Hybrid computational fluid dynamics model for dissolved oxygen field prediction in a full-scale wastewater aeration basin

ABSTRACT Graphical abstract showing that calibrated CFD of a full-scale 12.6 m aeration basin was converted into a deep-learning surrogate, reducing dissolved-oxygen prediction time from 1,856 s to 61–67 ms with 0.11–0.40 mg/L error. This study aimed to develop a fast surrogate model for spatial dissolved oxygen prediction in a deep, full-scale activated sludge aeration basin and to evaluate its suitability for operational use. A two-phase water-air computational fluid dynamics model was built in COMSOL Multiphysics 6.3 for a basin with a water depth of 12.6 m using a realizable k-epsilon turbulence model. A computational fluid dynamics-informed deep neural network was then trained on computational fluid dynamics-generated datasets using Bayesian hyperparameter optimization. Model performance was evaluated against measured dissolved oxygen data from two independent 24-hour field campaigns. Bootstrap analysis yielded 95% confidence intervals of 0.11–0.40 mg/L for the MAE and 6.0–17.7% for the normalized root mean square error. The surrogate reduced evaluation time from 1,856 seconds to 61–67 milliseconds, corresponding to an average speed-up of about 27,900-fold. These results indicate that the framework can provide real-time spatial dissolved oxygen estimates and show strong potential for plant-specific monitoring and aeration control support.