Registe, Joshua

Joshua Registe is a Senior Data Scientist with Jacobs Engineering, based in New Jersey.

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

Polymer Optimization Using Machine Learning

Abstract

BACKGROUND Jacobs operates two wastewater treatment plants for the City of Vancouver Washington, across the Columbia River from Portland Oregon. The West Side WWTP is a 28 MGD capacity plant that uses a biological nutrient removal process, followed by centrifuge dewatering and incineration. The project team is led by Travis Capson, with Sam Mielcarek as operations manager and Paul Muirhead as maintenance manager. Optimizing polymer dosage for solids thickening or dewatering is an especially challenging problem in our industry, with at least a dozen potential variables to consider when trying to maximize dewatered solids at the lowest overall cost: feed solids concentration, flow rate, polymer dosage and concentration, temperature, feed solids characteristics and blends, machine settings, etc. The site operations staff already fully understood the complexity of the challenge but was forced to spend extra effort tracking and making frequent changes to various settings in the system every day, all while balancing the competing demands of polymer usage cost vs. the cake solids dryness and fuel cost at the incinerator. DISCUSSION A machine learning model that matched the biggest drivers of polymer dosage against actual dewatered cake solids content values multiple times per day, over several years, was created. This involved leveraging recent upgrades to the SCADA control systems designed by Jacobs at the plant to support the capture of real time data for accurate dosage predictions. Starting in May 2023, push notifications for dosage recommendations went live to field staff. Gradually at first, but with increasing confidence, the staff have been following the predictive recommendations and seeing reduced polymer usage and utilization throughout FY 24 is shown in Figure 1. The predictive data science approach to polymer dosage control is savings around 10%-20% - an improvement on a budget of over $700,000 per year in polymer. The intent behind this optimization strategy is to reduce polymer consumption while maintaining appropriate cake solids concentrations for downstream processes and off-hauling. The plant has indicated a target concentration of 26-28% as optimal based on their experiences and the figure below provides a summary of where the system operates below range and when it has operated within range historically. Although It is understood that polymer may not be the only factor for contributing to differences in cake TS concentrations, for simplicity, prior to deployment of the optimization of a preliminary opportunity assessment was done to check these ranges as a function of polymer dose and simplify this to 'overdosing' and 'underdosing'. Based on the historical data going back to January 2021, 54% of the time the system operates within the expected range, 30% of the time above the expected range (overdosing 30% of the time), and 15% of the time below the expected range (underdosing 15% of the time). and thus the opportunity stemmed from being able to cut the 30% overdosing value to being within the desired Cake TS range. A multi-headed convolutional neural network (MH-CNN) approach was used with a temporal component with lookback features (as predictors, response, control variable -- polymer feed) and lookforward features (response and control variable) technique. For each prediction, the model looks back at a period of data and based on what it observes predicts what will happen over the lookforward period. This allows for the MH-CNN to do its own lag engineering by identifying the phemonena within the lookback window are most predictive for lookforward dynamics an schematic illustrating this architecture is shown in Figure 3. In addition to the data provided from the facility, an acoustic sensor was installed on the external wall of pipe carrying post-centrifuged cake. The theory behind this was to use the vibrations and noise measured at different frequencies when concentration of cake varies in order to predict cake TS. This was a non-intrusive install and sensor was glued to the pipe at the discharge point of schwing pump. The location of this was set such that it was installed close to a wall to minimize readings of ambient and non-relevant noise signals. Figure 4 shows the location of this installation. The site has robust sampling plan of measuring total solids at various locations in the process and this data is used to validate and continuously improve the results of the models. Figure 5 shows the results of the model's ability to predict cake TS concentrations. The test set shows a Pearson's R correlation value of 0.59 with a p value less than 0.05 indicating a statistically significant correlation. The variable importance figure shows the importance of the relative features used to train the model where WAS feed rates and the installed acoustic sensor showed significant importance on the model's ability to predict cake TS concentrations. This case-study demonstrates significant progress in the successful implementation of AI at a treatment facility. The strong engagement with operators has advanced their skillset such that they are submitting abstracts to their local operator's conferences at their own volition.

This paper was presented at the WEF Residuals & Biosolids and Innovations in Treatment Technology Joint Conference, May 6-9, 2025.

SpeakerRegiste, Joshua

Presentation time

10:15:00

22:35:00

Session time

10:15:00

23:45:00

SessionPolymer Optimization: How to Get the Most Bang for your Buck

Session number27

Session locationBaltimore Convention Center, Baltimore, Maryland, USA

TopicBelt Filter Press, Biosolids, Data Analytics, Dewatering Optimization, Innovative Technology, Machine Learning, Operators, Polymer, wastewater

Author(s)

Registe, Joshua, Rickermann, John, Pfister, Nick, Myers, John, Bauer, Heidi

Author(s)J. Registe1, J. Rickermann1, N. Pfister1, J. Myers1, H. Bauer1

Author affiliation(s)Jacobs Engineering, 1

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date May 2025

DOI10.2175/193864718825159791

Volume / Issue

Content sourceResiduals and Biosolids Conference

Word count6

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

Author(s)C. Yang 1; B. Johnson 2 ; J. Registe 3; T. Johnson 4; A. Rahman 5; J. Kenyon 6; C. Yang 1;

