Kestel, Steven

Steven Kestel has spent the last 19 years working as a process and controls engineer in the wastewater treatment industry. Since joining APG Neuros 2...

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About Kestel, Steven

Steven Kestel has spent the last 19 years working as a process and controls engineer in the wastewater treatment industry. Since joining APG Neuros 2 years ago, he has lead development of APG Neuros' Model Predictive Control based aeration control system. He has participated in control system installations at global facilities ranging in size from 1 MGD to greater than 750 MGD. His primary interests are modeling, simulation, and automation of the wastewater treatment process and how to incorporate these fields into practical control systems to improve process performance and efficiency.

Kestel, Steven

Titles from this speaker

Evaluating Model Predictive Control for Aeration System Efficiency and Reliability

Abstract

Introduction To meet water quality regulations and the needs of a growing service area, Metro Water Recovery (Metro) is partnering with APG-Neuros to install and pilot APG-Neuros' Advanced Aeration Control (AAC) system at the Northern Treatment Plant (NTP). AAC uses state-of-the-art model predictive control (MPC) algorithms to improve controller performance compared to traditional control methods. The goal of piloting this advanced control system was to demonstrate the ability of the AAC to meet valve position, airflow, dissolved oxygen (DO), and effluent ammonia setpoints as determined by the model outputs. This presentation will provide results of this pilot including the ability of the controller to meet the desired setpoints during two phases: (1) the initial commissioning and operation in the spring/summer of 2023 and (2) following the commissioning of an additional bioreactor in the winter/spring 2024. NTP Site and Process Description The NTP is a regional water resource recovery facility located in Brighton, Colorado, operated by Metro since 2016. With an average influent flow of 7.4 million gallons per day (mgd) in 2023, the NTP uses a step-feed, five-stage Bardenpho-type process to achieve a low effluent total nitrogen concentration. The plant has operated two bioreactors, and an additional bioreactor has been brought online to meet an expected 4.5-mgd increase in flows in 2024. Aeration to eight bioreactors is supplied by six APG-Neuros NX350-C100 High Speed Turbo blowers on a common header, which also supplies air to NTP's Post Digestion Solids Treatment (PDST). Historically, the facility has used a Proportional-Integral-Derivative (PID) based DO control with user-defined DO setpoints to control the bioreactors' aeration. With this complex flow system containing 24 modulating valves requiring phosphorus removal, low nitrogen limits, and increasing flows and loads, the PID controller struggles to maintain consistent control. Controller Description The new APG Neuros AAC consists of several cascaded control loops working in series to ensure effluent ammonia discharge targets are achieved using the lowest airflow possible. Ammonia-based aeration control (ABAC) logic varies the DO setpoints based on the ammonia loading. The blower speed(s) are then adjusted to produce the required amount of total airflow. All control loops, including the ABAC loop, use MPC instead of PID logic. An MPC controller consists of two parts, a predictor and an optimizer. Methodology Each aeration zone was calibrated with all eight aeration zones being controlled individually by the AAC to provide the model with historical data on valve reaction time and DO response. From there, the AAC controlled the bioreactor in a two-setpoint manner, with an ammonia setpoint provided for the first pass and a lower setpoint in the second pass. Due to influent loading increases and mechanical aeration restrictions that adversely impacted single-setpoint control, phase 1 testing only used two-setpoint control. During phase 1, Bioreactor 4 was placed into AAC mode from April to August 2023. The performance of Bioreactor 4 was compared against its own historical operation under DO control and against Bioreactor 2. Effluent ammonia target concentration and air demand were the primary performance criteria. Data was analyzed for a 24-hour period on July 29, 2023 at one-minute intervals and compared to data from the DO control strategy used at NTP on February 27, 2023. A comparison of DO, effluent ammonia, valve position, and airflow setpoints was conducted to determine the accuracy and stability of the AAC in maintaining the determined setpoints. During phase 2, Bioreactor 4 will be operated side-by-side with Bioreactor 3 for three months to compare AAC with DO control using a single ammonia setpoint for full bioreactor aeration control. Results and Discussion The initial assessment of the AAC performance showed that the airflow and valve position accurately operated at the determined setpoint in AAC operation (Figure 1). DO setpoint tracking and stability also improved with AAC control compared to DO control (Figure 2). During AAC testing, there was a period of significant DO setpoint fluctuations when the system was able to accurately aerate to maintain the DO setpoint despite the significant change in the AAC-generated DO setpoint. This speaks to the AACs ability to effectively predict DO setpoints and adjust the system's aeration to meet the aeration demands. The airflow and valve operation and setpoints with AAC are smoother (less erratic) and do not change drastically during the day. This is important as constant and significant changes to the valve positions can cause valve wear that can lead to premature failure. For Bioreactor 4 Pass I, the process performance, when comparing influent ammonia and effluent ammonia, was significantly better with AAC as compared to DO control. Overall, the system stability and predictability with AAC significantly improved equipment operation and overall process performance as shown by the Pass I effluent ammonia concentration (Figure 3). Further investigation and evaluation in a Phase 2 evaluation will provide a more comprehensive picture into how the dynamics of the system affect aeration controls and controller performance.

The ABAC implementation and optimization at the Metro Water Recovery (Metro) Northern Treatment Plant (NTP) focused on the controllers' ability to accurately control the process to the calculated setpoints. Testing of APG-Neuros' ammonia-based Advanced Aeration Control (AAC) system showed that the system was able to efficiently control the aeration demands to meet dynamic DO setpoints generated using model predictive ABAC loops.

