International Journals
31. Kim, M.1, Jeong, K.1, Kang, M., Im, S.*, Jang, A.* Predicting performance and economic assessment of ultra-filtration and reverse osmosis process using pilot scale water reuse plant big data base, In preparation.
30. Park, J., Jeong, W., Jeong, K.*, Kim, B.* Machine learning–based prediction of free chlorine generation and pH behavior in electrochemical disinfection system driven by microbial fuel cells, In preparation.
29. Jang, H., Cho, S., Kadam, R., Jeong, K., Park, J.* (2026) Interpretable STL-LightGBM forecasting and monitoring of biogas production in a full-scale anaerobic co-digestion plant, Process Safety and Environmental Protection 218, 109495. (IF=7.9)
doi.org/10.1016/j.psep.2026.109495
28. Moon, J.1, Yun, B.1, Park, K., Kim, S.S., Lee, Y., Jeong, K.*, Cho, K.H.* (2026) Adaptive reinforcement learning for energy-efficient high-recovery closed-circuit reverse osmosis, Water Research 299, 125855. (IF=12.4, Top 1.1% in Water Resources)
https://doi.org/10.1016/j.watres.2026.125855
27. Yun, B., Shim, J., Moon, J., Lee, S., Jeong, K.*, Cho, K.H.* (2025) Towards autonomous operation of reverse osmosis water treatment system with multi-agent reinforcement learning, Desalination 609, 118870. (IF=9.9, Top 2.7% in Water Resources)
https://doi.org/10.1016/j.desal.2025.118870
26. Moon, J., Jeong, K., Chae, S.H., Shim, J., Kim, J., Cho, K.H.*, Park, K.* (2025) Robust deep learning model combined with missing input data estimation: Application in a 1,000 m3/day high-salinity SWRO plant, Desalination 603, 118678. (IF=9.9, Top 2.7% in Water Resources)
https://doi.org/10.1016/j.desal.2025.118678
25. Nurhayati, M.1, Jeong, K.1, Kim, S., Park, J., Cho, K.H., Shon, H.K., Lee, S.* (2025) From comparison to integration: enhancing forward osmosis performance prediction with mathematical and RBF neural network models, Desalination 597, 118322. (IF=9.9, Top 2.7% in Water Resources)
https://doi.org/10.1016/j.desal.2024.118322
24. Nurhayati, M.1, Jeong, K.1, Park, J., Hong, B.U., Kang, H.G., Shon, H.K., Lee, S.* (2024) Predicting and optimizing forward osmosis membrane operation using machine learning, Desalination 592, 118154. (IF=9.8, Top 2.7% in Water Resources)
https://doi.org/10.1016/j.desal.2024.118154
23. Park, S., Son, M., Shim, J., Jeong, K.*, Cho, K.H.* (2022) Physically-assisted removal of organic fouling by osmotic backwashing coupled with chemical cleaning, Journal of Cleaner Production 378, 134490. (IF=9.9, Top 7.8% in Environmental Sciences)
https://doi.org/10.1016/j.jclepro.2022.134490
22. Son, M.1, Shim, J.1, Park, S., Yoon, N., Jeong, K., Cho, K.H.*, (2022) Seawater battery desalination with sodium-intercalation cathode for hypersaline water treatment, Desalination 531, 115713. (IF=9.9, Top 2.4% in Water Resources)
https://doi.org/10.1016/j.desal.2022.115713
21. Jeong, K., Son, M., Yoon, N., Park, S., Shim, J., Kim, J., Lim, J.L., Cho, K.H.* (2021) Modeling and evaluating performance of full-scale reverse osmosis system in industrial water treatment plant, Desalination 518, 115289. (IF=11.211, Top 2.5% in Water Resources)
https://doi.org/10.1016/j.desal.2021.115289
20. Son, M., Yoon, N., Jeong, K., Abbas, A., Logan, B.E., Cho, K.H.* (2021) Deep learning for pH prediction in water desalination using membrane capacitive deionization, Desalination 516, 115233. (IF=11.211, Top 2.5% in Water Resources)
