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DOI: https://doi.org/10.4491/eer.2019.324
Analysis of long-term performance of full-scale reverse osmosis desalination plant using artificial neural network and tree model
Yongjun Choi1, Younggeun Lee2, Kwanghee Shin2, Youngkyu Park1, and Sangho Lee1
1School of Civil and Environmental Engineering, Kookmin University, Jeongneung-Dong, Seoul, 02707, Korea
2Cooperate R&D Institute, Doosan Heavy Industries & Construction, Co., Ltd., 16858, Korea
Corresponding Author: Sangho Lee ,Tel: 82-2-910-4529 , Fax: 82-2-910-4939, Email: sanghlee@kookmin.ac.kr
Received: July 29, 2019;  Accepted: October 17, 2019.
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The reverse osmosis (RO) technology is currently the leading desalination method. However, until recently, application of RO technology on a large scale has been primarily limited by membrane fouling. The mechanism of fouling is complex, which is not well understood in full-scale plants. Although many studies about modeling and prediction of fouling have been done, in most cases, the experimental data set of lab or pilot scale systems, which may not show fouling characteristics well in full-scale systems were used. In this study, both artificial neural network (ANN) model and tree model (TM) was evaluated to analyze long-term performance of full scale reverse osmosis desalination plant. The results of application of the ANN and TM indicated high correlation coefficients between the measured and simulated output variables. However, it is not easy to use ANN for the full scale plant operation because the final model is not expressed as a form of mathematical functions. TM has advantages over ANN because the model can be obtained as forms of simple function and it showed reasonably high R2. Therefore, TM is shown to be more adequate than ANN for developing models in which the full-scale RO plant data is considered as an input.
Keywords: Artificial neural network | Fouling | Reverse osmosis | Statistical analysis | Tree model
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