Johnson, Bruce

Mr. Bruce Johnson is a wastewater technology senior fellow with Jacobs located in Denver. He has been doing wastewater treatment design for over 35 years, the last 28 of which has been with CH2M/Jacobs where he has held the roles of wastewater process and simulation global technology leader. He has been active outside of Jacobs both in WEF and IWA. Within WEF, he has led and contributed the development of a number of MOPs and helped found the MEGA group. Within IWA, Mr. Johnson is a past scientific chair of WWTmod and a founding member of the IWA Design and Operations Uncertainty Task Group.

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

Clean Water Services ODIN Pilot Case Study

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 CWS are to: Predict the expected diurnally varying plant influent orthophosphate loading over the following 24 hours. Recommend an alum dosing set point to maintain a requested primary effluent orthophosphate load. A secondary goal is the additional information provided on parameters like influent ammonia. For the CWS application, the ODIN systems appears to be a promising opportunity to improve plant operation using digital twin technology.

A hybrid (machine learning + mechanistic modelling) controller called ODIN has been developed through WRF5121 and is currently being tested at the Clean Water Services Durham WRRF. The system recommends for primary alum addition setpoints based on forecasted phosphorus loads and target bioreactor ortho-phosphate mass loading targets. The ODIN system appears to be a promising opportunity to improve plant operation using digital twin technology. This paper summarizes the status of the CWS pilot.

SpeakerMenniti, Adrienne

Presentation time

15:35:00

15:50:00

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)

Menniti, Adrienne

Author(s)A. Menniti 1; A. Menniti 1 ; B. Johnson 2; K. Lesnik 3; C. Yang 4;

Author affiliation(s)Clean Water Services 1; Clean Water Services 1 ; Jacobs 2; Jacobs 3; 4;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159116

Volume / Issue

Content sourceWEFTEC

Word count8

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

Description: Design Development and Startup of an Aerobic Granular Sludge SBR (Nereda™)...

Design Development and Startup of an Aerobic Granular Sludge SBR (Nereda™) Under Cold and Weak Wastewater Conditions

Abstract

United Utilities (UU) is the UK’s largest listed water and wastewater company, serving over 7 million customers in the Northwest of England; including Liverpool and Manchester. In June of 2015, UU awarded the upgrade of the Morecambe Wastewater Treatment Works (WwTW) to C2V+, which is a joint venture between Jacobs Engineering (CH2M at the time) and Volkerstevin. The Morecambe WwTW influent is typically cold (minimum 8 C) and weak (design COD of 251 mg/L). The influent design average flow was 19,700 m3/d (5.2 MGD) with a peak flow of 62,600 m3/d (16.5 MGD). A Nereda™ aerobic granular sludge sequencing batch reactor system was constructed and started up during the Fall of 2019. The system exhibited good granule development and met all effluent criteria for the facility. The performance indicates that AGS systems are applicable to weak and cold wastewaters.

SpeakerJohnson, Bruce

Presentation time

13:30:00

13:50:00

Session time

13:30:00

15:30:00

SessionGranular Developments: Advancements in Aerobic Granular Sludge Formation

Session number206

TopicMunicipal Wastewater Treatment Design, Nutrients, Research and Innovation

Author(s)

R. TsotsosB. JohnsonJ. BlackE. FoxL. MansellB. Oliver

Author(s)R. Tsotsos1; B. Johnson1; J. Black3; E. Fox2; L. Mansell3; B. Oliver4;

Author affiliation(s)Jacobs1; Volkerstevin2; United Utilities 3; RHDHV4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2020

DOI10.2175/193864718825157795

Volume / Issue

Content sourceWEFTEC

Word count18

Designing for Humans

Abstract

The Water Research Foundation Project 5121 is titled Development of Innovative Predictive Control Strategies for Nutrient Removal 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 philosophy behind the ODIN User Interface is to make this tool easy to use for operations staff. To accomplish this objective, we’ve focused on two goals with the user interface design: 1. Keep the user priorities and mind set in mind at every step of the user interface design. 2. As engineers, we need to remember to keep the interface easily accessible and simple. Any new way of doing things must make the life of the operator better in some way. Without that, even the best ideas will likely fail at widespread adoption

A hybrid (machine learning + mechanistic modelling) controller developed through WRF5121 and named ODIN has been developed and is currently being tested at four different WRRFs. The philosophy behind the ODIN User Interface is to make this tool easy to use for operations staff. Every step of the user interface development process has kept user priorities and needs at the forefront. This paper reviews the ODIN approach to user interface development.

