Optimization of artificial neural network topology for membrane bioreactor filtration using response surface methodology

Syahira Ibrahim, Norhaliza Abdul Wahab, Fatimah Sham Ismail, Yahaya Md Sam


The optimization of artificial neural networks (ANN) topology for predicting permeate flux of palm oil mill effluent (POME) in membrane bioreactor (MBR) filtration has been investigated using response surface methodology (RSM). A radial basis function neural network (RBFNN) model, trained by gradient descent with momentum (GDM) algorithms was developed to correlate output (permeate flux) to the four exogenous input variables (airflow rate, transmembrane pressure, permeate pump and aeration pump). A second-order polynomial model was developed from training results for natural log mean square error of 50 developed ANNs to generate 3D response surfaces. The optimum ANN topology had minimum ln MSE when the number of hidden neurons, spread, momentum coefficient, learning rate and number of epochs were 16, 1.4, 0.28, 0.3 and 1852, respectively. The MSE and regression coeffcient of the ANN model were determined as 0.0022 and 0.9906 for training, 0.0052 and 0.9839 for testing and 0.0217 and 0.9707 for validation data sets. These results confirmed that combining RSM and ANN was precise for predicting permeates flux of POME on MBR system. This development may have significant potential to improve model accuracy and reduce computational time.


Artificial neural network; Membrane bioreactor; Palm oil mill effluent; Response surface methodology; Topology

Full Text:



M. F. Alkhatib, et. al., “Application of response surface methodology ( RSM ) for optimization of color removal from POME by granular activated carbon,” Int. J. Environ. Sci. Technol., vol. 12, pp. 1295–1302, 2015.

W. P. Wah, et al., “Pre-treatment and membrane ultrafiltration using treated palm oil mill effluent (POME),” Songklanakarin J. Sci. Technol., vol. 24, pp. 891–898, 2002.

A. Cassano and A. Basile, “20 - Membranes for industrial microfiltration and ultrafiltration,” in Advanced Membrane Science and Technology for Sustainable Energy and Environmental Applications, 1st ed., A. Basile and S. P. Nunes, Eds. In Woodhead Publishing Series in Energy, 2011, pp. 647–679.

G. Mohd Syahmi Hafizi, et al., “Fouling assessment of tertiary palm oil mill effluent (POME) membrane treatment for water reclamation,” J. Water Reuse Desalin., vol. 8, no. 3, pp. 412–423, 2018.

T. Leiknes, “Wastewater Treatment by Membrane Bioreactors,” in Membrane Operations: Innovative Separations and Transformations, E. Drioli and L. Giorno, Eds. Italy: WILEY-VCH Verlag GmbH & Co. KGaA, 2009, pp. 374–391.

H. Lin et al., “Membrane Bioreactors for Industrial Wastewater Treatment : A Critical Review,” Environ. Sci. Technol., vol. 42, pp. 677–740, 2012.

T. Janus, “Modelling and Simulation of Membrane Bioreactors for Wastewater Treatment,” De Montfort University, Leicester, 2013.

S. Judd, “Fouling control in submerged membrane bioreactors,” Water Sci. Technol., vol. 51, no. 6–7, pp. 27–34, 2005.

N. H. Abdurahman, et al., “An Integrated Ultrasonic Membrane An aerobic System (IUMAS) for Palm Oil Mill Effluent (POME) Treatment,” Energy Procedia, vol. 138, pp. 1017–1022, 2017.

Z. Ahmad, et al., “Membrane Bioreactor for Palm Oil Mill Effluent and Resource Recovery,” in International Conference on Sustainable Development for Water and Waste Water Treatment. December 2009, 2009, pp. 1–8.

S. Muhammad, et al., “Investigation of Three Pre-treatment Methods Prior to Nanofiltration Membrane for Palm Oil Mill Effluent Treatment,” Sains Malaysiana, vol. 44, no. 3, pp. 421–427, 2015.

N. S. Azmi and K. F. M. Yunos, “Wastewater Treatment of Palm Oil Mill Effluent (POME) by Ultrafiltration Membrane Separation Technique Coupled with Adsorption Treatment as Pre-treatment,” Agric. Agric. Sci. Procedia, vol. 2, pp. 257–264, 2014.

R. Badrnezhad and B. Mirza, “Modeling and optimization of cross-flow ultrafiltration using hybrid neural network-genetic algorithm approach,” J. Ind. Eng. Chem., vol. 20, no. 2, pp. 528–543, 2014.

R. Soleimani, et al., “Experimental investigation, modeling and optimization of membrane separation using artificial neural network and multi-objective optimization using genetic algorithm,” Chem. Eng. Res. Des., vol. 91, no. 5, pp. 883–903, 2013.

S. Curcio, et al., “Reduction and control of flux decline in cross-flow membrane processes modeled by artificial neural networks and hybrid systems,” Desalination, vol. 236, no. 1–3, pp. 234–243, 2009.

Y. Zakariah, et al., “Permeate Flux Measurement and Prediction of Submerged Membrane Bioreactor Filtration Process Using Intelligent Techniques,” J. Teknol. UTM, vol. 73, no. 3, pp. 85–90, 2015.

Y. Zakariah, et al., “Modeling of submerged membrane bioreactor filtration process using NARX-ANFIS model,” in 2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015, 2015, pp. 1–6.

E. Razmi-Rad, et al., “Prediction of rheological properties of Iranian bread dough from chemical composition of wheat flour by using artificial neural networks,” J. Food Eng., vol. 81, no. 4, pp. 728–734, 2007.

K. C. Keong, et al., “Artificial Neural Network Flood Prediction for Sungai Isap Residence,” in 2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS), 2016, pp. 236–241.

D. Krishna and R. P. Sree, “Artificial Neural Network and Response Surface Methodology Approach for Modeling and Optimization of Chromium (VI) Adsorption from Waste Water using Ragi Husk Powder,” Indian Chem. Eng., vol. 55, no. 3, pp. 200–222, 2013.

F. Ibrahim, et al., “A novel dengue fever (DF) and dengue haemorrhagic fever (DHF) analysis using artificial neural network (ANN),” Comput. Methods Programs Biomed., vol. 79, no. 3, pp. 273–281, 2005.

M. Mustafa, et al., “Classification of EEG Spectrogram Image with ANN approach for Brainwave Balancing Application,” Int. J. Simul. Syst. Sci. Technol., vol. 12, pp. 29–34, 2011.

M. Aghbashlo, et al., “Optimization of an artificial neural network topology for predicting drying kinetics of carrot cubes using combined response surface and genetic algorithm,” Dry. Technol., vol. 29, no. 7, pp. 770–779, 2011.

T. Nazghelichi, et al., “Optimization of an artifial neural network topology using couple response surface meethodology and genetic algorithm for fluidize bed drying,” Comput. Electron. Agric., vol. 75, pp. 84–91, 2011.

I. Syahira, “Amylose content calibration model for the three types of selected rice grains using visible shortwave near infrared spectroscopy,” Universiti Teknologi Malaysia, 2015.

H. Nourbakhsh, et al., “Prediction of red plum juice permeate flux during membrane processing with ANN optimized using RSM,” Comput. Electron. Agric., vol. 102, pp. 1–9, 2014.

K. Chia, et al., “Neural network and principal component regression in non-destructive soluble solids content assessment: a comparison.,” J. Zhejiang Univ. Sci. B, vol. 13, no. 2, pp. 145–51, 2012.

DOI: http://doi.org/10.11591/ijai.v9.i1.pp117-125
Total views : 35 times


  • There are currently no refbacks.

View IJAI Stats

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.