Dunlap, Patrick

Patrick Dunlap is a wastewater process engineer with Black & Veatch specializing in process modeling, aeration system design, and enhanced...

Titles from this speaker

Better Clarification from the Floor Baffle Up

Abstract

The Centennial Water and Sanitation District in the State of Colorado upgraded the Marcy Gulch Wastewater Treatment Plant as part of their Phase II Improvements Project to meet stricter regulatory requirements. Secondary clarification enhancements were made to help mitigate the four hydrodynamic phenomena that can negatively impact clarifier performance. The District implemented a McKinney feedwell floor baffle on all four secondary clarifiers to control density currents and improve performance, and sludge removal was converted to a spiral rake mechanism from suction draft tubes. Computational fluid dynamic modeling demonstrated improved hydrodynamics with the McKinney floor baffle design. Preliminary results show improved clarification, improved thickening and sludge blanket control, and reduced floc shearing in the modified clarifiers, with effluent turbidity generally lower than the old clarifiers. Overall, the McKinney floor baffle design has demonstrated the potential to optimize clarification in secondary clarifiers.

The secondary clarifiers at the Marcy Gulch Wastewater Treatment Plant were recently upgraded to include a McKinney feedwell floor baffle to optimize overall efficiency, stability, and effluent quality of the activated sludge process. Originally conceived in the 1970s, this simple clarifier design feature is largely underrepresented in the U.S. compared to European installations but can provide significant operational and performance benefits.

SpeakerChapa, Rebecca

Presentation time

11:00:00

11:30:00

Session time

10:30:00

12:00:00

SessionSecondary Clarification Advancements

Session locationRoom S401d - Level 4

TopicAdvanced Level, Municipal Wastewater Treatment Design

Author(s)

Chapa, Rebecca

Author(s)R. Chapa 1; P. Bong 2 ; R. Chapa 1; B. Jessee 3; P. Dunlap 4; J. Fitzpatrick 5; N. George 6;

Author affiliation(s)Black & Veatch, Denver, CO 1; Centennial Water & Sanitation District, Centennial, CO 2 ; Black & Veatch, Denver, CO 1; Black & Veatch 3; Black & Veatch 4; Black & Veatch 5; Centennial Water & Sanitation District, Centennial, CO 6;

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2023

DOI10.2175/193864718825159195

Volume / Issue

Content sourceWEFTEC

Word count8

Comparing Approaches to Probabilistic Design of ABAC Aeration Systems

Abstract

The traditional WRRF design approach has been to design for the combined maximum load of several constituents during worst-case environmental conditions. Additionally, factors of safety may be applied to account for the uncertainty of various design assumptions. This can lead to over sizing of infrastructure and equipment or under estimation of existing process capacity. Recognizing that aeration systems (blowers and oxygen transfer systems) are notorious for being oversized, a recent project at the Denver Metropolitan Wastewater Reclamation District (MWRD) Robert W. Hite Treatment Facility (RWHTF) developed a basis of design for an aeration system based on a probabilistic approach. By taking a probabilistic approach it should be possible to simultaneously account for actual system variability and assumed uncertainty, thereby limiting conservatism of traditional design. Monte Carlo (MC) analysis is a commonly applied probabilistic approach whereby variability or uncertainty of model inputs can be propagated through mechanistic models resulting in probability distributions as outputs; effectively creating probabilistic shell around deterministic models (Benedetti, 2009). Probabilistic design approaches have been taken in our industry (Belia, 2012; Fries, 2010; McCormick, 2007; Gray, 2012). McCormick (2007) proposes a 20% reduction in the design blower capacity was possible by taking an MC approach and Gray (2012) determined that a 27% reduction in blowers capacity was possible by using an MC approach when designing a system using ammonia-based aeration control (ABAC) system. With the publication of the DOUT STR in 2020, it is likely that these techniques will see more application. Despite the benefits, properly executing a Monte Carlo analysis can be challenging, with possible pitfalls to consider and unique challenges in interpreting results. In this technical assessment, an actual aeration study will be used as a lens to compare MC analysis with an alternative Brute Force (BF) search approach and to answer the question of how these different approaches impact resulting basis of design?

The following conference paper was presented at WEFTEC 2021, October 16-20, 2021. To read the full abstract, see "Abstract" tab below.

SpeakerDunlap, Patrick

Presentation time

16:22:00

16:37:00

Session time

16:00:00

17:30:00

SessionAmmonia-based Aeration Control: How to Make it Work

Session number313

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

Author(s)

Patrick Dunlap

Author(s)P. Dunlap1; D. Freedman1; L.S. Downing1; B.D. Shoener1; B. Wisdom2; L. Cavanaugh2;

Author affiliation(s)Black & Veatch, Denver CO1Denver MWRD, Denver CO 2University of Illinois At Urbana-Champaign, IL4City of Longmont WWTP5

