Journal of Korean Society for Quality Management (품질경영학회지)

Korean Society for Quality Management (한국품질경영학회)
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A Machine Learning Model for Predicting Silica Concentrations through Time Series Analysis of Mining Data

광업 데이터의 시계열 분석을 통해 실리카 농도를 예측하기 위한 머신러닝 모델

  • Lee, Seung Hoon (Department of Industrial and Management Engineering, Kyonggi University) ;
  • Yoon, Yeon Ah (Department of Industrial and Management Engineering, Kyonggi University Graduate School) ;
  • Jung, Jin Hyeong (Department of Industrial and Management Engineering, Kyonggi University Graduate School) ;
  • Sim, Hyun su (Department of Industrial and Management Engineering, Kyonggi University Graduate School) ;
  • Chang, Tai-Woo (Department of Industrial and Management Engineering, Kyonggi University) ;
  • Kim, Yong Soo (Department of Industrial and Management Engineering, Kyonggi University)
  • 이승훈 (경기대학교 산업경영공학과) ;
  • 윤연아 (경기대학교 일반대학원 산업경영공학과) ;
  • 정진형 (경기대학교 일반대학원 산업경영공학과) ;
  • 심현수 (경기대학교 일반대학원 산업경영공학과) ;
  • 장태우 (경기대학교 산업경영공학과) ;
  • 김용수 (경기대학교 산업경영공학과)
  • Received : 2020.09.13
  • Accepted : 2020.09.21
  • Published : 2020.09.30
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Abstract

Purpose: The purpose of this study was to devise an accurate machine learning model for predicting silica concentrations following the addition of impurities, through time series analysis of mining data. Methods: The mining data were preprocessed and subjected to time series analysis using the machine learning model. Through correlation analysis, valid variables were selected and meaningless variables were excluded. To reflect changes over time, dependent variables at baseline were treated as independent variables at later time points. The relationship between independent variables and the dependent variable after n point was subjected to Pearson correlation analysis. Results: The correlation (R2) was strongest after 3 hours, which was adopted as a dependent variable. According to root mean square error (RMSE) data, the proposed method was superior to the other machine learning methods. The XGboost algorithm showed the best predictive performance. Conclusion: This study is important given the current lack of machine learning studies pertaining to the domestic mining industry. In addition, using time series analysis in mining data will show further improvement. Before establishing a predictive model for the proposed method, predictions should be made using data with time series characteristics. After doing this work, it should also improve prediction accuracy in other domains.

Keywords

References

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