SpeakerMenniti, Adrienne

Presentation time

13:35:00

13:50:00

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)

Menniti, Adrienne

Author(s)A. Menniti 1; B. Johnson 2 ; A. Menniti 1;

Author affiliation(s)Clean Water Services 1; Jacobs 2 ; Clean Water Services 1;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159078

Volume / Issue

Content sourceWEFTEC

Word count4

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: 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

Description: Water Reuse and Recovery Facility Connected Digital Twin Case Study: Singapore...

Water Reuse and Recovery Facility Connected Digital Twin Case Study: Singapore PUB’s Changi WRP Process, Control, and Hydraulics Digital Twin

Abstract

Jacobs is collaborating with Singapore Public Utilities Board (PUB) to create a digital twin (DT) of Changi Water Reclamation Plant (CWRP). CWRP is currently averaging 920,000 m3/d (243 MGD) of influent flow. In the context of water resource recovery facilities, a digital twin is includes a process model of the facility that automatically takes live data from the facility. The DT being developed for PUB also includes a first-of-its-kind simulation of control systems and liquid hydraulics integrated with process under one platform. The digital twin uses approximately 1200 data tags and includes three primary functionalities. First, comparison between model predictions and measured data highlights area’s for attention by operations and maintenance staff, thus minimizing their efforts. The second use of the calibrated model includes evaluation of various operational scenarios, both operator defined, and fixed. This allows operation staff to better understand the possible impacts of either unexpected or planned outages on the facility. Lastly, the auto-calibrated model will be used to predict the likelihood of future events at the wastewater facility up to 5 days in the future, a “wastewater weather forecast”.

Jacobs is collaborating with Singapore Public Utilities Board (PUB) to create a digital twin (DT) of the 920,000 m3/d (243 MGD) Changi Water Reclamation Plant. The digital twin is a process/hydraulics/control model of the facility which automatically takes live data. The PUB DT uses ≈1200 data tags and includes three functionalities; 1) a comparison between the model and measured data highlights area’s for attention by O&M staff, thus minimizing their efforts. 2) Evaluation of scenarios, which allows operation staff to better understand impacts of outages. 3) The model is used to predict performance up to five days in the future.

SpeakerJohnson, Bruce

Presentation time

11:00:00

11:15:00

Session time

11:00:00

12:30:00

SessionDigital Twins: Trial by Jury

Session number202

TopicFacility Operations and Maintenance, Intelligent Water, Research and Innovation, Resilience, Disaster Planning and Recovery

Author(s)

Bruce Johnson

Author(s)B.R. Johnson1; R. Kadiyala2; G. Owens3; Y. Mak4; P. Grace4; C. Newbery4; S. Sing4; A. Saxena4; J. Greene1;

Author affiliation(s)Jacobs, Denver, CO1Jacobs, Oakland, CA2Jacobs, San Antonio, TX3Jacobs, SG4

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2021

DOI10.2175/193864718825158033

Volume / Issue

Content sourceWEFTEC

Word count21

Description: Why Does Proper Simulation Matter In The Acronym Soup of Biofilm Systems? AGS, IFAS,...

Why Does Proper Simulation Matter In The Acronym Soup of Biofilm Systems? AGS, IFAS, MBBR

