Evaluation of moving bed biofilm reactor (MBBR) by applying adaptive neuro-fuzzy inference systeme (ANFIS), radial basis function (RBF) and Fuzzy Regression Analysis

Document Type : Original Article

Authors

1 Assistant Professor of Environmental engineering, Kharazmi University, Tehran, Iran

2 MSc. of Environmental engineering, Kharazmi University, Tehran, Iran

Abstract

The purpose of this study is to investigate the accuracy of predictions of aniline removal efficiency in a moving bed biofilm reactor (MBBR) by various methods, namely by RBF, ANFIS, and fuzzy regression analysis. The reactor was operated in an aerobic batch and was filled by light expanded clay aggregate (LECA) as a carrier for the treatment of Aniline synthetic wastewater. Exploratory data analysis was used to detect relationships between the independent and the dependent evaluated data. The models were found to be efficient and robust tools in predicting MBBR performance. Results showed that increasing the neurons in the hidden layer would improve the function of RBF network. The ANFIS model made according to the membership functions of generalized bell, triangular, and Gaussian with R2 equals to 0.99 and RMS error of 0.027 for the anticipation of the concentration of Aniline and R2 equals to 0.99 and RMS error of 0.034 for the prediction of removal COD efficiency.
Highlights

Application of ANN-based models (RBF network), ANFIS models and Fuzzy regression as a robust tool in predicting MBBR efficiency
Determination of process efficiency in various operation conditions for the treatment of aniline  synthetic wastewater

ANFIS had more ability than fuzzy regression analysis in simulating and predicting removal efficiency in different experimental conditions.

Keywords

Article Title [Persian]

Evaluation of moving bed biofilm reactor (MBBR) by applying adaptive neuro-fuzzy inference systeme (ANFIS), radial basis function (RBF) and Fuzzy Regression Analysis

Authors [Persian]

  • محمد دلنواز 1
  • حسین زنگویی 2
1 دانشگاه خوارزمی، ایران
2 دانشگاه خوارزمی، ایران
Abstract [Persian]

The purpose of this study is to investigate the accuracy of predictions of aniline removal efficiency in a moving bed biofilm reactor (MBBR) by various methods, namely by RBF, ANFIS, and fuzzy regression analysis. The reactor was operated in an aerobic batch and was filled by light expanded clay aggregate (LECA) as a carrier for the treatment of Aniline synthetic wastewater. Exploratory data analysis was used to detect relationships between the independent and the dependent evaluated data. The models were found to be efficient and robust tools in predicting MBBR performance. Results showed that increasing the neurons in the hidden layer would improve the function of RBF network. The ANFIS model made according to the membership functions of generalized bell, triangular, and Gaussian with R2 equals to 0.99 and RMS error of 0.027 for the anticipation of the concentration of Aniline and R2 equals to 0.99 and RMS error of 0.034 for the prediction of removal COD efficiency.
Highlights

Application of ANN-based models (RBF network), ANFIS models and Fuzzy regression as a robust tool in predicting MBBR efficiency
Determination of process efficiency in various operation conditions for the treatment of aniline  synthetic wastewater

ANFIS had more ability than fuzzy regression analysis in simulating and predicting removal efficiency in different experimental conditions.

Keywords [Persian]

  • Evaluation of moving bed biofilm reactor (MBBR) by applying adaptive neuro-fuzzy inference systeme (ANFIS)
  • radial basis function (RBF) and Fuzzy Regression Analysis

