품질경영학회지 (Journal of Korean Society for Quality Management)

  • 제48권1호
  • /
  • Pages.171-186
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  • 2020
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  • 1229-1889(pISSN)
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  • 2287-9005(eISSN)
한국품질경영학회 (Korean Society for Quality Management)
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역삼투압 해수담수화(SWRO) 플랜트에서 독립변수의 다중공선성을 고려한 예측모델에 관한 연구

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

  • 한인섭 (유니드컴즈 세일즈팀) ;
  • 윤연아 (경기대학교 일반대학원 산업경영공학과) ;
  • 장태우 (경기대학교 산업경영공학과) ;
  • 김용수 (경기대학교 산업경영공학과)
  • 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)
  • 투고 : 2020.01.23
  • 심사 : 2020.02.18
  • 발행 : 2020.03.31
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초록

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.

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참고문헌

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