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Practical modeling for assessing the efficiency of a full-scale reverse osmosis (RO) system may be a challenging task. This is because the operating conditions of RO systems can change significantly in actual practice owing to high seasonal variations and different progress of membrane fouling during long-term filtration. Accordingly, it is difficult to reliably model the RO performance if such conditions are excluded. In this study, we model a full-scale installation of a RO membrane system, considering actual operations of the industrial water treatment plant. A numerical model is built to describe spatiotemporal behavior of (water, salt, and foulant) mass transport inside a full-dimension pressure vessel. By performing a global sensitivity analysis, we evaluate the relative importance of key influential factors on model accuracy and specific energy consumption (SEC). The model and its parameters are optimized based on the sensitivity result and validated using best-fitted time-series measurement data of 3875 h. The results demonstrate the practical behaviors of fouling development and separation performance of the primary RO process. A regression tree analysis of SEC for 27 different operational scenarios in simulations may benefit decision making for energy efficient RO. Results reveal the high dependence of SEC on cleaning frequency in the feed temperature range.
Kwanho Jeong; Moon Son; Nakyung Yoon; Sanghun Park; Jaegyu Shim; Jihye Kim; Jae-Lim Lim; Kyung Hwa Cho. Modeling and evaluating performance of full-scale reverse osmosis system in industrial water treatment plant. Desalination 2021, 518, 115289 .
AMA StyleKwanho Jeong, Moon Son, Nakyung Yoon, Sanghun Park, Jaegyu Shim, Jihye Kim, Jae-Lim Lim, Kyung Hwa Cho. Modeling and evaluating performance of full-scale reverse osmosis system in industrial water treatment plant. Desalination. 2021; 518 ():115289.
Chicago/Turabian StyleKwanho Jeong; Moon Son; Nakyung Yoon; Sanghun Park; Jaegyu Shim; Jihye Kim; Jae-Lim Lim; Kyung Hwa Cho. 2021. "Modeling and evaluating performance of full-scale reverse osmosis system in industrial water treatment plant." Desalination 518, no. : 115289.
Rechargeable seawater battery (SWB) is a unique energy storage system that can directly transform seawater into renewable energy. Placing a desalination compartment between SWB anode and cathode (denoted as seawater battery desalination; SWB-D) enables seawater desalination while charging SWB. Since seawater desalination is a mature technology, primarily occupied by membrane-based processes such as reverse osmosis (RO), the energy cost has to be considered for alternative desalination technologies. So far, the feasibility of the SWB-D system based on the unit cost per desalinated water ($ m−3) has been insufficiently discussed. Therefore, this perspective aims to provide this information and offer future research directions based on the detailed cost analysis. Based on the calculations, the current SWB-D system is expected to have an equipment cost of ≈1.02 $ m−3 (lower than 0.60–1.20 $ m−3 of RO), when 96% of the energy is recovered and stable performance for 1000 cycles is achieved. The anion exchange membrane (AEM) and separator contributes greatly to the material cost occupying 50% and 41% of the total cost, respectively. Therefore, future studies focusing on creating low cost AEMs and separators will pave the way for the large-scale application of SWB-D.
Moon Son; Sanghun Park; Namhyeok Kim; Anne Therese Angeles; YoungSik Kim; Kyung Hwa Cho. Simultaneous Energy Storage and Seawater Desalination using Rechargeable Seawater Battery: Feasibility and Future Directions. Advanced Science 2021, 2101289 .
AMA StyleMoon Son, Sanghun Park, Namhyeok Kim, Anne Therese Angeles, YoungSik Kim, Kyung Hwa Cho. Simultaneous Energy Storage and Seawater Desalination using Rechargeable Seawater Battery: Feasibility and Future Directions. Advanced Science. 2021; ():2101289.
Chicago/Turabian StyleMoon Son; Sanghun Park; Namhyeok Kim; Anne Therese Angeles; YoungSik Kim; Kyung Hwa Cho. 2021. "Simultaneous Energy Storage and Seawater Desalination using Rechargeable Seawater Battery: Feasibility and Future Directions." Advanced Science , no. : 2101289.
The pH of a solution has a large influence on the ion removal efficiency of the membrane capacitive deionization (MCDI) process, an electrochemical ion separation process. We developed a convolutional neural network linked with a long short-term memory (CNN-LSTM) model based on an artificial intelligence algorithm to predict the effluent pH of MCDI, as effluent pH is difficult to predict using conventional numerical modeling. The model accurately predicted effluent pH (R2≥0.998) based on the analysis of five input variables (current, voltage, influent conductivity and pH, and effluent conductivity) under standard operating conditions of MCDI using either constant-current or constant-voltage conditions. The developed model predicted effluent pH using only limited input variables, current and voltage, with high accuracy (R2≥0.997). Thus, the CNN-LSTM model can be used in practical applications as only the current and voltage of MCDI cells are often monitored in field applications.