References

(1)  Jiang, Y., Wang, H., Shang, Y., Yang, K. Simultaneous removal of aniline, nitrogen and phosphorus in aniline-containing wastewater treatment by using sequencing batch reactor. Bioresource Technology 2016, 207, pp 422-429.
(2)  Ren, Z., Zhu, X., Liu, W., Sun, W., Zhang, W., Liu, J. Removal of Aniline from Wastewater Using Hollow Fiber Renewal Liquid Membrane. Chinese Journal of Chemical Engineering 2014, 22, pp 1187–1192.
(3)  Dvořák, L., Lederer, T., Jirků, V., Masák, J., Novák, L. Removal of aniline, cyanides and diphenylguanidine from industrial wastewater using a full-scale moving bed biofilm reactor. Process Biochemistry 2014, 49, pp 102–109.
(4)  Jiang, L., Liu, L., Xiao, S., Che, J. Preparation of a novel manganese oxide-modified diatomite and its aniline removal mechanism from solution. Chemical Engineering Journal 2016, 284, pp 609-619
(5)  Zhang, J., Wu, Y., Qin, C., Liu, L., Lan, Y. Rapid degradation of aniline in aqueous solution by ozone in the presence of zero-valent zinc, Chemosphere 2015, 141, pp 258–264.
(6)  Delnavaz, M., Ayati, B., Ganjidoust, H. Biodegradation of aromatic amine compounds using moving bed biofilm reactors, Iran. Journal of Environmental Health Science & Engineering 2008, 5, pp 243-250.
(7)  Wang, X. J., Xia, S. Q., Chen, L., Zhao, J. F., Renault, N. J., Chovelon, J. M. Nutrients removal from municipal wastewater by chemical precipitation in a moving bed biofilm reactor. Process Biochemistry 2006, 41, pp 824–828.
(8)  Rusten, B., Eikebrokk, B., Ulgenes, Y., Lygren, E. Design and operations of the Kaldnes moving bed biofilm reactors, Aquacult. Eng. Aquacultural Engineering 2006, 34, pp 322–331.
(9)  Ayati, B., Ganjidoust, H., Mir Fattah, M. Degradation of aromatic compounds using moving bed biofilm reactor. Journal of Environmental Health Science & Engineering 2007, 4, pp 107-112.
(10)             Mohan Raju, M., Srivastava, R. K., Bisht, D. C. S., Sharma, H. C., Kumar, A. Development of Artificial Neural-Network-BasedModels for the Simulation of Spring Discharge. Lecture Notes in Artificial Intelligence2011, 2011, pp 1-11.
(11)             Anderson, D., McNeill, G. Artificial Neural Networks Technology, Data & Analysis Center for Software, Kaman Sciences Corporation: New York, 1992.
(12)             Delnavaz, M., Ayati, B., Ganjidoust, H. Prediction of Moving Bed Biofilm Reactor (MBBR) Performance for the Treatment of Aniline Using Artificial Neural Networks (ANN). Journal of Hazardous Materials 2010, 179, pp 769–775.
(13)             Cristea, V. M., Pop, C., Agachi, P.S. Artificial Neural Networks Modeling of PID and Model Predictive Controlled Wastewater Treatment Plant Based on the Benchmark Simulation Model No.1, Computer Aids for Chemical Engineering 2009, 26, pp 1183-1188.
(14)             Sadrzadeh, M., Mohammadia, T., Ivakpour, J., Kasiri, N. Neural network modeling of Pb2+ removal from wastewater using electro-dialysis. Chemical Engineering and Processing: Process 2009, 48, pp 1371–1381.
(15)             Sahinkaya, E. Biotreatment of zinc-containing wastewater in a sulfidogenic CSTR: Performance and artificial neural network (ANN) modeling studies. Journal of Hazardous Materials 2009, 164, pp 105–113.
(16)             Prakash, N., Manikandan, S. A., Govindarajan, L., Vijayagopal, V. Prediction of biosorption efficiency for the removal of copper(II) using artificial neural networks. Journal of Hazardous Materials 2008, 152, pp 1268–1275.
(17)             Clescerl, L.S., Greenberg, A.E., Eaton. A.D. Standard method for the examination water and wastewater, 20th ed. American Public Health Association: Washington DC. 2005.
(18)             Bestamin, O., Ahmet, D. Neural network prediction model for the methane fraction in biogas from field scale landfill bioreactors.  Environmental Modelling & Software 2007, 22, pp 15 -822.
(19)             Hagan, M. T., Demuth, H. B., Beale, M. H. Neural network design. Stamford, CT: Thomson Learning, 1996.
(20)             Bagheria, M., Mirbagheria, S. A., Ehteshamia, M., Bagherib, Z. Modeling of a sequencing batch reactor treating municipal wastewater using multi-layer perceptron and radial basis function artificial neural networks. Process Safety and Environmental Protection 2015, 93, PP 111–123.
(21)             Turkdogan-Aydınol, F. I., Yetilmezsoy, K. A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater, Journal of Hazardous Materials 2010, 182, pp 460–471.
(22)             Zadeh, L. A. Fuzzy sets. Information and Control 1965, 8, pp 338-353.
(23)             Tanaka, H., Uejima, S., Asai, K. Linear regression analysis with fuzzy model, IEEE Transactions on Systems, Man, and Cybernetics: Systems 1982, 12, pp 903-907.
(24)             Jung, H. Y., Yoon, J. H., Choi, S. H. Fuzzy linear regression using rank transform method. Fuzzy Sets and Systems 2014, 274, pp 97-108. 
(25)             Choi, S. H., Buckley, J. J. Fuzzy regression using least absolute deviation estimators. Soft computing 2008, 12, pp 257-263.
(26)             Taheri, S. M., Kelkinnama, M. Fuzzy linear regression based on least absolute deviations. Iranian Journal of Fuzzy Systems2012, 9, pp 121-140.
(27)             Nasrabadi, E., Hashemi, M., hatee, M. G. An LP-based approach to outliers detection in fuzzy regression analysis. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 2007, 15, pp 441-456.
(28)             Dubois, D., Prade, H. Fuzzy Sets and Systems: Theory and Applications, Academic Press: New York, 1980.
(29)             Badalians Gholikandi, G., Delnavaz, M., Riahi, R. Use of Artificial Neural Network for Prediction of Coagulation/Flocculation Process by PAC in Water Treatment Plant.  Environmental Engineering and Management Journal 2011, 10, pp 1719-1725.
(30)             Jang, J. S. R. ANFIS: adaptive network based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics: Systems 1993, 23, pp 665–685.
(31)             Salahia, A., Mohammadia, T., Mosayebi Behbahanib, R., Hemmatic, M. Asymmetric polyethersulfone ultrafiltration membranes for oily wastewater treatment: Synthesis, characterization, ANFIS modeling, and performance. Journal of Environmental Chemical Engineering 2015, 3, pp 170-178.
(32)             Wan, J., Huang, M., Ma, Y., Guo, W., Wang, Y., Zhang, H., Li, W., Sun, X. Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system. Applied Soft Computing 2011, 11, PP 3238-3246.
Iranian Journal of Environmental Technology 
Volume 2, Issue 1
Spring & Summer
April 2016
Pages 27-40

Files

Share

How to cite

Statistics