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Analyzing Operation Deviation in the Deasphalting Process Using Multivariate Statistics Analysis Method
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  • Analyzing Operation Deviation in the Deasphalting Process Using Multivariate Statistics Analysis Method
  • Analyzing Operation Deviation in the Deasphalting Process Using Multivariate Statistics Analysis Method
저자명
Park. Joo-Hwang,Kim. Jong-Soo,Kim. Tai-Suk
간행물명
멀티미디어학회논문지
권/호정보
2014년|17권 7호|pp.858-865 (8 pages)
발행정보
한국멀티미디어학회
파일정보
정기간행물|ENG|
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이 논문은 한국과학기술정보연구원과 논문 연계를 통해 무료로 제공되는 원문입니다.
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기타언어초록

In the case of system like MES, various sensors collect the data in real time and save it as a big data to monitor the process. However, if there is big data mining in distributed computing system, whole processing process can be improved. In this paper, system to analyze the cause of operation deviation was built using the big data which has been collected from deasphalting process at the two different plants. By applying multivariate statistical analysis to the big data which has been collected through MES(Manufacturing Execution System), main cause of operation deviation was analyzed. We present the example of analyzing the operation deviation of deasphalting process using the big data which collected from MES by using multivariate statistics analysis method. As a result of regression analysis of the forward stepwise method, regression equation has been found which can explain 52% increase of performance compare to existing model. Through this suggested method, the existing petrochemical process can be replaced which is manual analysis method and has the risk of being subjective according to the tester. The new method can provide the objective analysis method based on numbers and statistic.