Prediction of dissolved oxygen concentration along sanitary sewers

Matos, José; Sousa, E. Ribeiro de

doi:10.2166/wst.1996.0592

Cited by 7 publications

(5 citation statements)

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“…The general case in which sulfide stripping would appear to be the dominant phenomenon is in partly full sewers with low d / D and turbulent flow, in which the water is in most cases fully aerobic (Matos and Sousa 1996;Hvitved-Jacobsen 2002). In such cases the following conditions can be assumed: a.…”

Section: Theory and Model Derivationmentioning

confidence: 99%

Modeling Hydrogen Sulfide Emission Rates in Gravity Sewage Collection Systems

Lahav

Shavit

et al. 2004

J. Environ. Eng.

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The factors affecting sulfide buildup in gravity sewers are complex, consisting of biological and physical processes, both in the aqueous and the gas phases of the sewer. The rate of each of these processes varies (among other parameters) according to flow characteristics, temperature, and pH. Under fast and turbulent flow conditions, the stripping of hydrogen sulfide into the gas phase may become the dominant process. The paper presents a semiempirical approach to the problem of quantifying hydrogen sulfide emission rates in sewers. Kinetics of hydrogen sulfide emission as a function of hydraulic parameters was measured in the laboratory using methods adopted from flocculation theory. A flocculation unit was used to impart a selected velocity gradient ͑G͒ into the water, and sulfide concentration was measured with time. The process was repeated for a number of G values. Regression analysis was then used to fit the rate of hydrogen sulfide emission equation against G. An equation was developed linking G to H L (head loss) in sewers assuming plug flow conditions. The hydraulic model and the kinetic model were linked (via G) to give the desired rate equation for hydrogen sulfide emission along a sewer line. The model was used to predict H 2 S emission from a uniform flow sewer and the effect of parameters such as pH, sewer slope and degree of fullness was studied. As expected, results show that low pH, high slope, and low degree of fullness enhance emission rates. Reasonable agreement was attained when the model output was compared with measured results from a field test sewer in Virginia, South Africa, under conditions where sulfide stripping was the rate-dominant process.

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Section: Theory and Model Derivationmentioning

confidence: 99%

Modeling Hydrogen Sulfide Emission Rates in Gravity Sewage Collection Systems

Lahav

Shavit

et al. 2004

J. Environ. Eng.

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“…To date, forecasts of aquatic systems have mostly relied on process‐based models and statistical models. These models have enabled managers and other partners to anticipate future changes in water temperatures (Abdi et al, 2020; Thomas et al, 2020), dissolved oxygen concentrations (Matos & de Sousa, 1996), and streamflow (Block et al, 2009; Hansen et al, 2019; Turner et al, 2020). Process‐based models define relations between driver data and the variable being forecasted a priori, whereas statistical models discover relations between driver data and the target variable during a model training phase with a few simple assumptions about model structure and distributions.…”

Section: Introductionmentioning

confidence: 99%

Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Zwart

Oliver

Watkins

et al. 2022

J American Water Resour Assoc

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Deep learning (DL) models are increasingly used to make accurate hindcasts of management‐relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real‐time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process‐guided DL and DA approach to make 7‐day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1‐day‐ahead and 1.4 to 1.9°C for 7‐day‐ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process‐guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision‐making.

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“…To date, forecasts of aquatic systems have mostly relied on process-based models and statistical models. These models have enabled managers and stakeholders to anticipate future changes in water temperatures (Thomas et al 2021), dissolved oxygen concentrations (Matos and de Sousa 1996;Abdi et al 2020), and streamflow (Block et al 2009;Hansen et al, 2009;Turner et al 2020). Process-based models define relations between driver data and the variable being forecasted a priori, while statistical models discover relations between driver data and the target variable during a model training phase assuming a few simple assumptions about model structure and distributions.…”

Section: Introductionmentioning

confidence: 99%

Near-term forecasts of stream temperature using process-guided deep learning and data assimilation

Zwart¹,

Oliver²,

Watkins³

et al. 2021

Preprint

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Near-term forecasts of environmental outcomes can inform real-time decision making. Data assimilation modeling techniques can be used for forecasts to leverage real-time data streams, where the difference between model predictions and observations can be used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided deep learning and data assimilation approach to make 7-day forecasts of daily maximum water temperature in the Delaware River Basin. Our modeling system produced forecasts of daily maximum stream temperature with an average root mean squared error (RMSE) from 1.2 to 1.6°C for 1-day lead time across all sites. The data assimilation algorithm successfully adjusted the process-guided deep learning model states and marginally improved forecast performance when compared to forecasts produced using the process-guided deep learning model alone (7-13% lower RMSE with the data assimilation algorithm). Our model characterized forecast uncertainty relatively well as 57-80% of observations were within 90% forecast confidence intervals across all sites and lead times, and the uncertainty associated with our forecasts allow managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of deep learning models to be applied to various prediction problems shows promise for using these types of models to forecast many other important environmental variables and aid in decision making.

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Prediction of dissolved oxygen concentration along sanitary sewers

Cited by 7 publications

References 0 publications

Modeling Hydrogen Sulfide Emission Rates in Gravity Sewage Collection Systems

Modeling Hydrogen Sulfide Emission Rates in Gravity Sewage Collection Systems

Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions

Near-term forecasts of stream temperature using process-guided deep learning and data assimilation

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