Research Paper
Alam, G., Ihsanullah, I., Naushad, M., and Sillanpää, M., 2022. Applications of artificial intelligence in water treatment for optimization and automation of adsorption processes: Recent advances and prospects, Chemical Engineering Journal, 427, 130011.
10.1016/j.cej.2021.130011Cravotta, C.A., 2020. 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.
10.1016/j.apgeochem.2020.104845Duda, R.O., Hart, P.E., and Stork, D.G., 2000. Pattern classification, John Wiley & Sons, Inc., New York, USA, 654p.
Geo Big Data Open Platform, 2024.10.03., https://data.kigam.re.kr/mgeo/map/main.do?process=geology_50k
Gholami, R., Ziaii, M., Ardejani, F.D., and Maleki, S., 2011. Specification and prediction of nickel mobilization using artificial intelligence methods, Central European Journal of Geosciences, 3(4), p.375-384.
10.2478/s13533-011-0039-xHyung, J.S., 2022. Development of operation diagnosis and optimal decision making model based on big data for drinking water treatment process, PhD Thesis, University of Seoul, Seoul, Korea, 278p.
Joo, H.G., 2022. A water quality prediction model using big data and machine learning of sewage treatment facilities, MS Thesis, Hanbat National University, Daejeon, Korea, 48p.
Jun, G.I., Kwon, D.H., and Ki, S.J., 2020. Comparing the performance of machine learning algorithms in predicting river water quality and quantity, KSWST Journal of Water Treatment, 28(1), p.49-57.
10.17640/KSWST.2020.28.1.49Khandelwal, M. and Singh, T. N., 2005. Prediction of mine water quality by physical parameters, Journal of Scientific & Industrial Research, 64, p.564-570.
Kim, D.K., Choi, J.W., Kim, D.W., and Byun, J.M., 2020. Predicting mineralogy by integrating core and well log data using a deep neural network, Journal of Petroleum Science and Engineering, 195, 107838.
10.1016/j.petrol.2020.107838Kim, D.M., Kwon, H.L., Park, M.S., 2023. Underestimation of alkaline dosage and precipitate amount during water treatment: Role of inorganic carbon and use of PHREEQ-N-AMDTreat, Journal of Cleaner Production, 433, 139683.
10.1016/j.jclepro.2023.139683Kim, J.Y., Kang, B.S., and Jung, H.K., 2021. 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), p.792-798.
Kim, S.Y., Park, K.S., Lee, S.M., Heo, B.M., and Ryu, K.H., 2018. Development of prediction model for greenhouse control based on machine learning, Journal of Digital Contents Society, 19(4), p.749-756.
Kim, T.H., 2021. A study on development of optimal process management model for water treatment plant using deep learning based on big data, PhD Thesis, University of Seoul, Seoul, Korea, 295p.
KOMIR (Korea Mine Rehabilitation and Mineral Resources Corporation) MiRe GIS, 2024.10.11., https://miregis.komir.or.kr/mine/mineStateView.do
Lee, C.Y., Kim, S.M., and Choi, Y.S., 2019. Case analysis for introduction of machine learning technology to the mining industry, Tunnel & underground space, 29(1), p.1-11.
MIRECO (Mine Reclamation Corporation), 2019. Implementation Design Report for Filtration Facility of Samtan Leachate Treatment Facilities, MIRECO Report, Wonju, Korea, 108p. (in Korean)
- Publisher :The Korean Society of Mineral and Energy Resources Engineers
- Publisher(Ko) :한국자원공학회
- Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
- Journal Title(Ko) :한국자원공학회지
- Volume : 61
- No :5
- Pages :333-346
- Received Date : 2024-10-04
- Revised Date : 2024-10-15
- Accepted Date : 2024-10-17
- DOI :https://doi.org/10.32390/ksmer.2024.61.5.333