Fouling Prediction using Neural Network Model for Membrane Bioreactor System

Membrane bioreactor (MBR) technology is a new method for water and wastewater treatment due to its ability to produce better and high-quality effluent that meets water quality regulations. MBR also is an advanced way to displace the conventional activated sludge (CAS) process. Even this membrane gives better performances compared to CAS, it does have few drawbacks such as high maintenance cost and fouling problem. In order to overcome this problem, an optimal MBR plant operation need to be developed. This can be achieved through an accurate model that can predict the fouling behaviour which could optimise the membrane operation. This paper presents the application of artificial neural network technique to predict the filtration of membrane bioreactor system. The Radial Basis Function Neural Network (RBFNN) is applied to model the developed submerged MBR filtration system. RBFNN model is expected to give good prediction model of filtration system for estimating the fouling that formed during filtration process.

[1]  Shafishuhaza Sahlan,et al.  Modeling of submerged membrane bioreactor filtration process using NARX-ANFIS model , 2015, 2015 10th Asian Control Conference (ASCC).

[2]  Fangang Meng,et al.  Membrane Bioreactors for Industrial Wastewater Treatment: A Critical Review , 2012 .

[3]  Matthias Kraume,et al.  Membrane Bioreactors in Waste Water Treatment – Status and Trends , 2010 .

[4]  Majid Bagheri,et al.  Modeling of effluent quality parameters in a submerged membrane bioreactor with simultaneous upward and downward aeration treating municipal wastewater using hybrid models , 2016 .

[5]  T. Melin,et al.  Treatment of landfill leachate in a bench scale MBR , 2009 .

[6]  N. B. Chang,et al.  Assessing wastewater reclamation potential by neural network model , 2003 .

[7]  Haiying Yu,et al.  Membrane fouling in a submerged membrane bioreactor with focus on surface properties and interactions of cake sludge and bulk sludge. , 2014, Bioresource technology.

[8]  A. Drews,et al.  Recent advances in membrane bioreactors (MBRs): membrane fouling and membrane material. , 2009, Water research.

[9]  Jaya Kandasamy,et al.  Modelling of submerged membrane flocculation hybrid systems using statistical and artificial neural networks methods , 2010 .

[10]  Guohe Huang,et al.  A neural network predictive control system for paper mill wastewater treatment , 2003 .

[11]  Zurina Zainal Abidin,et al.  Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network. , 2011, Journal of hazardous materials.

[12]  Dae Sung Lee,et al.  Monitoring of sequencing batch reactor for nitrogen and phosphorus removal using neural networks , 2007 .

[13]  Anja Drews,et al.  Membrane fouling in membrane bioreactors—Characterisation, contradictions, cause and cures , 2010 .

[14]  Jianrong Chen,et al.  A critical review of extracellular polymeric substances (EPSs) in membrane bioreactors: Characteristics, roles in membrane fouling and control strategies , 2014 .

[15]  Bogdan Suchacz,et al.  The recognition of similarities in trace elements content in medicinal plants using MLP and RBF neural networks. , 2006, Talanta.

[16]  Özer Çinar,et al.  New tool for evaluation of performance of wastewater treatment plant: Artificial neural network , 2005 .

[17]  Haiying Yu,et al.  Thermodynamic analysis of membrane fouling in a submerged membrane bioreactor and its implications. , 2013, Bioresource technology.

[18]  Chung‐Hak Lee,et al.  Comparison of the filtration characteristics between attached and suspended growth microorganisms in submerged membrane bioreactor. , 2001, Water research.