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

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159227

Volume / Issue

Content sourceWEFTEC

Word count18

Utilizing Intelligent O&M to Harness Machine Learning Across the Water and Wastewater Industry

Abstract

BACKGROUND AND LEARNING OBJECTIVE This presentation will describe a successful approach taken for developing and deploying wide-scale machine learning algorithms in the wastewater industry. Attendees will learn the level of effort and investment required to truly leverage the power of Artificial Intelligence (AI) at scale and all the accompanying challenges, benefits, and considerations. This session focuses on the utilization of Foundry (a data delivery software developed by Palantir) to facilitate the development, deployment, and monitoring of machine learning at two wastewater treatment facilities (WWTF) — Agua Nueva WWTF as well as Wilmington WWTP. These implementations have been active for over a year and are utilizing the models to support day to day operations consistently. With the case study at both Agua Nueva and Wilmington WWTF, disinfection plays a large role in the successful treatment of wastewater and has significant cost implications dependent on operations. Using Foundry, Jacobs unlocked the power of streaming sensor data, integrated with Jacobs Smart Algorithms and subject matter expertise, models can be custom tailored and translated to simple human readable recommended actions. Real-world feedback, including operator messages model error, and sensor drift, are captured in platform to continually improve performance over time. PROJECT OBJECTIVES The objectives were to: 1.Increase operator engagement with tools developed by data scientists and process engineers to model the complexities of treatment facilities and translate those models into actionable workflows that operators can use to make their jobs easier while improving plant efficiency with chemical usage. Discussion will present how lead Jacobs operators at two different wastewater facilities leverages these tools developed by data scientist and process engineers to facilitate chemical savings. 2.Build technical infrastructure that involves heavy data engineering to configuring data connectors and pipelines to ingest SCADA historian data, maintenance data, HACH WIMs data, and financial data all into a single unified platform and using data pipelines and cloud technology. 3.Develop and deploy machine learning that ingest all this information and provide actionable insights and recommendations to operators to fully leverage big data, cloud technology, data science, and wastewater subject matter expertise. Figure 1 shows a mapping of all the processes used in the foundry toolkit to facilitate the objectives from start to finish. METHODOLOGY, RESULTS, AND CONCLUSION The modeling for the case studies at Agua Nueva and Wilmington follow a 3 part modeling format: -multivariate bayesian inferencing to statistically quantify risk profiles for disinfection and bacterial exceedances (Figure 2). This allowed for designing a dosing strategy for the facility that would result in as close to 0% probability of exceedance as statistically definable based on the available data streams and past performance of the facility considering things like CT, contact time, dose, wind speeds, pH, temperature, and other conditions at the facility. -machine learning modeling to predict plant flows and other future conditions to estimate (Figure 3). Using weather forecasts, temporal data, and plant data allowed for successful prediction of flow concentrations with R2 values > 0.9. -estimating required dosages based on aforementioned steps and using machine learning to estimate downstream implications such as residual concentrations. (Figure 4) There are many components to how the models are built and how model metrics are tracked. The main metrics tracked in foundry are root mean squared errors (RMSE), mean absolute percentage errors (MAPE), and coefficients of determinations (r2). These are tracked in real time in Foundry and can alert to model health and notify Data Scientists for any sensor drift or model retraining. Additionally, Figure 6 shows several randomized back tests of the response variable (total residual chlorine concentrations) that depict how well models remain relevant to optimizing for compliance and savings. As of current, both sites are on track to having 10-30% energy Savings at Agua Nueva with close to $100,000 in annual energy costs for Agua Nueva and around $250,000 in savings for Wilmington. For Agua Nueva and Wilmington respectively, Figure 5 and Figure 6 shows the results of this on a gal/MGD (gallons of hypo used per million gallons of water treated) for basis where the red line shows the historical average usage and the green line shows the chemical usages after implementation of the tool. Savings are the area between the two lines and shaded in blue and the trends are continuing to be positive. In addition to this, implementation of the technology is also tracked over time and both site exhibit model utilization rates of over 70% shown in Figure 7 and Figure 8. This case-study demonstrates significant progress in the successful implementation of AI at a treatment facility. The strong engagement with operators has advanced their skillset such that they are submitting abstracts to their local operator's conferences at their own volition.

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 as well as Wilmington wastewater treatment facility (WWTF), both with a focus on disinfection chemical optimization.

SpeakerRegiste, Joshua

Presentation time

09:30:00

09:50:00

Session time

08:30:00

10:00:00

SessionLeveraging Machine Learning for Facility Operations

Session number509

Session locationRoom 253

TopicAdvanced Level, Facility Operations and Maintenance, Intelligent Water, Municipal Wastewater Treatment Design

Author(s)

Registe, Joshua, Rickermann, John

Author(s)J. Registe1, J.H. Rickermann2

Author affiliation(s)1Jacobs Engineering, NJ, 2Jacobs, CT

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159593

Volume / Issue

Content sourceWEFTEC

Word count14

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