https://doi.org/10.1016/j.desal.2021.115233
19. Jeong, K.1, Abbas, A.1, Shin, J., Son, M., Kim, Y.M.*, Cho, K.H.* (2021) Prediction of biogas production in anaerobic co-digestion of organic wastes with deep learning models, Water Research 205, 117697. (IF=13.4, Top 1.5% in Water Resources)
https://doi.org/10.1016/j.watres.2021.117697
18. Yoon, N., Kim, J., Lim, J.L., Abbas, A., Jeong, K.*, Cho, K.H.* (2021) Dual-stage attention-based LSTM for simulating performance of brackish water treatment, Desalination 512, 115107. (IF=11.211, Top 2.5% in Water Resources)
https://doi.org/10.1016/j.desal.2021.115107
17. Son, M., Jeong, K., Yoon, N., Shim, J., Park, S., Park, J.*, Cho, K.H.* (2021) Pharmaceutical removal at low energy consumption using membrane capacitive deionization, Chemosphere 276, 130133. (IF=8.943, Top 11.65% in Environmental Sciences)
https://doi.org/10.1016/j.chemosphere.2021.130133
16. Park, S., Jeong, Y.D., Lee, J.H., Kim, J., Jeong, K.*, Cho, K.H.* (2021) 3D printed honeycomb-shaped feed channel spacer for membrane fouling mitigation in nanofiltration, Journal of Membrane Science 620, 118665. (IF=10.53, Top 3.89% in Polyer Science)
https://doi.org/10.1016/j.memsci.2020.118665
15. Shim, J.1, Yoon, N.1, Park, S., Park, J., Son, M., Jeong, K.*, Cho, K.H.* (2021) Influence of natural organic matters on membrane capacitive deionization performance, Chemosphere 264, 128519. (IF=8.943, Top 11.65% in Environmental Sciences)
https://doi.org/10.1016/j.chemosphere.2020.128519
14. Jeong, K., Yoon, N., Park, S., Son, M., Lee, J., Park, J.*, Cho, K.H.* (2020) Optimization of a nanofiltration and membrane capacitive deionization (NF-MCDI) hybrid system: Experimental and modeling studies, Desalination 493, 114658. (IF=9.501, Top 2.55% in Water Resources)
https://doi.org/10.1016/j.desal.2020.114658
13. Son, M., Cho, K.H., Jeong, K.*, Park, J.* (2020) Membrane and Electrochemical Processes for Water Desalination: A Short Perspectives and the Role of Nanotechnology. Membranes 10(10), 280. (IF=4.106)
https://doi.org/10.3390/membranes10100280
12. Chun, Y., Jeong, K.*, Cho, K.H. (2020) Influences of Combined Organic Fouling and Inorganic Scaling on Flux and Fouling Behavior in Forward Osmosis. Membranes 10(6), 115. (IF=4.106)
https://doi.org/10.3390/membranes10060115
11. Lim, S.J., Jeong, K., Cho, Y., Park, J., Lee, T., Ki, S.J.*, Kim, J.H. (2020) Improvement of reverse osmosis process stability in internally staged design under seasonal variation of feed water. Desalination and Water Treatment 180, 95-103. (IF=1.254)
https://doi.org/10.5004/dwt.2020.25165
10. Tay, M.F., Lee, S., Xu, H., Jeong, K., Liu, C., Cornelissen, E.R., Wu, B.*, Chong, T.H.* (2020) Impact of salt accumulation in the bioreactor on the performance of nanofiltration membrane bioreactor (NF-MBR)+Reverse osmosis (RO) process for water reclamation. Water Research 170, 115352. (IF=11.236, Top 1.53% in Water Resources)
https://doi.org/10.1016/j.watres.2019.115352
9. Jeong, K., Park, M., Oh, S., Kim, J.H.* (2020) Impacts of flow channel geometry, hydrodynamic and membrane properties on osmotic backwash of RO membranes—CFD modeling and simulation. Desalination 476, 114229. (IF=9.501, Top 2.55% in Water Resources)
https://doi.org/10.1016/j.desal.2019.114229