Abstract

INTRODUCTION
Advanced nutrient management approaches are increasingly relying on biofilm systems to achieve their goals. These include such processes as aerobic granular sludge (AGS), both batch and continuous flow, the mobile carrier (MOBâ„¢) process that attempts to simplify AGS, moving bed biofilm reactor (MBBR) technologies for Anammox based systems in both main and sidestream processes, and IFAS technologies for low temperature or intensified nitrification. Many practitioners do not have available the knowledge or mentoring needed to properly model such biofilm systems, and thus often rely on empirical approaches or vendor provided sizing for such systems. These empirical approaches can result in either oversizing or under sizing biofilm-based systems, both of which can have negative impacts upon the adoption and use of biofilm systems to benefit the public and environment. This paper discusses the capabilities, considerations, and implications of advanced biofilm system simulation with the goal of providing readers with the information they need to understand what biofilm simulation results actually mean.
METHODS
Simulations of the above-described biofilm systems were completed in Dynamita's Sumo21Â® to illustrate the impacts of various modelling settings and structure upon the simulation results. For continuous flow systems an identical primary and MLE bioreactor system was used to compare various approaches to biofilm modelling. For sequencing batch reactor (SBR) systems, the base Sumo AGS reactor was used, as well as Jacobs SBR AGS model. The model structures of these approaches are compared against each other and the structures used by Envirosim's Biowin.
RESULTS AND BENEFITS
All currently implemented biofilm models in commercial simulators use a layer approach where a biofilm is modeled as a variable number of CSTRs vertically along the depth of the biofilm acting as different layers (Figure 1), with the needed diffusion and mixing between layers. The geometry of these layers is generally rectangular, i.e. the area of the biofilm is the same on the bottom as it is on the top, but with the recent interest in granular activated sludge, the geometry can also be implemented as spherical, where the area of the bottom layer is smaller than the top. Two of the key parameters in biofilm modelling is the selected number of layers, as well as the mass transfer boundary layer (MTBL) thickness. The MTBL thickness is variable depending on the shear rate present in the reactor being modelled, generally the MTBL is considered to be thinner in aerated zones and thicker in the non-aerated. The selection of the number of layers to use in the model is very dependent on the desired accuracy of the results balanced against the speed of the simulation. Figure 2 shows the impact of the number of modeled biofilm layers on the ammonia concentration in the IFAS tank, with all other parameters being held constant. Another significant consideration in modelling biofilm systems is the physical geometry of the biofilm reactor. Almost all current simulators use a MBBR model where the biofilm is retained within the CSTR and only suspended growth transfers between reactors. This assumption works relatively well in traditional biofilm reactor systems like IFAS and MBBRs, but if there is a plug flow through the biofilm bed (as in AGS SBRs) or that the biofilm transfers between CSTRs, as in continuous flow AGS systems, this assumption is no longer correct. To illustrate this point, a continuous flow AGS system was modeled as a series of MBBRs with retained biofilms, and another was modeled with the mobile carrier approach where biofilm granules are allowed to flow freely throughout the process. The effluent ammonia concentrations produced by a typical diurnal variation is shown in Figure 3. It can be seen that the modeling results of these two approaches provide significantly different results, with the mobile carrier model structure showing improved ammonia removal. This is a result of the mobile carrier having a higher nitrifier biomass fraction than the MBBR reactor in the last aerobic zone. The only commercial simulators that allow transfer of biofilms between zones are Sumo and SIMBA# by ifak. Sumo uses an inert spherical core and biofilm model (Mobile Carrier) with a variable number of fixed thickness layers. This core is allowed to flow between units, and thus transfer the biofilm between CSTRs. SIMBA# uses a detailed geometry model to adapt the model to any biofilm type and shape with a variable thickness biofilm model. A special Mobile Carrier Biofilm Reactor (MCBR) block library has been developed for modeling movement of carriers within a plant. AGS can be modeled with a specific AGS reactor block or with the mobile carrier library. The current intense industry interest in aerobic granular sludge has resulted in all the simulator providers developing AGS reactor models. It is important to understand the differences between these models from a functional perspective. Three are compared in Figure 4: 1) Jacobs AGS Model: Full hydrodynamic model with effluent breakthrough and bed growth calculations and mobile carrier model. 2) Biowin AGS Model: Single MBBR AGS model followed by a plug flow clear liquid reactor. Does not capture plug flow through reactor bed. Will result in differences from actuality, and 3) Sumo AGS Model: Single MBBR AGS Model, with specified effluent and sludge TSS. Approximates effluent quality by fixing the decant at the concentrations present at the start of feed/decant cycle. Each of these reactors have varying levels of functionality that are appropriate for different modelling goals.
CONCLUSIONS
Biofilm based systems are rapidly becoming core to achieving intensification and efficiency goals in our industry and proper simulation of them will encourage their use and capabilities. The structure and setup of the biofilm simulation to achieve the simulation goals is of critical importance and often requires the users to understand the deeper model structures for proper usage. This paper will compare and contrast the various model structures to illustrate the impacts of these decisions for engineers and utilities.

Biofilm based systems are rapidly becoming core to achieving intensification and efficiency goals in our industry. Proper simulation of them will encourage their use and capabilities. The structure and setup of the biofilm simulation goals is of critical importance and often requires the users to understand the deeper model structures for proper usage. The proper setup falls into two categories; 1) The biofilm model itself, and 2) The bioreactor model that uses the biofilm model.

SpeakerJohnson, Bruce

Presentation time

13:30:00

13:55:00

Session time

13:30:00

15:00:00

TopicAdvanced Level, Municipal Wastewater Treatment Design, Nutrients, Research and Innovation

Author(s)

Johnson, Bruce

Author(s)Bruce R. Johnson1

Author affiliation(s)Jacobs, Englewood, CO1