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.
Highlights
Deep bed filtration is an effective process in removing particles from range 0.01 to 100 in size [1] Removal of these particles by deep bed filtration involves complex mechanisms .First; particles in suspension are transported near filter grains by mechanisms such as sedimentation, interception, diffusion, inertia and hydrodynamic effect
The simulation results were evaluated using mean absolute percentage error (MAPE), whereas the results showed that the prediction of adaptive of neuron fuzzy inference system (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
Summary
Deep bed filtration is an effective process in removing particles from range 0.01 (μm) to 100 (μm) in size [1] Removal of these particles by deep bed filtration involves complex mechanisms .First; particles in suspension are transported near filter grains by mechanisms such as sedimentation, interception, diffusion, inertia and hydrodynamic effect. Several mathematical filtration models have been proposed to describe Dispersed particles removal by filter whereas macroscopic and microscopic theories are widely accepted. There are many other models available in the literature including in [3,4,5]. These models require data for characteristics of filter media, type of flow and liquid. The term computing with words has been introduced by [6] to explain the notion of reasoning linguistically rather than with numerical quantities. Such reasoning has central importance for many emer-
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