Managing Municipal Wastewater Treatment to Control Nitrous Oxide Emissions from Tidal Rivers

Abstract

Waste load allocation management models were developed for controlling nitrous oxide emissions from a tidal river. The decision variables were treatment levels at wastewater discharging stations and the rate of upstream water release. The simulation model for N2O emissions from the river was embedded in the optimization model and the problem was solved using the simulated annealing technique. In two of the models, the total cost was minimized, while in the third model, emissions from the river were minimized for a specified constraint on the available money. Proof-of-concept studies, with hypothetical scenarios for contaminant loading but realistic flow conditions corresponding to the Tyne River, UK, were carried out. It was found that the treatment cost could be reduced by 36% by treating wastewater discharges in the upper reaches more during the high tide as compared to during low tide. For the same level of N2O emissions, approximately 16.7% lesser costs could be achieved by not only treating the wastewater but also inducing dilution by releasing more water from the upstream side. It was also found that beyond a limit, N2O emissions cannot be reduced significantly by spending more money on treatment and water release.

Modeling greenhouse gas emissions from riverine systems: A review

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Optimal Implementation of Wastewater Reuse in Existing Sewerage Systems to Improve Resilience and Sustainability in Water Supply Systems

“…As the formulated optimization problem is non-linear, it is solved in this study using a simulation-optimization framework [39][40][41]. For optimization, the Genetic Algorithm is used from the MATLAB© toolkit, with Population size = 50; Max Stall generations = 50, Max Generations = 200, Function Tolerance = 0.1, Constraint Tolerance = 0.01, Crossover fraction = 0.8, and Gaussian mutation function.…”

Enhanced physics‐informed neural networks for efficient modelling of hydrodynamics in river networks

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