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Updated: Feb 7, 2026

Extraction of Structural Extracellular Polymeric Substances from Aerobic Granular Sludge
Published on: September 26, 2016
Performance prediction of an aerobic granular SBR using modular multilayer artificial neural networks
Mohamed Sherif Zaghloul1, Rania Ahmed Hamza1, Oliver Terna Iorhemen1
1Department of Civil Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB., Canada. T2N 1N4.
Abstract:
Aerobic granulation is a complex process that, while proven to be more effective than conventional treatment methods, has been a challenge to control and maintain stable operation. This work presents a static data-driven model to predict the key performance indicators of the aerobic granulation process. The first sub-model receives influent characteristics and granular sludge properties. These predicted parameters then become the input for the second sub-model, predicting the effluent characteristics. The model was developed with a dataset of 2600 observations and evaluated with an unseen dataset of 286 observations. The prediction R2 and RMSE were >99% and <5% respectively for all predicted parameters. The results of this paper show the effectiveness of data-driven models for simulating the complex aerobic granulation process, providing a great tool to help in predicting the behaviour, and anticipating failures in aerobic granular reactors.
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