Moon Son; Nakyung Yoon; Kwanho Jeong; Ather Abass; Bruce E. Logan; Kyung Hwa Cho. Deep learning for pH prediction in water desalination using membrane capacitive deionization. Desalination 2021, 516, 115233 .
AMA StyleMoon Son, Nakyung Yoon, Kwanho Jeong, Ather Abass, Bruce E. Logan, Kyung Hwa Cho. Deep learning for pH prediction in water desalination using membrane capacitive deionization. Desalination. 2021; 516 ():115233.
Chicago/Turabian StyleMoon Son; Nakyung Yoon; Kwanho Jeong; Ather Abass; Bruce E. Logan; Kyung Hwa Cho. 2021. "Deep learning for pH prediction in water desalination using membrane capacitive deionization." Desalination 516, no. : 115233.
Remote sensing techniques have been applied to monitor the spatiotemporal variation of harmful algal blooms (HABs) in many inland waters. However, these studies have been limited to monitor the vertical distribution of HABs due to the optical complexity of inland water. Therefore, this study applied a deep neural network model to monitor the vertical distribution of Chlorophyll-a (Chl-a), phycocyanin (PC), and turbidity (Turb) using drone-borne hyperspectral imagery, in-situ measurement, and meteoroidal data. The pigment concentrations were measured between depths of 0 m and 5.0 m with 0.05 m intervals. Here, four state-of-the-art data-driven model structures (ResNet-18, ResNet-101, GoogLeNet, and Inception v3) were adopted for estimating the vertical distributions of the harmful algal pigments. Among the four models, the ResNet-18 model showed the best performance, with an R2 value of 0.70. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) substantially provided informative reflectance band ranges near 490 nm and 620 nm in the hyperspectral image for the vertical estimation of pigments. Therefore, this study demonstrated that the explainable deep learning model with drone-borne hyperspectral images has the potential to estimate Chl-a, PC, and Turb vertical distributions and to show influential features that contribute to describing the vertical profile phenomena.
Seok Min Hong; Sang-Soo Baek; Daeun Yun; Yong-Hwan Kwon; Hongtao Duan; Jongcheol Pyo; Kyung Hwa Cho. Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models. Science of The Total Environment 2021, 794, 148592 .
AMA StyleSeok Min Hong, Sang-Soo Baek, Daeun Yun, Yong-Hwan Kwon, Hongtao Duan, Jongcheol Pyo, Kyung Hwa Cho. Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models. Science of The Total Environment. 2021; 794 ():148592.
Chicago/Turabian StyleSeok Min Hong; Sang-Soo Baek; Daeun Yun; Yong-Hwan Kwon; Hongtao Duan; Jongcheol Pyo; Kyung Hwa Cho. 2021. "Monitoring the vertical distribution of HABs using hyperspectral imagery and deep learning models." Science of The Total Environment 794, no. : 148592.
Ather Abbas; Laurie Boithias; Yakov Pachepsky; Kyunghyun Kim; Jong Ahn Chun; Kyung Hwa Cho. Supplementary material to "AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods". 2021, 1 .
AMA StyleAther Abbas, Laurie Boithias, Yakov Pachepsky, Kyunghyun Kim, Jong Ahn Chun, Kyung Hwa Cho. Supplementary material to "AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods". . 2021; ():1.
Chicago/Turabian StyleAther Abbas; Laurie Boithias; Yakov Pachepsky; Kyunghyun Kim; Jong Ahn Chun; Kyung Hwa Cho. 2021. "Supplementary material to "AI4Water v1.0: An open source python package for modeling hydrological time series using data-driven methods"." , no. : 1.
The remarkable increment in the demand for freshwater in water-resource-stressed regions increases the necessity of saltwater desalination and the application of a brackish water treatment plant (BWTP). In that respect, model-based process analysis can play an essential role in optimizing BWTP operation and maintenance (O&M) and reducing costs. In modeling, it is challenging for either theoretical or numerical methods to sufficiently account for the complex causality and various correlations among the numerous process parameters or variables in the BWTP system. Contrastively, deep learning approaches are capable of modeling such a BWTP system as it can describe the complexity and nonlinearity of its variables with robust autonomous learning. In this study, we modeled an RO unit process of BWTP using conventional long short-term memory (Conv-LSTM) and dual-stage attention-based LSTM (DA-LSTM) based on hourly time-series data obtained from the actual BWTP operation during a one-year period. Hyperparameter optimization for Conv-LSTM and DA-LSTM was individually conducted to enhance the model prediction performance. The model prediction results demonstrated the superiority of DA-LSTM (R2 > 0.99) over Conv-LSTM (0.531 ≤ R2 ≤ 0.884). The sensitivity analysis offered straightforward interpretations of how the attention mechanisms of DA-LSTM used time-series data of the model input and output parameters for prediction.
