Registe, Joshua

Joshua is a lead Data Scientist and Process Engineer with Jacobs. He has expertise environmental planning, analysis, and the design of wastewater treatment facilities with a focus on applied science and research in disinfection, energy, nutrient removal, and solids + biogas treatment. Joshua specializes in statistics, visualization, data management, predictive analytics, advanced data analysis, machine learning, cloud solutions, and has led the development and deployment of several machine learning solutions in the water/wastewater industry.

Titles from this speaker

Agua Nueva WRF: Piloting Experience of Machine Learning Hybrid Nutrient Controller

Abstract

Water Research Foundation Project 5121 is titled Development of Innovative Predictive Control Strategies for Nutrient Removal. The project is focused on developing and full-scale testing of a hybrid (machine learning + mechanistic model) nutrient management controller at four different WRRFs. The project team has named the controller ODIN: Operational Decision-making Information Network. The primary objectives of the ODIN pilot for ANWRF are to: Provide twice daily DO setpoint recommendations for the step feed bioreactor. Provide WAS rate recommendations for appropriate SRT control The implementation of data-driven dissolved oxygen control recommendations at the Agua Nueva WRF has resulted in an aeration savings of approximately 10 percent as compared to historical operation at the facility. These recommendations have resulted in operations gaining more insight into the operation of the facility, as well as provide the cost savings described.

SpeakerJohnson, Bruce

Presentation time

16:00:00

16:15:00

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)

Johnson, Bruce

Author(s)B. Johnson 1; B. Johnson 1 ; C. Yang ; J. Registe 4; A. Menniti 5; A. McClymont 6; R Abel 6; T Mason 8;

Author affiliation(s)Jacobs 1; Jacobs 1 ; Jacobs ; Maia Analytica 4; Jacobs 5; Jacobs 6; Jacobs 6; Jacobs 8;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159117

Volume / Issue

Content sourceWEFTEC

Word count12

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: Leveraging Big Data Platforms in Water and Wastewater to Accelerate Machine Learning...

Leveraging Big Data Platforms in Water and Wastewater to Accelerate Machine Learning Optimization at a Regional Scale

Abstract

With the abundance of data availability across treatment facilities, artificial intelligence is paving the way for unique optimization opportunities. The examples provided encompass the Agua Nueva Water Reclamation Facility (WRF), which concentrates on minimizing aeration energy expenses and enhancing nutrient management, as well as the Wilmington WWTF, emphasizing the optimization of disinfection chemicals. Several artificial intelligence (AI) and Bayesian modeling frameworks were developed with both sites using AI algorithms with mean absolute percentage errors < 10% for both forecasting of future conditions at a facility, as well as causal inferencing for predicting water quality. Post deployment, both sites are regularly achieving anywhere from 10-30% savings in energy or chemical usage. These case-study demonstrates significant progress in the successful implementation of AI at a treatment facility and the ability to aid in the empowerment of treatment plant operators while improving efficiencies in wastewater treatment.

This paper describes successful approaches taken for developing and deploying wide-scale machine learning algorithms in the water and wastewater industry. This includes lessons on the level of effort, investment, challenges, and benefits. Two case studies are presented: Agua Nueva Water Reclamation Facility (WRF) that focuses on reducing aeration energy cost and nutrient management, as well as Wilmington wastewater treatment facility (WWTF) with a focus on disinfection chemical optimization.

SpeakerRegiste, Joshua

Presentation time

13:30:00

13:50:00

Session time

13:30:00

15:00:00

SessionPlanning and Process Improvement Case Studies: Winning with Twinning

Session locationRoom S505b - Level 5

TopicAdvanced Level, Energy Production, Conservation, and Management, Municipal Wastewater Treatment Design, Nutrients, Research and Innovation

Author(s)

Registe, Joshua

Author(s)J. Registe 1; J. Rickermann 2 ; B. Johnson 3; J. Registe 1;

Author affiliation(s)Jacobs 1; Jacobs 2 ; Jacobs 3; Jacobs 1;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159226

Volume / Issue

Content sourceWEFTEC

Word count18

Description: Performic Acid Testing for Evaluation of Disinfection Efficacy and Toxicity in WWTP...

Performic Acid Testing for Evaluation of Disinfection Efficacy and Toxicity in WWTP Secondary Effluent at Both Laboratory and Onsite Pilot Demonstration

Abstract

PFA has been used in Europe for nearly 20 years as a disinfectant for wastewater because of its environmentally sustainable characteristics. The goals for this study were to determine the required conditions for PFA efficacy to achieve permitted limits for bacteria and toxicity at the Gresham WWTP; to evaluate the log inactivation of PFA for indicator organisms; and to compare the efficacy of PFA to chlorine. Comparing PFA to hypochlorite, lab testing demonstrated that PFA required 5.6 times less dose to reduce E. coli to below the WWTP permit limit using a 15-minute contact-time. The pilot results showed a CT (dose * contact-time) of 20 - 40 mg * minute/L to provide effective disinfection (with a 2-3 log reduction). PFA doses up to 4 mg/L with a contact-time of 15 minutes showed no toxic effects. This evaluation proved PFA to be a good disinfectant alternative for the North American market.

Performic Acid (PFA) testing on secondary effluent from Gresham's WWTP is presented. Bench-scale testing first identified initial disinfectant dose/contact time scenarios. Chemical-based disinfection effectiveness is dependent on water quality characteristics, contact time, contactor hydraulics, and kinetics of inactivation; onsite pilot testing was then conducted to further investigate these conditions. Lessons learned are documented to guide potential implementation of this technology.

SpeakerNoesen, Matthew

Presentation time

16:00:00

16:30:00

Session time

15:30:00

17:00:00

SessionAdvancements and Optimization with Chlorine, Peracetic Acid, and Performic Acid Disinfection

Session locationRoom S405 - Level 4

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

Author(s)

Noesen, Matthew

Author(s)M.G. Noesen 1; I. Porat 2 ; M. Stammegna 3; C. Hill 4; J. Reina 5; J. Registe 6; M. Noesen 1;

Author affiliation(s)Jacobs 1; Kemira Chemicals Inc 2 ; Kemira Water Solutions Inc. 3; Kemira Water Solutions Inc. 4; Jacobs 5; Jacobs 6; Jacobs 1;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159015

Volume / Issue

Content sourceWEFTEC

Word count22

Description: Soft Sensing Influent Concentrations with Airflow Rates by Digital Twins: A Real...

Soft Sensing Influent Concentrations with Airflow Rates by Digital Twins: A Real Case Study from Oldham WwTW

Abstract

United Utilities initiated a case study using already available data to predict the real-time influent concentrations to optimize chemical addition for phosphorus removal without installing physical sensors. This paper proposes a novel soft sensor mechanism that successfully estimates the high-resolution influent profiles with a digital twin model plus regular measurements (e. g. airflows, flows and lab measurements). The soft sensor overcomes the lack of dynamic profiles in DT applications.

SpeakerYang, Cheng

Presentation time

14:00:00

14:20:00

Session time

13:30:00

15:00:00

SessionPlanning and Process Improvement Case Studies: Winning with Twinning

Session locationRoom S505b - Level 5

TopicAdvanced Level, Energy Production, Conservation, and Management, Municipal Wastewater Treatment Design, Nutrients, Research and Innovation

Author(s)

Yang, Cheng