Fuzzy Logic-Based Model to Predict the Impact of Flow Rate and Turbidity on the Performance of Multimedia Filters
Publication: Journal of Environmental Engineering
Volume 143, Issue 9
Abstract
This paper uses fuzzy logic–based models to predict and evaluate the performance of multimedia filters utilized in wastewater treatment. A fuzzy logic–based model is constructed and trained to predict the operating time (i.e., treated volume of water) of a multimedia filter. A preset acceptable turbidity value of 5 nephelometric turbidity units (NTU) is used as the breakthrough point. The model is based on a set of experimental data with variable flow rates and influent turbidity. The results from the fuzzy-based model indicate that the simulated treated volume at different inputs of turbidity and flow rate fits the experimental results with a coefficient of multiple determination () of 91.6%. To examine the efficiency of the developed model predicting treated volume, the results obtained from the model are compared with the results obtained from a multiple linear regression model. The accuracy of prediction of both models are examined using the mean absolute error (MSE), root-mean-square error (RMSE), and . The MSE, RMSE, and for the fuzzy-based model are 5,318, 72.92, and 98%, respectively, whereas for the regression model they are 3,302, 57.46, and 99%, respectively. Although the regression model appears to be more accurate, the fuzzy-based model is deemed to be more advantageous because it can incorporate the uncertainties in inputs as a result of human judgments and can indicate the errors in the outputs.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors wish to acknowledge Qatar University for the financial support.
References
Allison, P. (1999). Multiple regression: A primer, Pine Forge Press, Thousand Oaks, CA.
Babuska, R. (2003). “Fuzzy systems, modelling and identification.” ⟨http://lcewww.et.tudelft.nl/∼babuska/transp/fuzzmod.pdf⟩ (Apr. 1, 2016).
Betroluzza, C., Corral, N., and Salas, A. (1995). “On a new class of distances between fuzzy numbers.” Mathware Soft Comput., 2(2), 71–84.
Bongards, M. (2001). “Improving the efficiency of a wastewater treatment plant by fuzzy control and neural network.” Water Sci. Technol., 43(11), 189–196.
Carrasco, E., Rodriguez, J., Punal, A., Roca, E., and Lema, J. (2002). “Rule-based diagnosis and supervision of a pilot-scale wastewater treatment plant using fuzzy logic techniques.” Expert Syst. Appl., 22(1), 11–20.
Cawley, G., and Talbot, N. (2010). “On over-fitting in model selection and subsequent selection bias in performance evaluation.” J. Mach. Learn. Res., 11, 2079–2107.
Chen, J., and Chang, N. (2007). “Mining the fuzzy control rules of aeration in a submerged biofilm wastewater treatment process.” Eng. Appl. Artif. Intell., 20(7), 959–969.
Chen, S. H. (1985). “Ranking fuzzy numbers with maximising set and minimizing set.” Fuzzy Sets Syst., 17(2), 113–129.
Elektorowicz, M., and Amid, A. (2009). “Hierarchical fuzzy inference system (HFIS) modeling for environmental issues.” Int. Conf. on Structural Safety and Reliability, CRC Press, Boca Raton, FL.
Elektorowicz, M., Qasaimeh, A., and Balazinski, M. (2003). “Assessment of the uptake of bioavailable mercury using artificial intelligence.” 7th Int. Conf. on the Biogeochemistry of Trace Elements (ICOBTE), Eawag Aquatic Research, Dübendorf, Switzerland.
Goyal, M., Bharti, B., Quilty, J., Adamowski, J., and Pandey, A. (2014). “Modeling of daily pan evaporation in sub tropical climates using ANN, LS-SVR, fuzzy logic, and ANFIS.” Expert Syst. Appl., 41(11), 5267–5276.
Hampel, R. (2000). Fuzzy control: Theory and practice, Physica, Heidelberg.
Honggui, H., Ying, L., and Junfei, Q. (2014). “A fuzzy neural network approach for online fault detection in waste water treatment process.” Comput. Electr. Eng., 40(7), 2216–2226.
