ANFIS Based Effluent pH Quality Prediction Model for an Activated Sludge Process

Abstract:

Activated sludge process is the most efficient technique used for municipal wastewater treatment plants. However, a pH value outside the limit of 6-9 could inhibit the activities of microorganisms responsible for treating the wastewater, and low pH value may cause damage to the treatment system. Therefore, prediction of pH value is essential for smooth and trouble-free operation of the process. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) model for effluent pH quality prediction in the process. For comparison, artificial neural network is used. The model validation is achieved through use of full-scale data from the domestic wastewater treatment plant in Kuala Lumpur, Malaysia. Simulation results indicate that the ANFIS model predictions were highly accurate having the root mean square error (RMSE) of 0.18250, mean absolute percentage deviation (MAPD) of 9.482% and the correlation coefficient (R) of 0.72706. The proposed model is efficient and valuable tool for the activated sludge wastewater treatment process.

• Periodical: Advanced Materials Research (Volume 845)
• Pages: 538-542
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.845.538
• Online since: December 2013
• Authors: Muhammad Sani Gaya, Norhaliza Abdul Wahab*, Yahya M. Sam, Sharatul Izah Samsuddin
• Keywords: Fuzzy Inference System, Microorganism, Neural Network (NN), Prediction, Wastewater

References

[1] T. Y. Pai, P. Y. Yang, S. C. Wang, M. H. Lo, C. F. Chiang, J. L. Kuo, H. H. Chu, H. C. Su, L. F. Yu, H. C. Hu, Y. H. Chang, Predicting effluent from the wastewater treatment plant of industrial park based on fuzzy network and influent quality, Appl. Math. Modell. 35 (2011).

DOI: 10.1016/j.apm.2011.01.019

[2] T. -Y. Pai, S. C. Wang, C. F. Chiang, H. C. Su, L. F. Yu, P. J. Sung, C. Y. Lin, H. C. Hu, Improving neural network prediction of effluent from biologicalwastewater treatment plant of industrial park using fuzzy learning approach, Bioprocess Biosyst. Eng. 32 (2009).

DOI: 10.1007/s00449-009-0304-2

[3] B. Meenakhsipriya, K, Saravanan, S. Sathiyavathi, Neural Based pH System in Effluent Treatment Process Modern Appl. Sci. 3 (2009) 166–176.

DOI: 10.5539/mas.v3n4p166

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

DOI: 10.1109/21.256541

[5] 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).

[6] A. Engelbrecht: Computational intelligence: An introduction (John Wiley and Sons, England 2007).

[7] C. Lewis : Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting ( Butterwoth Scientific, London 1982).