Performance prediction of an aerobic granular SBR using modular multilayer artificial neural networks

Mohamed Sherif Zaghloul; Rania Ahmed Hamza; Oliver Terna Iorhemen; Joo Hwa Tay

doi:10.1016/j.scitotenv.2018.07.140

Performance prediction of an aerobic granular SBR using modular multilayer artificial neural networks

Sci Total Environ. 2018 Dec 15:645:449-459. doi: 10.1016/j.scitotenv.2018.07.140. Epub 2018 Jul 18.

Authors

Mohamed Sherif Zaghloul¹, Rania Ahmed Hamza², Oliver Terna Iorhemen³, Joo Hwa Tay⁴

Affiliations

¹ Department of Civil Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB., Canada. T2N 1N4. Electronic address: mohamed.abdelsamie@ucalgary.ca.
² Department of Civil Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB., Canada. T2N 1N4. Electronic address: rania.sayedeid@ucalgary.ca.
³ Department of Civil Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB., Canada. T2N 1N4. Electronic address: oliver.iorhemen@ucalgary.ca.
⁴ Department of Civil Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB., Canada. T2N 1N4. Electronic address: jhatay@ucalgary.ca.

PMID: 30025244
DOI: 10.1016/j.scitotenv.2018.07.140

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.

Keywords: Aerobic granulation; Data-driven modelling; Modelling; Neural networks; Wastewater treatment.