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Oct 2021

DOI10.2175/193864718825158065

Volume / Issue

Content sourceWEFTEC

Word count10

Effluent Excellence: Unleashing Hybrid Model Magic at Fond du Lac

Abstract

Background and Objective:
Wastewater treatment facilities must meet stringent effluent water quality targets while managing higher flows from extreme weather conditions. They rely on activated sludge models (ASM) for design capacity evaluation (Henze et al., 2000). For smaller utilities, maintaining these models is challenging due to frequent updates, recalibration based on influent flow characteristics, input data quality issues, and manual scenario simulations. Recently, hybrid process models combining predictive machine learning (ML) for influent forecasting, automated data handling for ASM, and auto-calibrated simulations have been proposed to increase modeling tool adoption (Mannina et al., 2019; Zhao et al., 2020). Automatic data storage and processing remain challenging due to variability in lab methods, analyzer drift, non-standard naming conventions, poor data organization, and cybersecurity concerns (Demir & Szczepanek, 2017 and Newhart et al., 2019). This study explores hybrid modeling at the Fond du Lac Wastewater Treatment & Resource Recovery Facility (WTRRF) in Wisconsin. Key facts about the WTRRF (Figure 1):
- Rated for 12 mgd (~44,000 m3/day), serving a population equivalent of 60,000 people.
- Utilizes EBPR processes combined with Chem P removal for phosphorus trimming. Managed extreme flow conditions in 2024, while maintaining low effluent TSS and nutrient limits.
- By 2027, a 6-month mass load allocation equating to an average total phosphorus (Total P) concentration of 0.19 mg/L-P is anticipated. It is aimed to meet this goal while minimizing operational and maintenance costs for chemical phosphorus removal and future capital costs. Fond du Lac WTRRF collaborated with Black & Veatch and Maia Water to design and deploy a hybrid model optimizing operational decision-making around flow and load management.

Approach:
The cloud-based hybrid modeling approach for WTRRF included an ML model for primary effluent flow and load forecasting, followed by an ASM to estimate effluent quality and performance using the application package interface (API) for SUMO22 (Dynamita, France) (Figure 2). Process data was imported, supplemented with live weather data to generate a historical dataset for the ML algorithm. The dataset was used to auto-calibrate a 1-D secondary clarifier SUMO model (Figure 3) to match observed secondary effluent TSS. Next, future influent flows and concentrations were forecasted using XGBoost and input into a larger plant model (Figure 4) with calibrated parameters. The SUMO model simulations provided performance forecasting and soft sensor capabilities. The workflow's modularity enabled new optimization routines, updates, and adoption lab results at varying frequencies. It culminated in a hybrid modeling dashboard as a data-backed advisor to Fond du Lac, offering visualizations, optimization routines, sensitivity analyses, and model training options.

Findings & Significance:
Influent Forecasting: For a 7-day trend of 15-minute interval influent flow, a mean absolute percentage error (MAPE) of 26.9% was observed between forecasted and actual flows (Figure 5). A rain event far exceeded forecasts (Figure 6), impacting flow forecasting accuracy. The MAPE between forecasted annual flow relative to actual was 16.2% (Figure 7). A reasonable match was observed between influent total COD and total P forecasts. The MAPE for COD was 9.4% (Figure 8) and for TP was 7.5% (Figure 9). These forecasts indicate that operations at Fond du Lac WTRRF can be augmented with forecasts for improved decision-making. The current ML forecasting model will be optimized as more data becomes available.

Hybrid model: The forecasted secondary effluent TSS from SUMO matched well with observed data, with an MAPE of 12.58% and absolute differences within ±0.8 mg/L (Figure 10). Sensitivity analysis can demonstrate differences in forecasted effluent TSS quality with clarifiers in/out of service for maintenance planning. Forecasted effluent TSS supports improved decision-making around clarifier operations.

The hybrid model indicated better P removal from EBPR and Chem P versus observations (Figure 11). The difference underscores challenges in predictively modelling EBPR and Chem P at low OP concentrations due to relevant parameters' complexity. While the absolute error between observed and forecasted effluent OP was 0.05 mg/L-P, less than industry standard stop criteria of Â±0.5mg/L-P (IWA, 2013), a more accurate match is required. An effluent OP error correction model using XGBoost and several features from influent, operational, temporal, and model-derived data improved effluent OP forecasting. Model-derived features indicate P loading to carbon uptake ratio by polyphosphate accumulating organisms over relevant time scales. The error correction allowed for a simpler regression model factoring system loading history. Figure 12 shows testing data from this model that will be integrated with outlier detection in final deployment.

This approach unlocked soft-sensor capabilities from model results: model estimates for rbCOD in selector effluent were compared to observed settleability revealing potential correlations. Spikes in rbCOD from selector effluent led to a rise in SVI-30 (Figure 13). Further analyses are planned to reveal more correlations using soft-sensor values, opening opportunities to leverage state variables for making informed process decisions.

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

SessionData in Action! Data-Driven Optimization Models

Session locationMcCormick Place, Chicago, Illinois, USA

TopicProcess Control and Modeling

Author(s)

Gaidhani, Chinmay, Coffey, Carolyn, Emaminejad, Aryan, Dunlap, Patrick, Avila, Isaac, Lesnik, Keaton, Schoepke, Cody, Downing, Leon

Author(s)C. Gaidhani1, C. Coffey1, A. Emaminejad1, P. Dunlap1, I. Avila1, K. Lesnik2, C. Schoepke3, L. Downing1

Author affiliation(s)Black & Veatch1, Maia Analytica2, Fond du Lac Wastewater Treatment and Resource Recovery Facility (WTRRF)3

SourceProceedings of the Water Environment Federation

Document typeConference Paper

PublisherWater Environment Federation

Print publication date Sep 2025

DOI10.2175/193864718825159983

Volume / Issue

Content sourceWEFTEC

Word count11