Prediction of Fecal Coliform Removal on Intermittent Media Infiltration by Varying Soil Content Based on FREN

Abstract

Current global water shortage and water pollution problem are some of the crucial issues in the<br />world, especially in the arid zones. The wastewater reuse was investigated the efficiency of fecal coliform<br />(FC) removal using the intermittent media infiltration (IMI) with varying soil content and natural porous<br />media (sand, zeolite, vermicompost and charcoal), and its prediction was introduced by applying fuzzy rules<br />emulated network (FREN). The physicochemical properties of the porous media were determined and the<br />mechanisms of FC removal were discussed as the effect of fine particle size and increasing of ion charges. The compositions of soil and porous media at a ratio of 75/25, respectively, gave the best performance of FC reduction. The network architecture was constructed by the knowledge regarding to the relation between soil content (25, 50 and 75) and FC removal, and was introduced IF-THEN rules for FREN construction as their<br />predicted curves at 20 iterations. The learning rate was selected as 5 following the main theorem and the<br />convergence of FREN prediction could be guaranteed. The results showed that the prediction methodology<br />gave a good performance to forecast FC removal with the range of soil content (20-80%) and several compositions of porous media in IMI system.