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Most research on membrane fouling models in the past are based on theoretical equations in lab-scale experiments. But these studies are barely suitable for applying on the full-scale spot where there is a sequential process such as filtration, backwash and drain. This study was conducted in submerged membrane system which being on operation auto sequentially and treating wastewater from G-water purification plant in Incheon. TMP had been designated as a fouling indicator in constant flux conditions. Total volume of inflow and SS concentration are independent variables as major operation parameters and time-series analysis and prediction of TMP were conducted. And similarity between simulated values and measured values was assessed. Final prediction model by using genetic algorithm was fully adaptable because simulated values expressed pulse-shape periodicity and increasing trend according to time at the same time. As results of twice validation, correlation coefficients between simulated and measured data were r = 0.721, r = 0.928, respectively. Although this study was conducted limited to data for summer season, the more amount of data, better reliability for prediction model can be obtained. If simulator for short range forecast can be developed and applied, TMP prediction technique will be a great help to energy efficient operation.

A Study on Time Series Analysis of Membrane Fouling by using Genetic Algorithm in the Field Plant