SpeakerAlmaraz, Nohemi

Presentation time

14:30:00

14:50:00

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)

Almaraz, Nohemi, Freedman, Daniel, Kestel, Steven, Cowan, Kimberly, Monsrud, Joshua, Elias, Carson, Cooney, Sean

Author(s)N.W. Almaraz1, D. Freedman1, S.M. Kestel2, K. Cowan1, J. Monsrud3, C. Elias3, S. Cooney1

Author affiliation(s)1Metro Water Recovery, CO, 2APGN Inc, QC, 3Metro Water Recovery

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159708

Volume / Issue

Content sourceWEFTEC

Word count11

Market Analysis of WRRF Real-Time, AI-Driven Optimization Solutions

Abstract

Introduction Water resource recovery facilities (WRRF) face continuing challenges to meeting tighter effluent limits while simultaneously managing or ideally reducing operating costs, energy consumption, and greenhouse gas emissions. To meet strict effluent limits, more complex treatment processes are being installed, which require tight control. These challenges are exacerbated by the loss of experienced operators, with the number of operators projected to decline by 6% by 2032 (Bureau of Labor Statistics, 2023). WRRF artificial intelligence (AI) driven optimization solutions are well positioned to address these challenges, especially to reduce operating expenditures (OPEX) with a focus on energy efficiency, improved biological stability, and even increased plant capacity to some extent. AI-driven solutions can also play a major role in process GHG emissions reduction such as nitrous oxide. Such real-time optimizations relying on Model Predictive Control (MPC) have already provided tangible benefits to WRRFs via different solutions and packages, and as drivers are evolving in the water industry with more focus on 'doing more with less' digital approaches, there will be significant uptake in such solutions deployment by water utilities relying on the latest cloud computing and 'internet of things' (IoT) development. However, the promoted features and benefits can be daunting to understand to build case studies, and there is merit in unpacking such solutions in further detail for the industry's benefit. Therefore, the goal of this paper and presentation is to provide a comprehensive and objective comparison of some of the key WRRF optimization AI-driven optimization solutions currently available accompanied by relevant case studies. This market analysis will benefit water utilities in identifying solutions that best fit their needs and constraints. Each software package was evaluated based on several factors, including: process optimization features, treatment processes optimized, vendor category (SCADA, Water Technology, etc.), optimization software hosting solution and communication architecture, primary outcome such as OPEX reduction, capacity increase, compliance or emissions reduction, and advantages/disadvantages. Each WRRF has its own requirements, so there is no objective 'best' software as utility managers and operators need to select the best software package(s) for their specific circumstances. Methods For each evaluated software package, data was collected and organized from a variety of sources including vendor-supplied literature and websites, public domain technical literature, and the authors' experience. Table 1 summarizes a 'long list' of available optimization solutions that cover both model predictive control as well as advisory dashboards. From this long list, the market analysis then focused on the top seven (7) most prominent vendors with real-world experience with review of case studies and interviews with WRRF managers and operators where specific software packages have been installed. Key criteria used in the evaluation were: 1.Functionality: Evaluating what functions are present in the software, such as the unit processes the software can optimize and the methods used to perform that optimization 2.Performance: Evaluating the effectiveness and robustness of the software in achieving its optimization goals 3.Complexity: Evaluating the degree of difficulty in installing, configuring, and maintaining the software 4.User-friendliness: Evaluating the ease of use and intuitiveness of the software along with available support 5.Openness: Evaluating how easily the software can interface with other systems within a WRRF Results The initial long list of available optimization systems that were considered are summarized in Table 1 and evaluation of the 'top 7' AI-driven short-listed optimization systems in Table 2 with respect to process optimization features and Table 3 with respect to commercialization and solution architectures. Conclusions AI-driven optimization solutions can provide solutions for a number of challenges that WWRFs are facing today and in the near future. However, understanding the functionality, features, and benefits of these solutions can be a daunting task for most utilities. This work has shown the strengths, weaknesses, and suitability of AI-driven optimization solutions for different utility needs. Critically, these solutions are already providing tangible benefits to WRRFs by addressing some of the most pressing challenges facing the wastewater industry. By considering the needs of their individual facility, utility managers can utilize this market survey to assist them in choosing the most appropriate solution for their needs. By making this technology more comprehensible to a wide range of utilities, more WRRFs can take advantage of their benefits, improving the industry as a whole.

Water resource recovery facilities (WRRFs) face continuing challenges to meet tighter effluent limits while simultaneously optimizing process performance. This work provides a market analysis of AI-driven optimization solutions for WRRFs, with a focus on real-time plant optimization, Model Predictive Control (MPC) and advanced aeration control. Various solutions from multiple vendors were evaluated.

SpeakerKestel, Steven

Presentation time

09:00:00

09:20:00

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)

Kestel, Steven, Debruyne, Thomas, Pretorius, Coenraad, Hammoud, Omar

Author(s)S.M. Kestel1, T. Debruyne2, C. Pretorius3, O. Hammoud4

Author affiliation(s)1APGN Inc, QC, 2GHD, 3GHD, CA, 4APGN INC, QC

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2024

DOI10.2175/193864718825159518

Volume / Issue

Content sourceWEFTEC

Word count9

Base text size -

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