ANFIS Modelling of Carbon Removal in Domestic Wastewater Treatment Plant

Muhammad Sani Gaya; Norhaliza Abdul Wahab; Yahya M. Sam; Aznah Nor Anuar; Sharatul Izah Samsuddin

doi:10.4028/www.scientific.net/AMM.372.597

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 372ANFIS Modelling of Carbon Removal in Domestic...

ANFIS Modelling of Carbon Removal in Domestic Wastewater Treatment Plant

Abstract:

Modelling of an ill-defined system such as the wastewater treatment plant is quite tedious and difficult. However, successful and optimal operation of the system relied upon a suitable model. Most of the available developed models were applied to industrial wastewater treatment plants. This paper presents adaptive neuro fuzzy inference system (ANFIS) model for carbon removal in the Bunu domestic wastewater treatment plant in Kuala Lumpur, Malaysia. For comparison feed-forward neural network (FFNN) was used. Simulation results revealed that ANFIS model is slightly better than the FFNN model, thus proving that the model is a reliable and valuable tool for the wastewater treatment plant.

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Periodical:

Applied Mechanics and Materials (Volume 372)

Pages:

597-601

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.372.597

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Online since:

August 2013

Authors:

Muhammad Sani Gaya, Norhaliza Abdul Wahab, Yahya M. Sam, Aznah Nor Anuar, Sharatul Izah Samsuddin

Keywords:

Fuzzy Inference System, Model, Neural Network (NN), Prediction

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References

[1] Perendeci, S. Arslan, S. Celebi, and A. Tanyolac, Prediction of effluent quality of an anaerobic treatment plant under unsteady state through ANFIS modeling with on-line input variables, Chem. Eng. J., 145 (2008) 78–85.

DOI: 10.1016/j.cej.2008.03.008

Google Scholar

[2] M. Huang, Y. Ma, J. Wan, H. Zhang, and Y. Wang, Modeling a paper-making wastewater treatment process by means of an adaptive network-based fuzzy inference system and principal component analysis, Ind. Eng. Chem. Res., 51 (2012) 6166–6174.

DOI: 10.1021/ie203049r

Google Scholar

[3] A. Benhalla, M. Houssou, and M. Charif, Linearization of the full activated sludge model No 1 for interaction analysis., Bioprocess Biosyst Eng., 33 (2010) 759–771.

DOI: 10.1007/s00449-009-0404-z

Google Scholar

[4] A. Nagy, G. Mourot, and B. Marx, Systematic multimodeling methodology applied to an activated sludge reactor model, Ind. Eng. Chem. Res., 49 (2010) 2790–2799.

DOI: 10.1021/ie8017687

Google Scholar

[5] J. Jang, ANFIS: Adaptive-network-based fuzzy inference system, Syst. Man Cybern, IEEE Transactions, 23 (1993) 665–685.

DOI: 10.1109/21.256541

Google Scholar

[6] J. Jang, C. Sun, and E. Mizutani, Neuro-fuzzy and soft computing: A Computational Approach to Learning and Machine Intelligence. Prentice Hall, New Jersey, 1997, pp.199-257.

Google Scholar