Nakyung Yoon; Jihye Kim; Jae-Lim Lim; Ather Abbas; Kwanho Jeong; Kyung Hwa Cho. Dual-stage attention-based LSTM for simulating performance of brackish water treatment plant. Desalination 2021, 512, 115107 .
AMA StyleNakyung Yoon, Jihye Kim, Jae-Lim Lim, Ather Abbas, Kwanho Jeong, Kyung Hwa Cho. Dual-stage attention-based LSTM for simulating performance of brackish water treatment plant. Desalination. 2021; 512 ():115107.
Chicago/Turabian StyleNakyung Yoon; Jihye Kim; Jae-Lim Lim; Ather Abbas; Kwanho Jeong; Kyung Hwa Cho. 2021. "Dual-stage attention-based LSTM for simulating performance of brackish water treatment plant." Desalination 512, no. : 115107.
The complexity associated with water quality models (WQMs) has increased owing to the introduction of numerous physical and biological mechanisms in the models. Sensitivity analysis (SA) is conducted to identify influential parameters in these mechanisms. However, enormous computational power and time are required to obtain numerical solutions from thousands of model simulations. Therefore, a cloud-based toolbox is developed for performing SA of WQMs by implementing a cloud computing system using grab sampling data and hyperspectral images (HSI) of waterbodies. Cloud computing can provide high-performance computation by adjusting the scale of the computational power according to user preference. The developed toolbox with the cloud system can reduce the computation time for SA by approximately 20 times compared to that of a desktop computer.
Soobin Kim; Yong Sung Kwon; Jongcheol Pyo; Mayzonee Ligaray; Joong-Hyuk Min; Jung Min Ahn; Sang-Soo Baek; Kyung Hwa Cho. Developing a cloud-based toolbox for sensitivity analysis of a water quality model. Environmental Modelling & Software 2021, 141, 105068 .
AMA StyleSoobin Kim, Yong Sung Kwon, Jongcheol Pyo, Mayzonee Ligaray, Joong-Hyuk Min, Jung Min Ahn, Sang-Soo Baek, Kyung Hwa Cho. Developing a cloud-based toolbox for sensitivity analysis of a water quality model. Environmental Modelling & Software. 2021; 141 ():105068.
Chicago/Turabian StyleSoobin Kim; Yong Sung Kwon; Jongcheol Pyo; Mayzonee Ligaray; Joong-Hyuk Min; Jung Min Ahn; Sang-Soo Baek; Kyung Hwa Cho. 2021. "Developing a cloud-based toolbox for sensitivity analysis of a water quality model." Environmental Modelling & Software 141, no. : 105068.
Ather Abbas; Sangsoo Baek; Norbert Silvera; Bounsamay Soulileuth; Yakov Pachepsky; Olivier Ribolzi; Laurie Boithias; Kyung Hwa Cho. Supplementary material to "In-stream Escherichia Coli Modeling Using high-temporal-resolution data with deep learning and process-based models". 2021, 1 .
AMA StyleAther Abbas, Sangsoo Baek, Norbert Silvera, Bounsamay Soulileuth, Yakov Pachepsky, Olivier Ribolzi, Laurie Boithias, Kyung Hwa Cho. Supplementary material to "In-stream Escherichia Coli Modeling Using high-temporal-resolution data with deep learning and process-based models". . 2021; ():1.
Chicago/Turabian StyleAther Abbas; Sangsoo Baek; Norbert Silvera; Bounsamay Soulileuth; Yakov Pachepsky; Olivier Ribolzi; Laurie Boithias; Kyung Hwa Cho. 2021. "Supplementary material to "In-stream Escherichia Coli Modeling Using high-temporal-resolution data with deep learning and process-based models"." , no. : 1.
