| An optimization strategy for a forward osmosis-reverse osmosis hybrid process for wastewater reuse and seawater desalination: A modeling study J Seo, YM Kim, SH Chae, SJ Lim, H Park, JH Kim Desalination 463, 40-49, 2019 | 53 | 2019 |
| A comprehensive review of the feasibility of pressure retarded osmosis: Recent technological advances and industrial efforts towards commercialization C Lee, SH Chae, E Yang, S Kim, JH Kim, IS Kim Desalination 491, 114501, 2020 | 51 | 2020 |
| Pressure retarded osmosis: renewable energy generation and recovery K Touati, F Tadeo, JH Kim, OAA Silva, SH Chae Academic Press, 2017 | 35 | 2017 |
| A simulation study with a new performance index for pressure-retarded osmosis processes hybridized with seawater reverse osmosis and membrane distillation SH Chae, J Seo, J Kim, YM Kim, JH Kim Desalination 444, 118-128, 2018 | 28 | 2018 |
| Modeling and simulation studies analyzing the pressure-retarded osmosis (PRO) and PRO-hybridized processes SH Chae, YM Kim, H Park, J Seo, SJ Lim, JH Kim Energies 12 (2), 243, 2019 | 26 | 2019 |
| Enhancing accuracy of membrane fouling prediction using hybrid machine learning models. SJ Lim, YM Kim, H Park, S Ki, K Jeong, J Seoe, SH Chae, JH Kim Desalination and water treatment 146, 22-28, 2019 | 10 | 2019 |
| Integration of PRO into desalination processes SH Chae, JH Kim Pressure Retarded Osmosis, 129-151, 2017 | 10* | 2017 |
| Recent issues relative to a low salinity pressure-retarded osmosis process and suggested technical solutions SH Chae, JH Kim Membrane-Based Salinity Gradient Processes for Water Treatment and Power …, 2018 | 8 | 2018 |
| Theoretical analysis of a mathematical relation between driving pressures in membrane-based desalting processes SH Chae, JH Kim Membranes 11 (3), 220, 2021 | 4 | 2021 |
| Modeling study of the effects of intrinsic membrane parameters on dilutive external concentration polarization occurring during forward and pressure-retarded osmosis SH Chae, H Rho, S Moon Desalination 569, 117043, 2024 | 3 | 2024 |
| Clustering micropollutants and estimating rate constants of sorption and biodegradation using machine learning approaches SJ Lim, J Seo, MG Seid, J Lee, WW Ejerssa, DH Lee, E Jeong, SH Chae, ... Npj Clean Water 6 (1), 69, 2023 | 2 | 2023 |
| Predicting micropollutant fate during wastewater treatment using refined classical kinetic model based on quantitative monitoring in multi-metropolitan regions of South Korea SH Chae, SJ Lim, MG Seid, WW Ejerssa, A Son, H Son, S Choi, W Lee, ... Water Research 245, 120627, 2023 | 2 | 2023 |
| Evaluating the performance of extended and unscented Kalman filters in the reverse osmosis process SJ Lima, SJ Kib, J Seoa, SH Chaea, YG Leec, K Jeongd, J Parke, ... Desalination and Water Treatment 163, 118-124, 2019 | 2 | 2019 |
| Economic analysis on environmentally sound brine disposal with RO and RO-hybrid processes SH Chae, J Kim, YM Kim, SH Kim, JH Kim Desalination and Water Treatment, 1-11, 2017 | 2 | 2017 |
| Adsorption of uranyl ion on hexagonal boron nitride for remediation of real U-contaminated soil and its interpretation using random forest BM Jun, SH Chae, D Kim, JY Jung, TJ Kim, SN Nam, Y Yoon, C Park, ... Journal of Hazardous Materials 469, 134072, 2024 | 1 | 2024 |
| Investigation on the occurrence and fate of micropollutants in domestic wastewater treatment plants based on full-scale monitoring and simple statistical analysis SH Chae, SJ Lim, J Lee, SM Gashaw, W Lee, S Choi, Y Lee, W Lee, ... Journal of Korean Society of Water and Wastewater 36 (2), 107-119, 2022 | 1 | 2022 |
| A forecast for the environmental effect of pressure-retarded osmosis on CO2 emissions from seawater reverse osmosis in Korea: A scenario-based study SH Chae, JH Kim Desalination and Water Treatment 219, 405-412, 2021 | 1 | 2021 |
| Metadata and feature importance analyses of membrane capacitive deionization models: Is a water treatment artificial intelligence panacea possible? SH Chae, SW Hong, M Son Desalination 585, 117784, 2024 | | 2024 |
| Performance investigation of osmotically assisted reverse osmosis using explainable machine learning models: A comparative study SH Chae, S Moon, SW Hong, C Lee, M Son Desalination 583, 117647, 2024 | | 2024 |
| Determining water and solute permeability of reverse osmosis membrane using a data-driven machine learning pipeline SH Chae, SW Hong, M Son, KH Cho Journal of Water Process Engineering 64, 105634, 2024 | | 2024 |
Joon Ha KimProfessor, School of Environmental Sci & Eng in Gwangju Institute of Science and Technology (GIST)Verified email at gist.ac.kr
