Membrane Fouling Prediction for Wastewater Treatment in Membrane Bioreactor System by Using ANN Modeling

Authors

  • Khac-Uan Do School of Environmental Science and Technology, Hanoi University of Science and Technology, Hanoi, Vietnam
  • Xuan-Quang Chu Center for Advanced Materials Technology, National Center for Technological Progress, Hanoi, Vietnam
  • Hung-Thuan Tran Center for Advanced Materials Technology, National Center for Technological Pro-gress, Hanoi, Vietnam

Keywords:

artificial neural networks, flux, fouling, membrane filtration, membrane bioreactor, wastewater

Abstract

Membrane fouling has been seen as a big disadvantage of membrane technology. It declined the membrane filtration flux and affected the treatment efficiency of the wastewater treatment systems. Prediction of membrane fouling could provide a suitable solution for operation. In this work, artificial neural networks (ANN) is developed for predicting membrane fouling. The input variables included several parameters such as pH, ammonium, nitrate and alkanility. As a result, two inputs (nitrate and alkanility concentrations inside MBR tank and in the effluent) could give a good performance of the ANN model. Therefore, nitrate and alkanility could be considered as an optimal set of parameters which was used to predict TMP using artificial neural networks. In conclusion, ANN could provide a good solution for predicting membrane fouling in MBR system. Application of ANN could support an alternative way to prevent the membrane fouling in the system.

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Published

2023-05-03

How to Cite

Do, K.-U., Chu, X.-Q. ., & Tran, H.-T. . (2023). Membrane Fouling Prediction for Wastewater Treatment in Membrane Bioreactor System by Using ANN Modeling. Membrane Science International, 2(1), 1–6. Retrieved from https://rasayely-journals.com/index.php/msi/article/view/38

Issue

Vol. 2 No. 1 (2023)

Section

Original Articles

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Copyright (c) 2023 Khac-Uan Do, Xuan-Quang Chu, Hung-Thuan Tran

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