Depletion of dissolved oxygen (DO) is a major cause of fish kills in urban streams. Although forecasting short‐term DO concentrations in streams prior to hypoxic events is necessary, such efforts have been rarely made. In this study, 24‐h forecasting models were developed for DO concentrations in three urban streams of South Korea. To forecast the DO concentrations at the outlet sites, which coincide with fish kill hot spot areas, water quality parameters at the lower reaches, and hydrometeorological parameters were used as input variables. The monitoring data were measured hourly between 2017 and 2018 and divided into training and test sets at a ratio of 8:2. Ten‐fold cross‐validation was performed for hyperparameter optimization. Due to the dynamic characteristics of DO concentrations and the discontinuity in time‐series data, a long short‐term memory (LSTM) neural network modeling approach was selected. Overall, a high degree of accuracy was recorded for all study streams. Although hypoxic events were forecast with lower accuracy, the timing and magnitude of abrupt DO depletion were well captured. Water temperature and DO concentrations at the lower reaches and 24‐h cumulative precipitation were important variables for forecasting DO concentrations at all stream outlets. In particular, the importance of cumulative precipitation across all streams indicated that the effects of non‐point sources were critical in depleting DO in urban streams. Monitoring of both lower reaches and outlets in conjunction with a variable importance analysis enhanced interpretability of the LSTM model outputs. This study improves our understanding of precursors of hypoxic events in urban streams.
Young Woo Kim; Taeho Kim; Jihoon Shin; Byeonggeon Go; Mokyoung Lee; Jinhyo Lee; Jayong Koo; Kyung Hwa Cho; Yoonkyung Cha. Forecasting Abrupt Depletion of Dissolved Oxygen in Urban Streams Using Discontinuously Measured Hourly Time‐Series Data. Water Resources Research 2021, 57, 1 .
AMA StyleYoung Woo Kim, Taeho Kim, Jihoon Shin, Byeonggeon Go, Mokyoung Lee, Jinhyo Lee, Jayong Koo, Kyung Hwa Cho, Yoonkyung Cha. Forecasting Abrupt Depletion of Dissolved Oxygen in Urban Streams Using Discontinuously Measured Hourly Time‐Series Data. Water Resources Research. 2021; 57 (4):1.
Chicago/Turabian StyleYoung Woo Kim; Taeho Kim; Jihoon Shin; Byeonggeon Go; Mokyoung Lee; Jinhyo Lee; Jayong Koo; Kyung Hwa Cho; Yoonkyung Cha. 2021. "Forecasting Abrupt Depletion of Dissolved Oxygen in Urban Streams Using Discontinuously Measured Hourly Time‐Series Data." Water Resources Research 57, no. 4: 1.
In recent years, as agricultural activities and types of crops have become diverse, the occurrence of micro-pollutants has been reported more frequently in rural areas. These pollutants have detrimental effects on human health and ecological systems; thus, it is important to manage and monitor their presence in the environment. The modeling approach could be an effective way to understand and manage these pollutants. This study predicts the concentrations of micro-pollutants (MPs) using deep learning (DL) models, and the results are then compared with simulation results obtained from the soil water assessment tool (SWAT) model. The SWAT model showed an unacceptable performance owing to the resulting negative Nash–Sutcliffe efficiency (NSE) values for the simulations. This may be caused by the limitations of SWAT, which pertains to adopting simplified equations to simulate micro-pollutants. In addition, the ambiguous plan of pesticide application increased the model uncertainty, thereby deteriorating the model result. Here, we developed two different DL models: long short-term memory (LSTM) and convolutional neural network (CNN). LSTM exhibited the highest model performance, with NSE values of 0.99 and 0.75 for the training and validation steps, respectively. In the multi-target MP model, the error decreased as the number of simulated pollutants increased. The simulation of the four pollutants had the highest error, while the six-target simulation had the lowest error. In conclusion, this study demonstrated that the LSTM model has the potential to improve the prediction of MPs in aquatic systems.
Daeun Yun; Ather Abbas; Junho Jeon; Mayzonee Ligaray; Sang-Soo Baek; Kyung Hwa Cho. Developing a deep learning model for the simulation of micro-pollutants in a watershed. Journal of Cleaner Production 2021, 300, 126858 .
AMA StyleDaeun Yun, Ather Abbas, Junho Jeon, Mayzonee Ligaray, Sang-Soo Baek, Kyung Hwa Cho. Developing a deep learning model for the simulation of micro-pollutants in a watershed. Journal of Cleaner Production. 2021; 300 ():126858.
Chicago/Turabian StyleDaeun Yun; Ather Abbas; Junho Jeon; Mayzonee Ligaray; Sang-Soo Baek; Kyung Hwa Cho. 2021. "Developing a deep learning model for the simulation of micro-pollutants in a watershed." Journal of Cleaner Production 300, no. : 126858.
