicate good soft sensor performance. Figure 3 shows the AN performance for primary influent and effluent ammonia. The top graph shows a good match between the composite samples. However, the primary effluent in the middle and bottom graphs shows a poor match. It was found that high influent sulfide was biasing the measurements high. The bottom graph showing the dynamic concentration variation, other than the bias, shows a good variability match. Flow forecasting is critical to predictive control. The Forecaster uses the available weather forecast and other variables to estimate plant influent flow. Figure 4 compares influent flow (forecasted vs actual) as well as the forecasted versus actual rainfall. A close examination shows that much of the influent forecast error can be attributed to the error in the precipitation forecast. The AR optimizer (Figure 5) aims to equalize ammonia load, which helps stabilize the aeration demand and optimization of aeration and chemicals. The flow and loading prediction from the HO allows the optimizer to recommend three equalization setpoints for the next 24 hours. In the top graph, the green line is the target daily average loading, red and blue show the load with and without the recommendations. CONCLUSIONS This abstract presents abbreviated results of the long-term performance of HO during three full-scale pilot studies. The HO appears to be a promising opportunity to improve plant operation using digital twin technology by giving both optimized operational recommendations and increased insight into the facility's status. The ongoing assessment of the HO performance is anticipated to yield further valuable practical experiences and insights, including the benefits and costs of such applications.

This paper was presented at the WEFTEC 2024 conference in New Orleans, LA October 5-9.

SpeakerYang, Cheng

Presentation time

13:30:00

13:50:00

Session time

13:30:00

15:00:00

SessionLeveraging Automation and Analytics for Better Situational Awareness and Optimization: Part I

Session number205

Session locationRoom 350

TopicEnergy Production, Conservation, and Management, Facility Operations and Maintenance, Intelligent Water, Intermediate Level, Nutrients

Author(s)

Yang, Cheng, Johnson, Bruce, Lesnik, Keaton, Registe, Joshua, Rieger, Leiv, Stewart, Heather, Miletic, Ivan, Menniti, Adrienne, Oristian, Monica, Pienta, Drew, Mason, Tim

Author(s)C. Yang1, B.R. Johnson1, K. Lesnik2, J. Registe3, L.P. Rieger4, H.A. Stewart5, I. Miletic6, A. Menniti7, M.K. Oristian8, D.J. Pienta9, T. Mason10

Author affiliation(s)1Jacobs, CO, 2Maia Analytica, OR, 3Jacobs Engineering, NJ, 4Jacobs, NL, 5Jacobs, PA, 6inCTRL Solutions Inc., ON, 7Clean Water Services, OR, 8Alexandria Renew Enterprise, VA, 9Jacobs, 10Jacobs Eng, AZ

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159642

Volume / Issue

Content sourceWEFTEC

Word count16

Base text size -

Lesnik, Keaton

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