Reverse osmosis (RO) membrane process has been considered a promising technology for water treatment and desalination. However, it is difficult to predict the performance of pilot- or full-scale RO systems because numerous factors are involved in RO performance, including variations in feed water (quantity, quality, temperature, etc), membrane fouling, and time-dependent changes (deteriorations). Accordingly, this study intended to develop a practical approach for the analysis of operation data in pilot-scale reverse osmosis (RO) processes. Novel techniques such as artificial neural network (ANN) and genetic programming (GP) technique were applied to correlate key operating parameters and RO permeability statistically. The ANN and GP models were trained using a set of experimental data from a RO pilot plant with a capacity of 1,000 m3/day and then used to predict its performance. The comparison of the ANN and GP model calculations with the experiment results revealed that the models were useful for analyzing and classifying the performance of pilot-scale RO systems. The models were also applied for an in-depth analysis of RO system performance under dynamic conditions.
Koo, Jaewuk; Shin, Yonghyun; Lee, Sangho; and Choi, Juneseok, "Application Of Data Mining For Reverse Osmosis Process In Seawater Desalination" (2014). CUNY Academic Works.