Controlling membrane fouling in a membrane filtration system is critical to ensure high filtration performance. A forecast of membrane fouling could enable preliminary actions to relieve the development of membrane fouling. Therefore, we established a long short-term memory (LSTM) model to investigate the variations in filtration performance and fouling growth. For data acquisition, we first conducted lab-scale membrane fouling experiments to identify the diverse fouling mechanisms of natural organic matter (NOM) in nanofiltration (NF) systems. Four types of NOMs were considered as model foulants: humic acid, bovine-serum-albumin, sodium alginate, and tannic acid. In addition, real-time 2D images were acquired via optical coherence tomography (OCT) to quantify the cake layer formed on the membrane. Subsequently, experimental data were used to train the LSTM model to predict permeate flux and fouling layer thickness as output variables. The model performance exhibited root mean square errors of <1 L/m2/h for permeate flux and <10 µm for fouling layer thickness in both the training and validation steps. In this study, we demonstrated that deep learning can be used to simulate the influence of NOMs on the NF system and also be applied to simulate other membrane processes.
Jaegyu Shim; Sanghun Park; Kyung Hwa Cho. Deep learning model for simulating influence of natural organic matter in nanofiltration. Water Research 2021, 197, 117070 .
AMA StyleJaegyu Shim, Sanghun Park, Kyung Hwa Cho. Deep learning model for simulating influence of natural organic matter in nanofiltration. Water Research. 2021; 197 ():117070.
Chicago/Turabian StyleJaegyu Shim; Sanghun Park; Kyung Hwa Cho. 2021. "Deep learning model for simulating influence of natural organic matter in nanofiltration." Water Research 197, no. : 117070.
Alexandrium catenella (A. catenella) is a notorious algal species known to cause paralytic shellfish poisoning (PSP) in Korean coastal waters. There have been numerous studies on its temporal and spatial blooms in Korea. However, its bloom dynamics have not been fully understood because of the complexity in physical, chemical, and biological environments. This study aims to identify the factors that influence A. catenella blooms by applying a numerical model and machine learning. Intensive monitoring of A. catenella was conducted to investigate temporal variations in its population and its spatial distribution in the area with frequent occurrences of PSP bloom initiation. Moreover, a numerical model was built to analyze the ocean physical factors related to the bloom of A. catenella. Based on the information obtained from the monitored and simulated results, the decision tree (DT) method was applied to identify factors that caused the bloom. The outbreak of A. catenella was observed in the eastern coastal water of Geoje Island in 2017, recording a peak density of 4 × 104 (cell L−1). Retention time and particle scattering demonstrated that the physical force in 2017 was weaker than that in 2018, as shown by the smaller effects of advection and dispersion in 2017. The decision tree model showed that (1) water temperature below 17.21 °C was ideal for the growth of A. catenella, (2) phosphate influenced the growth of the species, and (3) cell density was accelerated with increasing retention time. The results from DT can contribute to the prediction of A. catenella blooms by determining the conditions that cause bloom initiation. Further, they can be used as a practical approach for mitigating HABs. Thus, machine learning and numerical simulation in this study can be a potential approach for effectively managing the bloom of A. catenella.
Sang-Soo Baek; Yong Sung Kwon; Jongcheol Pyo; Jungmin Choi; Young Ok Kim; Kyung Hwa Cho. Identification of influencing factors of A. catenella bloom using machine learning and numerical simulation. Harmful Algae 2021, 103, 102007 .
AMA StyleSang-Soo Baek, Yong Sung Kwon, Jongcheol Pyo, Jungmin Choi, Young Ok Kim, Kyung Hwa Cho. Identification of influencing factors of A. catenella bloom using machine learning and numerical simulation. Harmful Algae. 2021; 103 ():102007.
Chicago/Turabian StyleSang-Soo Baek; Yong Sung Kwon; Jongcheol Pyo; Jungmin Choi; Young Ok Kim; Kyung Hwa Cho. 2021. "Identification of influencing factors of A. catenella bloom using machine learning and numerical simulation." Harmful Algae 103, no. : 102007.
Antibiotic resistance genes (ARGs) have been reported to threaten the public health of beachgoers worldwide. Although ARG monitoring and beach guidelines are necessary, substantial efforts are required for ARG sampling and analysis. Accordingly, in this study, we predicted ARGs occurrence that are primarily found on the coast after rainfall using a conventional long short-term memory (LSTM), LSTM-convolutional neural network (CNN) hybrid model, and input attention (IA)-LSTM. To develop the models, 10 categories of environmental data collected at 30-min intervals and concentration data of 4 types of major ARGs (i.e., aac(6′-Ib-cr), blaTEM, sul1, and tetX) obtained at the Gwangalli Beach in South Korea, between 2018 and 2019 were used. When individually predicting ARGs occurrence, the conventional LSTM and IA-LSTM exhibited poor R2 values during training and testing. In contrast, the LSTM-CNN exhibited a 2–6-times improvement in accuracy over those of the conventional LSTM and IA-LSTM. However, when predicting all ARGs occurrence simultaneously, the IA-LSTM model exhibited a superior performance overall compared to that of LSTM-CNN. Additionally, the influence of environmental variables on prediction was investigated using the IA-LSTM model, and the ranges of input variables that affect each ARG were identified. Consequently, this study demonstrated the possibility of predicting the occurrence and distribution of major ARGs at the beach based on various environmental variables, and the results are expected to contribute to management of ARG occurrence at a recreational beach.
