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,

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