A Fast Predicting Neural Fuzzy Model for Suspended Solid Removal Efficiency in Multimedia Filter

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.

A Fast Predicating of Nutrient Removal Efficiency in Five Steps Sequencing Batch Reactor System Using Fuzzy Logic Control Model

Removal efficiency of COD, NH4-N and PO4-P and NO3-N in five step SBR processes is widely influenced by hydraulic retention time of Anaerobic/Anoxic/Aerobic/Anoxic/Aerobic step of this system where the hydraulic retention time in each step is influence directly on removal efficiency of this system therefore the operator of this system cannot control on this system without experience or a control model. The major objective of this paper is develop a control model (Fuzzy Logic Control Model) based on fuzzy logic rule to predict the maximum removal efficiency of COD,NH4-N,PO4-P and NO3-N and minimize hydraulic retention time in each step of SBR process where the controlled variables was the hydraulic retention times in the Anaerobic/Anoxic/Aerobic/Anoxic/Aerobic step respectively and the output variables was the COD, NH4-N, PO4-P and NO3-N removal efficiency at constant ratio of C/N/P and sludge age. As a results Fuzzy logic if-then rules were used and MIMO Model was built to control COD, NH4-Nand PO4-P and NO3-N removal efficiency based on hydraulic retention time in each tank of five step SBR process where the three dimension results show that the influence of hydraulic residence time at each step of SBR system on removal efficiency COD, NH4-N, PO4-P and NO3-N. Fuzzy control model provide a suitable tool for control and fast predict of Hydraulic residence time effects on biological nutrient removal efficiency in five-step sequencing batch reactor.