Rieger, Leiv

Leiv Rieger, PhD, PEng, a Wastewater Technology Fellow with Jacobs with 3 decades of academic and practical experience in optimizing water resource recovery facilities through digital tools and solutions. Leiv has extensively published his work and has been acknowledged by the 2015 WEF Eddy Wastewater Principles/Processes Medal for his paper on Ammonia-based Aeration Control and being a Fellow of the International Water Association (IWA). His combined expertise and experience in instrumentation, process and aeration system modeling, biological nutrient removal, and process control helps guide process teams around the world to develop optimized solutions.

Titles from this speaker

Applied Fundamentals: Instrumentation and Controls to Optimize N and P removal

Abstract

This paper provides an overview of process control fundamentals for the proportional–integral–derivative (PID) concept and its application in wastewater treatment. After a brief overview of the PID algorithm, the focus is on designing, commissioning, and maintenance of process control systems.

SpeakerRieger, Leiv

Presentation time

09:10:00

09:40:00

Session time

08:30:00

12:00:00

SessionStrategies to Optimize WRRFs for Nutrient Removal

Session number510

TopicNutrients, Research and Innovation

Author(s)

L. RiegerL. Rieger

Author(s)L. Rieger1; L. Rieger1;

Author affiliation(s)inCTRL Solutions Inc, ON1

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2020

DOI10.2175/193864718825157506

Volume / Issue

Content sourceWEFTEC

Word count12

Hybrid Modeling and Diagnosis to Reduce N2O Emissions at WRRFs

Abstract

Introduction
A key challenge in monitoring N2O at WRRFs is managing and analyzing large amounts of data to develop mitigation insights and inform operations. This paper presents a unique hybrid modeling approach that combines data-driven models with process knowledge to predict liquid-phase N2O concentrations. The tool has been successfully applied to two full-scale, long-term monitoring datasets; information about the two facilities is summarized in Table 1, and Figures 1 and 2.

The focus of the project was identifying drivers for N2O generation using diagnostic tools and not simply fitting a model to data. The project aimed at answering the following questions:
- Why did a specific N2O peak happen, and which variables are associated with it?
- Which operational settings should be changed to reduce or prevent an N2O peak?
- Is there a model that describes the correlation between measured variables that can explain N2O behavior and can be used to mitigate N2O emissions?

Methodology
Proprietary code was developed and applied to automate the building and training of a PLS (Projection of Latent Structures) model via the NIPALS (Non-linear Iterative Partial Least Squares) algorithm. The model is integrated with diagnostic tools that objectively evaluate model performance, guide model iterations, and identify effective control handles to mitigate N2O emissions. A key statistic used was the percent sum of squares explained in the input (%SSX — the goodness of fit for input data) and output (%SSY — indication of model output consistency) (Figure 3). Variable Importance Plots (VIP) were used to rank the most information-rich variables and identify which variables could be removed (Figure 4). Contribution plots were used to further analyze specific events by ranking the variables with the highest contributions to the result data point (Figure 5) - this ability to examine specific events is a key advantage of the PLS modeling approach.
A stepwise and iterative procedure was applied to develop a robust and reliable hybrid model (Figure 6).
A typical iteration identifies that the model is not providing a good fit for a specific section of the data, which would trigger a discussion among domain experts and typically lead to the identification of data issues or information to add to the model inputs. This iterative procedure guided the project team in adding domain expertise in process and control. Examples are the translation of operational information (e.g., logbooks, operator interviews) into a digital format to feed into the model (i.e., removing data for events such as sensor cleaning/calibration, or unreliable data based on operator insights).

Duffin Creek WPCP Results
The performance of the first model (%SSX: 28.9) was improved with every iteration with the last model iteration explaining 72% of the data (Figure 7). Key improvements were data cleaning (based on plant information, statistical analysis, and identification of a faulty temperature sensor for the N2O probe (Figure 8)), and the addition of domain expertise such as influent load calculations and feeding DO control errors (Figure 9). The final model achieves a very good fit and follows diurnal and seasonal variations, with an average tank N2O Emission Factor (EF) of 0.7%.

