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http://dx.doi.org/10.7469/JKSQM.2020.48.1.171

A Study on the Prediction Model Considering the Multicollinearity of Independent Variables in the Seawater Reverse Osmosis  

Han, In sup (Uneedcomms Sales Team)
Yoon, Yeon-Ah (Department of Industrial and Management Engineering, Kyonggi University)
Chang, Tai-Woo (Department of Industrial and Management Engineering, Kyonggi University)
Kim, Yong Soo (Department of Industrial and Management Engineering, Kyonggi University)
Publication Information
Journal of Korean Society for Quality Management / v.48, no.1, 2020 , pp. 171-186 More about this Journal
Abstract
Purpose: The purpose of this study is conducting of predictive models that considered multicollinearity of independent variables in order to carry out more efficient and reliable predictions about differential pressure in seawater reverse osmosis. Methods: The main variables of each RO system are extracted through factor analysis. Common variables are derived through comparison of RO system # 1 and RO system # 2. In order to carry out the prediction modeling about the differential pressure, which is the target variable, we constructed the prediction model reflecting the regression analysis, the artificial neural network, and the support vector machine in R package, and figured out the superiority of the model by comparing RMSE. Results: The number of factors extracted from factor analysis of RO system #1 and RO system #2 is same. And the value of variability(% Var) increased as step proceeds according to the analysis procedure. As a result of deriving the average RMSE of the models, the overall prediction of the SVM was superior to the other models. Conclusion: This study is meaningful in that it has been conducting a demonstration study of considering the multicollinearity of independent variables. Before establishing a predictive model for a target variable, it would be more accurate predictive model if the relevant variables are derived and reflected.
Keywords
Seawater Reverse Osmosis; Factor Analysis; Multicollinearity; Prediction Model;
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Times Cited By KSCI : 4  (Citation Analysis)
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