Jiyi Jang; Ather Abbas; Minjeong Kim; Jingyeong Shin; Young Mo Kim; Kyung Hwa Cho. Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models. Water Research 2021, 196, 117001 .
AMA StyleJiyi Jang, Ather Abbas, Minjeong Kim, Jingyeong Shin, Young Mo Kim, Kyung Hwa Cho. Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models. Water Research. 2021; 196 ():117001.
Chicago/Turabian StyleJiyi Jang; Ather Abbas; Minjeong Kim; Jingyeong Shin; Young Mo Kim; Kyung Hwa Cho. 2021. "Prediction of antibiotic-resistance genes occurrence at a recreational beach with deep learning models." Water Research 196, no. : 117001.
The seawater battery (SWB) is a promising desalination technology that utilizes abundant sodium ions as an energy storage medium. Recently, the alternative desalination system, seawater battery desalination (SWB-D), was developed by placing an SWB next to the desalination compartment. This SWB-D system can desalt water while charging the SWB next to it. However, only a fixed catholyte solution has been investigated, although the catholytes impact the overall SWB-D performance. Therefore, we evaluated the effect of different catholytes on the desalination performance. High-saline reverse osmosis (RO) concentrate or brackish water exhibited excellent salt removal capability (>85.3% of sodium and >76.6% of chloride ions) with relatively short operation times (36.4 h for RO concentrate and 39.5 h for brackish water) upon charging, whereas the relatively low-saline river water showed the longest operation time (81.0 h), implying that river water should be excluded as a potential catholyte. The amount of desalinated water was marginally reduced due to osmosis through the anion exchange membrane; however, the amount of treated salt was >82.9% even after the reduction in water volume. These findings suggest that the catholyte with a resistance of >0.041 kΩ·cm can be ideal for the SWB-D.
Sanghun Park; Mayzonee Ligaray; YoungSik Kim; Kangmin Chon; Moon Son; Kyung Hwa Cho. Investigating the influence of catholyte salinity on seawater battery desalination. Desalination 2021, 506, 115018 .
AMA StyleSanghun Park, Mayzonee Ligaray, YoungSik Kim, Kangmin Chon, Moon Son, Kyung Hwa Cho. Investigating the influence of catholyte salinity on seawater battery desalination. Desalination. 2021; 506 ():115018.
Chicago/Turabian StyleSanghun Park; Mayzonee Ligaray; YoungSik Kim; Kangmin Chon; Moon Son; Kyung Hwa Cho. 2021. "Investigating the influence of catholyte salinity on seawater battery desalination." Desalination 506, no. : 115018.
In this study, the adsorption of herbicides using ground coffee residue biochars without (GCRB) and with NaOH activation (GCRB-N) was compared to provide deeper insights into their adsorption behaviors and mechanisms. The physicochemical characteristics of GCRB and GCRB-N were analyzed using Brunauer–Emmett–Teller surface area, Fourier transform infrared spectroscopy, scanning electron microscopy, and X-ray diffraction and the effects of pH, temperature, ionic strength, and humic acids on the adsorption of herbicides were identified. Moreover, the adsorption kinetics and isotherms were studied. The specific surface area and total pore volume of GCRB-N (405.33 m2/g and 0.293 cm3/g) were greater than those of GCRB (3.83 m2/g and 0.014 cm3/g). The GCBR-N could more effectively remove the herbicides (Qe,exp of Alachlor = 122.71 μmol/g, Qe,exp of Diuron = 166.42 μmol/g, and Qe,exp of Simazine = 99.16 μmol/g) than GCRB (Qe,exp of Alachlor = 11.74 μmol/g, Qe,exp of Diuron = 9.95 μmol/g, and Qe,exp of Simazine = 6.53 μmol/g). These results suggested that chemical activation with NaOH might be a promising option to make the GCRB more practical and effective for removing herbicides in the aqueous solutions.