8. Lim, S.J., Ki, S.J.*, Seo, J., Chae, S.H., Lee, Y.G., Jeong, K., Park, J., Kim, J.H. (2019) Evaluating the performance of extended and unscented Kalman filters in the reverse osmosis process. Desalination and Water Treatment 163, 118-124. (IF=0.854)
https://doi.org/10.5004/dwt.2019.24408
7. Park, J.K., Jeong, K., Baek, S.S., Park, S.H., Ligaray, M., Chong, T.H., Cho, K.H.* (2019) Modeling of NF/RO membrane fouling and flux decline using real-time observations. Journal of Membrane Science 576, 66–77. (IF=7.183, Top 2.81% in Polyer Science)
https://doi.org/10.1016/j.memsci.2019.01.031
6. Lim, S.J., Kim, Y.M.*, Park, H., Ki, S.J., Jeong, K., Seo, J., Chae, S.H., Kim, J.H. (2019) Enhancing accuracy of membrane fouling prediction using hybrid machine learning models. Desalination and Water Treatment 146, 22-28. (IF=1.234)
https://doi.org/10.5004/dwt.2019.23444
5. Jeong, K., Park, M., Chong, T.H.* (2019) Numerical model-based analysis of energy-efficient reverse osmosis (EERO) process: Performance simulation and optimization. Desalination 453, 10–21. (IF=7.098, Top 1.6% in Water Resources)
https://doi.org/10.1016/j.desal.2018.11.021
4. Jeong, K., Park, M., Ki, S.J., Kim, J.H.* (2017) A systematic optimization of Internally Staged Design (ISD) for a full-scale reverse osmosis process. Journal of Membrane Science 540, 285–296. (IF=6.578, Top 2.87% in Polyer Science)
https://doi.org/10.1016/j.memsci.2017.06.066
3. Jeong, K., Lee, Y.G., Ki, S.J., Kim, J.H.* (2016) Modeling seawater reverse osmosis system under degradation conditions of membrane performance: Assessment of isobaric energy recovery devices and feed pressure control benefits. Desalination and Water Treatment 57, 20210–20218. (IF=1.272)
https://doi.org/10.1080/19443994.2015.1112980
2. Kim, J., Jeong, K., Park, M.J., Shon, H.K., Kim, J.H. * (2015) Recent Advances in Osmotic Energy Generation via Pressure-Retarded Osmosis (PRO): A Review. Energies 8(10), 11821–11845. (IF=1.602)
https://doi.org/10.3390/en81011821
1. Guo H., Jeong, K., Lim, J., Jo, J., Kim, Y.M., Park, J., Kim, J.H., Cho, K.H.* (2015) Prediction of Effluent Concentration in a Wastewater Treatment Plant using Machine Learning Models. Journal of Environmental Sciences 32, 90–101. (IF=2.002)
Domestic Journals
4. Choe, Y., Jeong, K.* (2026). Groundwater Level Prediction Modeling in Coastal Aquifers Using Tree-Based Ensemble Machine Learning and SHAP. Joural of Korean Society on Water Environment 42(4), 301-314.
doi.org/10.15681/KSWE.2026.42.4.301
3. Kang, D.H.1, Jeong, K.1, Jeong, Y., Song, S.H., Lee, S., Nam, S.Y., Jang, J.K., Yang, E.* (2023). Evaluating the Efficacy of Commercial Polysulfone Hollow Fiber Membranes for Separating H2 from H2/CO Gas Mixtures. Membrane Journal 33(6), 352-361.
doi.org/10.14579/MEMBRANE_JOURNAL.2023.33.6.352
2. Lee, M.J., Kim, H.S.*, Kim, J.Y., Jeong, K. (2022). A Study on the Improvement of River and Source Water Quality Using an Infiltration Constructed Wetland as an Eco-Friendly Water Treatment Technology. Joural of Korean Society of Water Science and Technology 30(6), 31-41.
kiss.kstudy.com/Detail/Ar?key=3993510
1. Lee, Y., Kim, S.S., Seo, I., Jeong, K.* (2022). Study on Retrofitting of Small-scale Water Supply Facility using aeration and Closed-circuit Reverse Osmosis. J. Korean Soc. Environ. Eng. 44(11), 436-443.