ABSTRACT Modeling of filter performance is very difficult because of complexity of the defining physical and chemical events in the filtration system whereas the knowledge of functionality of filter coefficient. The main objective of this study is to predict the performance of multimedia filter and to evaluate both the initial and transient stage of suspended solid removal efficiency depending on experimental data. Fuzzy logic has been used to build a model of multi input and one output (MISO) for the removal efficiency of multimedia filter which it consists from sand and granular activated carbon (GAC) mediums. The control parameters of (FLC) of Sugeno model are seven parameters which are media depths, media grains size for both sand and GAC, filtration rate, diameter of suspension particle, feed concentration, and operation time. The output parameter is removal efficiency of media filter whereas these data are collocated from pilot scale deep bed filter, thus, the removal efficiency of filter was modeled by 575 rules as a function of different control parameters. An adaptive of neuron fuzzy inference system (ANFIS) had used to simulate the experimental data. The simulation results were evaluated using mean absolute percentage error (MAPE), whereas the results showed that the prediction of ANFIS model has a good agreement with experimental data when the MAPE is equal to 7.0458 and fuzzy rule -based modeling proved a reliable and flexible tool to study the performance of multimedia filter. The conclusion showed that there is a relationship between flow rate, effective size and optimum bed depth for all filter media, the increment of effective size generates a higher value of optimum filter bed depth for a lower value of filtration rate. It was concluded that the optimal removal efficiency (95-100) achieved by (0.5-0.7 mm) grain size of sand, (1.5-1.9) mm grain size of granular activated carbon (GAC), with media depths should range from 0.3 to 0.6 m.
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nullR. Naseer, A. Jassim and S. AbuAlhail, "A Fast Predicting Neural Fuzzy Model for Suspended Solid Removal Efficiency in Multimedia Filter," Journal of Environmental Protection, Vol. 1 No. 4, 2010, pp. 438-447. doi: 10.4236/jep.2010.14051.
 C. T. Alan, D. R. Don and J. B. Malcolm, “Water Supply,” 5th Edition, 2000, pp. 335-347.
 K. Yao, M. T. Habiban and C. R. O’Melia, “Water and Wastewater Concept and Applications,” Environmental Science Technology, Vol. 5, No. 11, 1971, pp. 1105-1112.
 C. Tien and B. V. Remoarao, “Granuler Filtration of Aersols and Hydrosols,” 2007, pp. 29-31.
 A. Salim, S. Sizert and A. Bruand. “Development of Adarcy Brinkman Model to Simulate under Flow and Tracer Transport in a Heterogeneous Karstic Aquifer,” Hydrogeology Journal, Vol. 15, 2008.
 Y. Sung and Andrew “Simulation of Pore-Scale Particle Deposition and Clogging,” Journal of Transpiration in Porous Media, Vol. 65, No. 11, 2005, pp. 53-87.
 L. A. Zadeh, “Fuzzy Logic Computing with Words,” IEEE Transactions—Fuzzy Systems, Vol. 4, No. 2, 1996, pp. 103-111.
 S. R. Al-Zahrani, M. A. Sheikh, A. K. T. Husain and S. Farooq, “Performance Evaluation of Slow Sand Filters Using Fuzzy Rule-Based Modeling,” Environmental Modeling & Software, Vol. 19, No. 5, 2003, pp. 507-515.
 A. Altunkaynak, M. Ozger and M. Cakmakci, “Fuzzy Logic Modeling of the Dissolved Oxygen Fluctuations in Golden Horn,” Ecological Modelling, Vol. 189, No. 34, 2005, pp. 436-446.
 O. Terzi, M. E. Keskin and E. D. Taylan, “Estimating Evaporation Using ANFIS,” Journal of Irrigation and Drainage Engineering – ASCE, Vol. 32, No. 5, 2006, pp. 503-507.
 A. Altunkaynak, M. ?zger and M. Cakmakci, “Consümption Prediction of Istanbul City by Using Fuzzy Logic Approach,” Water Resources Management, Vol. 19, No. 5, 2005b, pp. 641-654.
 F. J. Chang and Y. T. Chang, “Adaptive Neuro-Fuzzy Inference System for Prediction of Water Level in Reservoir,” Advances in Water Resources, Vol. 29, No. 1, 2005(b), pp. 1-10.
 M. Cakmakci, “Adaptive Neuro-Fuzzy Modeling of Anaerobic Digestion of Primary Sedimentation Sludge,” Bioprocess and Biosystems Engineering, Vol. 30, No. 5, 2005(b), pp. 349-357.
 S. R. Jang, “ANFIS: Adaptive-Network-based Fuzzy Inference Systems,” IEEE Transaction on Systems, Man and Cybernetics, Vol. 23, No. 3, 1993, pp. 665-685.
 M. Z. Huang, J. Q. Wan, Y. W. Ma, and W. J. Li, “A Fast Predicting Neural Fuzzy Model for On-Line Estimation of Nutrient Dynamics in an Anoxic Process,” Journal of Science Direct Bioresource Technology, Vol. 101, No. 6, 2010, pp. 1642-1651.