The diagnostic tools identified aeration control as the main driver for N2O accumulation. An aeration improvement project is currently ongoing and the impact on N2O will be closely monitored. The second highest ranking could be linked to oxygen ingress in the pre-anoxic zone during mixer maintenance; the plant has been advised to minimize airflows into the pre-anoxic zones.

Elmira WWTP Results
Model iterations improved the goodness of fit from 43.8% to 64.9% SSX explained (Figure 11). Key improvements were achieved by cleaning data and removing variables without relevant information. The largest improvement was from removing the datasets where the performance was disrupted during MABR commissioning and MABR aeration system failure due to cold weather. Results indicate relatively low N2O emissions from the bioreactors downstream of the MABR with occasional spikes, with the average N2O EF at 0.42%.

Diagnosis of the results showed aeration as the main contributor to N2O accumulation and improvements have been recommended. A well-operated MABR seems to keep the downstream N2O emissions low, however, the MABR off-gas could contribute an additional 30% to the N2O emissions based on limited off-gas data. Other N2O peaks could be linked to wet weather events and it has been recommended to investigate controls and process optimization during and after such events.

Summary and Next Steps
The hybrid models were able to explain 65 to 70% of the measured N2O and accurately predicted the trends and diurnal variations. The model allows for multivariate analysis to identify key operational parameters with the highest importance on the measured N2O, which combined with process knowledge, enables extraction of actionable information to develop potential operational strategies to mitigate N2O emissions. At the Duffin Creek WPCP, recommended improvements to the aeration control system will be implemented in spring 2025 together with ammonia-based aeration control, providing the opportunity to optimize aeration energy efficiency while minimizing process N2O emissions; some of the mitigation insights identified by the model will be tested.

This paper was presented at WEFTEC 2025, held September 27-October 1, 2025 in Chicago, Illinois.

Presentation time

16:00:00

16:15:00

Session time

15:30:00

17:00:00

SessionDecarbonizing Water: Mathematical Modeling and Digital Twins to Reduce N2O Emissions from WWTP

Session locationMcCormick Place, Chicago, Illinois, USA

TopicProcess Control and Modeling

Author(s)

Shen, Emma, Miletic, Ivan, Rieger, Leiv, Brandimarte Molleta, Lucas, Flores, Jesus, Green, Joe, Medd, Jeff

Author(s)E. Shen1, I. Miletic1, L. Rieger1, L. Brandimarte Molleta1, J. Flores1, J. Green2, J. Medd3

Author affiliation(s)Jacobs1, Regional Municipality of Durham2, Regional Municipality of Waterloo3

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Sep 2025

DOI10.2175/193864718825159915

Volume / Issue

Content sourceWEFTEC

Word count11

Improving Aeration Controls While Being Limited by Blower Flexibility

Abstract

Background
The Agua Nueva WRF (Figure 1) was designed as a scalping plant with a capacity of 32 MGD currently receiving an average daily flow of 25-28 MGD, following a distinct daily pattern. Secondary treatment includes 4 parallel basins in a 3-pass step-feed, 5-stage Bardenpho bioreactor system, followed by secondary clarifiers, tertiary disk filters and is disinfected using chloramines (Johnson et al. 2014). The objectives of the aeration controls were to manage the extreme dynamics of the influent flow and loads, and control ammonia to a band of 1.5 ±0.5 mgN/L to minimize external ammonia dosage for the chloramination system and the dosage of sodium hypochlorite.

The A zones (Figure 2) were designed to carry out most of the nitrification and handle the load dynamics via a high-low feedforward controller based on influent flow or ammonia load. The downstream B and C Zones of each step-feed pass were designed to adjust aeration intensity based on an ammonia feedback controller such that the Disc Filter effluent maintains the targeted ammonia range. Additional coarse bubble diffusers were installed in the A zones to supplement the oxygen supply of the fine bubble aeration in the 30 ft deep tanks.

