Baffled clarification basin hydrodynamics and elution in a continuous time domain

Li, Haochen; Spelman, David; Sansalone, John J.

doi:10.1016/j.jhydrol.2021.125958

Cited by 14 publications

(5 citation statements)

References 66 publications

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“…In some cases, the complex in-situ basin hydraulic loading can deviate significantly from design flowrates that are estimated based on simplified and/or lumped drainage calculations. As a result of these confounding conditions, a basin responds at a lower and non-stationary level of performance ( 6 ). In these situations, impaired basins need to be properly retrofitted to restore the treatment functionality.…”

Section: Resultsmentioning

confidence: 99%

“…Impaired basin retrofit by approaches of expanding geometry, baffling without expanding basin, and baffling with expanding basin. The mean wet season flowrate is 0 . 1 m 3 / s based on 12-month field monitoring ( 6 ) for this basin and a medium particle size distribution is considered for this result. The gray circle markers illustrate the evaluated designs in the non-linear evolutionary optimization by DeepXtorm.…”

Section: Resultsmentioning

confidence: 99%

“…A monitored basin system retrofit design based on CFD for an airport in southwest Florida (APF) is also considered in this study. The median value of the mean wet season flowrate for this basin is 1 . 7 × 3 m − 3 / s ( 6 , 19 ).…”

Section: Methodsmentioning

confidence: 99%

“…1. Basin design or retrofit optimization requires using higher-fidelity simulation tools to resolve the basin transport fate dynamics (6). Existing models such as residence time (RT) and continuous stirred-tank reactor (CSTR) (embedded in many existing tools such as the Stormwater Management Model [SWMM]), a common and comprehensive model, are lower-fidelity sub-models.…”

mentioning

confidence: 99%

“…Once the ML model is developed and tested, the ML model can be applied to predict the design performance (i.e., PM separation or removal), given the design configuration. The forward execution of the ML model is very efficient and completed within milliseconds on a personal laptop 6…”

mentioning

confidence: 99%

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Extensibility of a Machine Learning Model for Stormwater Basin Design and Retrofit Optimization Through a User-Friendly Web Application

Spelman

Sansalone

2023

Transportation Research Record: Journal of the Transportation R

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To sequester particulate matter (PM) and chemical loads, stormwater basins are a common infrastructure component of transportation land use systems in a planning/design or a retrofit phase. Basin design relies on residence time (RT), capacity–inflow ratio (CIR), and continuous stirred-tank reactor (CSTR) concepts to provide presumptive guidance for load reduction. For example, 14-day (or 21-day) RT is presumed to provide load reduction for total suspended solids (TSS) (e.g., commonly 80%), and for total phosphorus (TP) (e.g., commonly 60%). Despite such guidance, most existing basins are impaired—not meeting presumptive guidance, whether for TSS, nutrients, or chemicals (e.g., metals). RT guidance results in high initial basin design costs (land and construction). For impaired basin retrofit, RT guidance generates a significantly higher cost (area/volume increase), if such a retrofit is even feasible considering proximate infrastructure constraints. This study presents a computational fluid dynamics (CFD)-machine learning (ML) augmented web application, DeepXtorm, for cost–benefit optimization of basin design and retrofit. DeepXtorm is examined against basin costs from as-built data, RT, CIR, and CSTR (deployed in the Storm Water Management Model [SWMM]) over a range of load reduction levels of TSS (60%–90%) and TP (40%–60%). For a given load reduction requirement, DeepXtorm demonstrates up to an order of magnitude (or more) lower basin cost compared with RT, CIR, and CSTR models, and greater than an order of magnitude compared with as-built cost data. DeepXtorm represents a more robust and cost-effective tool for stormwater basin design and retrofit.

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Section: Resultsmentioning

confidence: 99%