Yong-Gu Lee; JaeGwan Shin; Jinwoo Kwak; Sangwon Kim; Changgil Son; Kyung Cho; Kangmin Chon. Effects of NaOH Activation on Adsorptive Removal of Herbicides by Biochars Prepared from Ground Coffee Residues. Energies 2021, 14, 1297 .
AMA StyleYong-Gu Lee, JaeGwan Shin, Jinwoo Kwak, Sangwon Kim, Changgil Son, Kyung Cho, Kangmin Chon. Effects of NaOH Activation on Adsorptive Removal of Herbicides by Biochars Prepared from Ground Coffee Residues. Energies. 2021; 14 (5):1297.
Chicago/Turabian StyleYong-Gu Lee; JaeGwan Shin; Jinwoo Kwak; Sangwon Kim; Changgil Son; Kyung Cho; Kangmin Chon. 2021. "Effects of NaOH Activation on Adsorptive Removal of Herbicides by Biochars Prepared from Ground Coffee Residues." Energies 14, no. 5: 1297.
The performance of the membrane capacitive deionization (MCDI) system was evaluated during the removal of three selected pharmaceuticals, neutral acetaminophen (APAP), cationic atenolol (ATN), and anionic sulfamethoxazole (SMX), in batch experiments (feed solution: 2 mM NaCl and 0.01 mM of each pharmaceutical). Upon charging, the cationic ATN showed the highest removal rate of 97.65 ± 1.71%, followed by anionic SMX (93.22 ± 1.66%) and neutral APAP (68.08 ± 5.24%) due to the difference in electrostatic charge and hydrophobicity. The performance parameters (salt adsorption capacity, specific capacity, and cycling efficiency) and energy factors (specific energy consumption and recoverable energy) were further evaluated over ten consecutive cycles depending on the pharmaceutical addition. A significant decrease in the specific adsorption capacity (from 24.6 to ∼3 mg−NaCl g−1) and specific capacity (from 17.6 to ∼2.5 mAh g−1) were observed mainly due to the shortened charging and discharging time by pharmaceutical adsorption onto the electrode. This shortened charging time also led to an immediate drop in specific energy consumption from 0.41 to 0.04 Wh L−1. Collectively, these findings suggest that MCDI can efficiently remove pharmaceuticals at a low energy demand; however, its performance changes dramatically as the pharmaceuticals are present in the target water.
Moon Son; Kwanho Jeong; Nakyung Yoon; Jaegyu Shim; Sanghun Park; Jongkwan Park; Kyung Hwa Cho. Pharmaceutical removal at low energy consumption using membrane capacitive deionization. Chemosphere 2021, 276, 130133 .
AMA StyleMoon Son, Kwanho Jeong, Nakyung Yoon, Jaegyu Shim, Sanghun Park, Jongkwan Park, Kyung Hwa Cho. Pharmaceutical removal at low energy consumption using membrane capacitive deionization. Chemosphere. 2021; 276 ():130133.
Chicago/Turabian StyleMoon Son; Kwanho Jeong; Nakyung Yoon; Jaegyu Shim; Sanghun Park; Jongkwan Park; Kyung Hwa Cho. 2021. "Pharmaceutical removal at low energy consumption using membrane capacitive deionization." Chemosphere 276, no. : 130133.
Cleaning-in-place (CIP) is a representative fouling management process from which the filtration performances of fouled membranes can be recovered. However, CIP can cause significant inefficiency in water production because frequent system restabilization is necessary for cleaning processes. This study applied a newly developed on-line cleaning agent (OCA, a feed water additive for fouling mitigation), to reduce the number of CIP by enhancing water productivity. Reverse osmosis filtration was performed to evaluate the effect of on-line cleaning on the mitigation of organic fouling originating from humic acid (HA) and bovine serum albumin. OCA increased the permeate flux in proportion to OCA concentration. In particular, OCA effectively reduced the fouling layer thickness by 22% when fouling was influenced by HA–Ca2+ complexation, increasing water production by 5%. It also had a minor influence on bovine serum albumin fouling, producing a 1.4% increase in permeate flux. Furthermore, the pore blockage-cake filtration model was used to evaluate OCA cleaning performance through the reduction in fouling layer resistance and the growth parameter. The results demonstrated the advantages of OCA utilization for mitigating cake layer development. These findings imply that OCA can be an effective cleaning additive, especially in seawater and groundwater treatment processes with a high proportion of HA and calcium ions.
Sanghun Park; Seok Min Hong; Jongkwan Park; Sunam You; Younggeun Lee; Eunggil Kim; Kyung Hwa Cho. Evaluating an on-line cleaning agent for mitigating organic fouling in a reverse osmosis membrane. Chemosphere 2021, 275, 130033 .