Problem Statement
The steep influent load changes (Figure 3) require a faster reaction in aeration intensity than a feedback controller could deliver.

Triggered by insufficient nitrification performance, a study identified the likelihood of Quaternary ammonium compounds in the influent inhibiting nitrification. Even at a higher SRT additional aeration capacity was required to fully compensate the slower nitrification rates. The result was that the B and C Zones had to be aerated to a higher DO setpoint than originally designed. Pushing more air through the steep diffuser tapering caused significant pressure drops and required a higher system pressure. This issue was exacerbated by turbo blowers with limited turn-down capabilities. At high pressure, the surge margin restricted the available speed range, resulting in frequent blower cycling. At low pressure, gaps in airflow delivery also caused blower cycling. The need for higher pressure was further aggravated by aging diffusers. The inability of the blowers to maintain the required higher pressure led to drops in DO concentrations and significant ammonia breakthroughs.

The original controls for feedforward were implemented as high-low DO controllers and the coarse bubble aerators were switched on or off according to the fine bubble airflows. This caused additional disturbances to the very sensitive overall aeration system and often triggered blower cycling. Over the years, several control workarounds were implemented to deal with the blower issues but were difficult to follow. One key aspect of the redesign was to inform operators of the active controls and their performance.

Control System Redesign
The conceptional design uses a flow or ammonia load feedforward controller for the calculation of DO setpoints for the A Zones, whereas an ammonia-based feedback controller feeds a DO setpoint to the B and C Zones. A key improvement was the addition of secondary integral windup protection (SIWP) (Carlsson et al. 2017) to prevent that an underlying control loop causes integral windup in the outer loop. This is especially important in situations such as at Agua Nueva with limited abilities to increase system pressure or situations where the controller setpoints cannot be reached.

To coordinate fine and coarse bubble aeration in the A Zones, a control concept is applied that uses the filtered fine bubble airflow as input into a proportional coarse air control logic with bounds. Another goal was to redesign the control system for seamless integration with Jacobs' digital twin-based decision support system (Johnson et al. 2024).

Results Figure 4 shows the ammonia concentration at the end of aeration basin 1 before and after the control improvements. The ammonia peaks have been eliminated by the control improvements and the ammonia setpoint is maintained in both directions resulting in better water quality and lower chemical and energy consumption.

Figure 5 shows the improvements from the new coarse air bubble controller and Figure 6 of the feedforward flow controller. Both controls smoothen out the response and stabilized the overall aeration system performance. The SIWP prevented integral windup accumulating in the controller cascade when the control loop setpoints could not be reached (Figure 7).

Figure 8 shows an example of a faceplate providing insights to operators on the functioning, status, and performance of the aeration control system.

Broader Impact
The project improved an existing aeration control system by thoroughly analyzing the existing system constraints and redesign accordingly. The constraints made it necessary to develop and implement control solutions with smooth responses to load changes so that the control outputs would not cause disturbances themselves. A feedforward controller targets the reduction of dynamics, so that the ammonia feedback controller can deliver the targeted tight ammonia range. Major performance improvements are due to introducing secondary integral windup protection.

Special attention has been put on providing insights and access to operators via graphical user interfaces. The findings are easily transferrable and are currently applied to other aeration projects.

This paper was presented at WEFTEC 2025, held September 27-October 1, 2025 in Chicago, Illinois.

Presentation time

09:00:00

09:15:00

Session time

08:30:00

10:00:00

SessionOvercoming Challenges to Implement Advanced Aeration

Session locationMcCormick Place, Chicago, Illinois, USA

TopicProcess Control and Modeling

Author(s)

Rieger, Leiv, Papukchiev, Uri, Gallien, Dennis, Mason, Timothy, Johnson, Bruce

Author(s)L. Rieger1, U. Papukchiev1, D. Gallien1, T. Mason1, B. Johnson1, , , , , , , ,

Author affiliation(s)Jacobs Solutions Inc.1

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Sep 2025

DOI10.2175/193864718825159946

Volume / Issue

Content sourceWEFTEC

Word count10

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