Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Prediction model for electric power consumption of seawater desalination based on machine learning by seawater quality change in future

장래 해수수질 변화에 따른 머신러닝 기반 해수담수 전력비 예측 모형 개발

  • Shim, Kyudae (Environment Solution Team, GS Engineering & Construction) ;
  • Ko, Young-Hee (AI Big Data MBA, Seoul School of Integrated Sciences & Technologies)
  • 심규대 (GS건설 환경솔루션팀) ;
  • 고영희 (서울과학종합대학원대학교 AI빅데이터 MBA)
  • Received : 2021.08.23
  • Accepted : 2021.09.27
  • Published : 2021.12.31
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Abstract

The electricity cost of a desalination facility was also predicted and reviewed, which allowed the proposed model to be incorporated into the future design of such facilities. Input data from 2003 to 2014 of the Korea Hydrographic and Oceanographic Agency (KHOA) were used, and the structure of the model was determined using the trial and error method to analyze as well as hyperparameters such as salinity and seawater temperature. The future seawater quality was estimated by optimizing the prediction model based on machine learning. Results indicated that the seawater temperature would be similar to the existing pattern, and salinity showed a gradual decrease in the maximum value from the past measurement data. Therefore, it was reviewed that the electricity cost for seawater desalination decreased by approximately 0.80% and a process configuration was determined to be necessary. This study aimed at establishing a machine-learning-based prediction model to predict future water quality changes, reviewed the impact on the scale of seawater desalination facilities, and suggested alternatives.

본 연구는 머신러닝 기반의 분석으로 해수담수화(Desalination) 시설의 전력비 예측모델의 가능성을 검토하였다. 해수담수화 주요 공정인 역삼투(Seawater Reverse Osmosis) 시설의 전력비 예측 모델을 개발하고, 전력비 산정에 영향을 미치는 인자를 분석하였으며, 해수 수질 중에서 선정된 수온 및 염분도 측정자료를 활용하여 검토하였다. 국립해양조사원(Korea Hydrographic and Oceanographic Agency, KHOA)의 2003년부터 2014년까지의 자료를 이용하였으며, 모형의 구조는 시행오차법(Trial & Error)으로 하이퍼파라미터를 최적화하여 머신러닝 기반의 예측 모델을 구축하고, 장래 해수 수질을 예측하였다. 해수 수온은 기존 패턴과 유사할 것으로 예측되었고, 염분도는 과거 측정자료 범위 이내로 최대값이 점차 감소되는 경향을 보여 해수담수화의 전력비가 약 0.80% 감소하는 것으로 검토되었다. 본 연구는 머신러닝 기반의 예측 모델을 구축하여 장래 수질 변화 예측하였으며, 해수 수질 변동의 영향 및 대안을 제시했다는데 의의가 있다.

Keywords

Acknowledgement

본 연구논문은 서울과학종합대학원대학교 AI 빅데이터 MBA 학위 논문을 수정·보완하여 작성되었습니다.

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