Jalalifar, H., Mojedifarb, S., and Sahebi, A. (2014). “Prediction of rock mass rating using fuzzy logic and multi-variable RMR regression model.” Int. J. Min. Sci. Technol., 24(2), 237–244.
Jang, J. S. R. (1991). “Fuzzy modeling using generalized neural networks and Kalman filter algorithm.” Proc., 9th National Conf. on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, Palo Alto, CA, 762–767.
Kleiner, Y., Sadiq, R., and Rajani, B. (2006). “Modeling the deterioration of buried infrastructure as a fuzzy Markov process.” J. Water Supply Res. Tech. AQUA, 55(2), 67–80.
Mackinson, S. (2000). “An adaptive fuzzy expert system for predicting structure, dynamics and distribution of herring shoals.” Ecol. Modell., 126(2–3), 155–178.
Mair, A., and El-Kadi, A. (2013). “Logistic regression modeling to assess groundwater vulnerability to contamination in Hawaii, USA.” J. Contam. Hydrol., 153, 1–23.
Mamdani, E. (1977). “Application of fuzzy logic to approximate reasoning using linguistic synthesis.” IEEE Trans. Comput., C-26(12), 1182–1191.
MATLAB [Computer software]. MathWorks, Natick, MA.
Mullai, P., Arulselvi, S., Ngo, H., and Sabarathinam, P. (2011). “Experiments and ANFIS modelling for the biodegradation of penicillin-G wastewater using anaerobic hybrid reactor.” Bioresour. Technol., 102(9), 5492–5497.
Nasr, M., Moustafa, M., Seif, H., and Kobrosy, G. (2012). “Application of artificial neural network (ANN) for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT.” Alexandria Eng. J., 51(1), 37–43.
Polit, M., Genovesi, A., and Claudet, B. (2001). “Fuzzy logic observers for a biological wastewater treatment process.” Appl. Numer. Math., 39(2), 173–180.
Qasaimeh, A., Abdallah, M., and Bani Hani, F. (2012). “Adaptive neuro-fuzzy logic system for heavy metal sorption in aquatic environments.” J. Water Resour. Prot., 4, 277–284.
RStudio Team [Computer software]. RStudio, Inc., Boston.
Sari, H., Yetilmezsoy, K., Ilhan, F., Yazici, S., Kurt, U., and Apaydin, O. (2013). “Fuzzy-logic modeling of Fenton’s strong chemical oxidation process treating three types of landfill leachates.” Environ. Sci. Pollut. Res., 20(6), 4235–4253.
Silva, I., and Flauzino, R. (2008). “An approach based on neural networks for estimation and generalization of crossflow filtration processes.” Appl. Software Comput., 8(1), 590–598.
Sockloff, A. (1975). “The analysis of nonlinearity via linear regression with polynomial and product variables: An examination.” Rev. Educ. Res., 6(2), 267–291.
Tran, L. T., and Duckstein, L. (2002). “Comparison of fuzzy numbers using a fuzzy distance measure.” Fuzzy Sets Syst., 130(3), 331–341.
Turkdogan-Aydinol, F. I., and Yetilmezsoy, K. (2010). “A fuzzy logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater.” J. Hazard. Mater., 182(1), 460–471.
Wan, J., et al. (2011). “Prediction of effluent quality of a paper mill wastewater treatment using an adaptive network-based fuzzy inference system.” Appl. Soft Comput., 11(3), 3238–3246.
Xu, Y., et al. (2015). “Method to predict key factors affecting lake eutrophication—A new approach based on support vector regression model.” Int. Biodeterior. Biodegrad., 102, 308–315.
Yager, R. R. (1980). “A general class of fuzzy connectives.” Fuzzy Sets Syst., 4(3), 235–242.
Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 143 • Issue 9 • September 2017
Copyright
©2017 American Society of Civil Engineers.
History
Received: Nov 7, 2016
Accepted: Apr 5, 2017
Published online: Jul 7, 2017
Published in print: Sep 1, 2017
Discussion open until: Dec 7, 2017
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.