Journal of Korean Society of Industrial and Systems Engineering (산업경영시스템학회지)

Society of Korea Industrial and System Engineering (한국산업경영시스템학회)
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Machine Learning Based Coagulant Rate Decision Model for Industrial Water Treatment Plant

머신러닝 기반의 공업용수 정수장 응집제 주입률 결정

  • Kyungsu, Park (Department of Business Administration, Pusan National University) ;
  • Yu-jin Lee (Department of Business Administration, Pusan National University) ;
  • Haneul Noh (Department of Business Administration, Pusan National University) ;
  • Jun Heo (Korea Water Resources Corporation) ;
  • Seung Hwan Jung (School of Business, Yonsei University)
  • 박경수 (부산대학교 경영학과) ;
  • 이유진 (부산대학교 경영학과) ;
  • 노하늘 (부산대학교 경영학과) ;
  • 허준 (한국수자원공사) ;
  • 정승환 (연세대학교 경영학과)
  • Received : 2024.08.06
  • Accepted : 2024.09.02
  • Published : 2024.09.30
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Abstract

This study develops a model to determine the input rate of the chemical for coagulation and flocculation process (i.e. coagulant) at industrial water treatment plant, based on real-world data. To detect outliers among the collected data, a two-phase algorithm with standardization transformation and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is applied. In addition, both of the missing data and outliers are revised with linear interpolation. To determine the coagulant rate, various kinds of machine learning models are tested as well as linear regression. Among them, the random forest model with min-max scaled data provides the best performance, whose MSE, MAPE, R2 and CVRMSE are 1.136, 0.111, 0.912, and 18.704, respectively. This study demonstrates the practical applicability of machine learning based chemical input decision model, which can lead to a smart management and response systems for clean and safe water treatment plant.

Keywords

Acknowledgement

This work was supported by Yonsei Business Research Institute and National Research Foundation of Korea (NRF) grant funded by the Korea government(MSIT) (No. 2022R1C1C101173111).

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