광산배수 처리를 위한 기계학습 기반 소석회 투입량 예측 연구
Study on Machine Learning-based Prediction of Lime Dosage for Mine Drainage Treatment
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
본 연구는 광산배수 처리시설의 효율적 설계와 운영을 위해 기계학습(ML) 기법으로 광산배수 처리시설(ST와 HT)의 소석회 투입량을 예측하였다. 처리시설 원본 자료에서 이상치를 제거한 후, 소석회 투입량과 관계가 있는 유량, 그리고 소석회에 의해 공급된 OH‒와 상당히 관계가 있는 금속이온(Fe, Mn, Al) 농도와 pH를 입력자료로 선정하였다. 합성곱 신경망(CNN) 모델이 사용되었고, 원본 자료의 제한적이고 불균형적인 자료의 문제를 보완하면서 원본 자료 특성을 유지하기 위해fancy 주성분 분석(PCA)를 이용한 자료증대 기법이 사용되었다. ML 모델에 의해 예측된 테스트자료 세트의 소석회 투입량은 이론적 계산식보다 평균 절대 오차(MAE)는 낮고 결정계수(R2)는 높았다. 기계학습 기법의 사용으로 처리시설의 운영효율 증가 및 신규 시설에의 기본 설계자료 제공이 기대된다.
Abstract
In this study, we predicted the lime dosage for treatment facilities (ST and HT) using machine learning (ML) to effectively design and operate mine drainage treatment facilities. After removing the bad data from the original data, the flow rate related to the lime dosage, and metal ion (Fe, Mn, Al) concentrations and the pH which are highly related to OH‒ supplied by lime, were selected as inputs. The convolutional neural network was used, and the data was augmented using fancy principal component analysis to compensate for the limited and imbalanced dataset while maintaining the characteristics of the original data. The test dataset prediction by the ML model demonstrated a lower mean absolute error and higher coefficient of determination (R2) than that of the theoretical calculation equation. The ML application is expected to enhance operational effectiveness of treatment facilities and offer fundamental design data for new facilities.
참고문헌 (22)
- Alam, G., "Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes:Recent advances and prospects", Chemical Engineering Journal, 427, pp.130011~ (2022)
- Cravotta, C. A., "Interactive PHREEQ-N-AMDTreat Water-Quality Modeling Tools to Evaluate Performance and Design of Treatment Systems for Acid Mine Drainage (Software Download)", U.S. Geological Survey Software Release, ~ (2020)
- Duda, R. O., "Pattern classification", pp.654~ (2000), John Wiley & Sons, Inc
- "Geo Big Data Open Platform" (2024)[URL]
- Gholami, R., "Specification and prediction of nickel mobilization using artificial intelligence methods", Central European Journal of Geosciences, 3(4), pp.375~384 (2011)
- Hyung, J. S., "Development of operation diagnosis and optimal decision making model based on big data for drinking water treatment process", PhD, pp.2022
- Joo, H. G., "A water quality prediction model using big data and machine learning of sewage treatment facilities", MS, pp.2022
- Jun, G. I., "Comparing the performance of machine learning algorithms in predicting river water quality and quantity", KSWST Journal of Water Treatment, 28(1), pp.49~57 (2020)
- Khandelwal, M., "Prediction of mine water quality by physical parameters", Journal of Scientific & Industrial Research, 64, pp.564~570 (2005)
- Kim, D. K., "Predicting mineralogy by integrating core and well log data using a deep neural network", Journal of Petroleum Science and Engineering, 195, pp.107838~ (2020)
- Kim, D. M., "Underestimation of alkaline dosage and precipitate amount during water treatment: Role of inorganic carbon and use of PHREEQ-NAMDTreat", Journal of Cleaner Production, 433, pp.139683~ (2023)
- Kim, J. Y., "Determination of coagulant input rate in water purification plant using K-means algorithm and GBR algorithm", Journal of the Korea Institute of information and communication engineering, 25(6), pp.792~798 (2021)
- Kim, S. Y., "Development of prediction model for greenhouse control based on machine learning", Journal of Digital Contents Society, 19(4), pp.749~756 (2018)
- Kim, T. H., "A study on development of optimal process management model for water treatment plant using deep learning based on big data", PhD, pp.2021
- "KOMIR (Korea Mine Rehabilitation and Mineral ResourcesCorporation) MiRe GIS" (2024)[URL]
- Lee, C. Y., "Case analysis for introduction of machine learning technology to the mining industry", Tunnel & underground space, 29(1), pp.1~11 (2019)
- Implementation Design Report for Filtration Facility of Samtan Leachate Treatment Facilities, "MIRECO(Mine Reclamation Corporation)", MIRECO, 108~ (2019)
- "Newsis" (2024)[URL]
- Park, M. S., "Assessment on impact factor for dehydration of mine drainage sludge using flocculant and dewatering tube (KOMIR-Tube System)", Economic and Environmental Geology, 57(2), pp.263~270 (2024)
- "Timeanddate" (2024)[URL]
- "Weather and Climate" (2024)[URL]
- Younger, P. L., "Mine Water: Hydrology, Pollution, Remediation", pp.442~ (2002), Kluwer Academic Publishers