AMA StyleSanghun Park, Seok Min Hong, Jongkwan Park, Sunam You, Younggeun Lee, Eunggil Kim, Kyung Hwa Cho. Evaluating an on-line cleaning agent for mitigating organic fouling in a reverse osmosis membrane. Chemosphere. 2021; 275 ():130033.
Chicago/Turabian StyleSanghun Park; Seok Min Hong; Jongkwan Park; Sunam You; Younggeun Lee; Eunggil Kim; Kyung Hwa Cho. 2021. "Evaluating an on-line cleaning agent for mitigating organic fouling in a reverse osmosis membrane." Chemosphere 275, no. : 130033.
Remote sensing can detect and map algal blooms. The HydroLight (Sequoia Scientific Inc., Bellevue, Washington, DC, USA) model generates the reflectance profiles of various water bodies. However, the influence of model parameters has rarely been investigated for inland water. Moreover, the simulation time of the HydroLight model increases as the amount of input data increases, which limits the practicality of the HydroLight model. This study developed a graphical user interface (GUI) software for the sensitivity analysis of the HydroLight model through multiple executions. The GUI software stably performed parameter sensitivity analysis and substantially reduced the simulation time by up to 92%. The GUI software results for lake water show that the backscattering ratio was the most important parameter for estimating vertical reflectance profiles. Based on the sensitivity analysis results, parameter calibration of the HydroLight model was performed. The reflectance profiles obtained using the optimized parameters agreed with observed profiles, with R2 values of over 0.98. Thus, a strong relationship between the backscattering coefficient and the observed cyanobacteria genera cells was identified.
Jong Cheol Pyo; Yong Sung Kwon; Jae-Hyun Ahn; Sang-Soo Baek; Yong-Hwan Kwon; Kyung Hwa Cho. Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water. Remote Sensing 2021, 13, 709 .
AMA StyleJong Cheol Pyo, Yong Sung Kwon, Jae-Hyun Ahn, Sang-Soo Baek, Yong-Hwan Kwon, Kyung Hwa Cho. Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water. Remote Sensing. 2021; 13 (4):709.
Chicago/Turabian StyleJong Cheol Pyo; Yong Sung Kwon; Jae-Hyun Ahn; Sang-Soo Baek; Yong-Hwan Kwon; Kyung Hwa Cho. 2021. "Sensitivity Analysis and Optimization of a Radiative Transfer Numerical Model for Turbid Lake Water." Remote Sensing 13, no. 4: 709.
A marine outfall can be a wastewater management system that discharges sewage and stormwater into the sea; hence, it is a source of microbial pollution on recreational beaches, including antibiotic resistant genes (ARGs), which lead to an increase in untreatable diseases. In this regard, a marine outfall must be efficiently located to mitigate these risks. This study aimed to 1) investigate the spatiotemporal variability of Escherichia coli (E. coli) and ARGs on a recreational beach and 2) design marine outfalls to reduce microbial risks. For this purpose, E. coli and ARGs with influential environmental variables were intensively monitored on Gwangalli beach, South Korea in this study. Environmental fluid dynamic code (EFDC) was used and calibrated using the monitoring data, and 12 outfall extension scenarios were explored (6 locations at 2 depths). The results revealed that repositioning the marine outfall can significantly reduce the concentrations of E. coli and ARGs on the beach by 46–99%. Offshore extended outfalls at the bottom of the sea reduced concentrations of E. coli and ARGs on the beach more effectively than onshore outfalls at the sea surface. These findings could be helpful in establishing microbial pollution management plans at recreational beaches in the future.
Minjeong Kim; Mayzonee Ligaray; Yong Sung Kwon; Soobin Kim; Sangsoo Baek; Jongcheol Pyo; Gahyun Baek; Jingyeong Shin; Jaai Kim; Changsoo Lee; Young Mo Kim; Kyung Hwa Cho. Designing a marine outfall to reduce microbial risk on a recreational beach: Field experiment and modeling. Journal of Hazardous Materials 2020, 409, 124587 .
AMA StyleMinjeong Kim, Mayzonee Ligaray, Yong Sung Kwon, Soobin Kim, Sangsoo Baek, Jongcheol Pyo, Gahyun Baek, Jingyeong Shin, Jaai Kim, Changsoo Lee, Young Mo Kim, Kyung Hwa Cho. Designing a marine outfall to reduce microbial risk on a recreational beach: Field experiment and modeling. Journal of Hazardous Materials. 2020; 409 ():124587.
Chicago/Turabian StyleMinjeong Kim; Mayzonee Ligaray; Yong Sung Kwon; Soobin Kim; Sangsoo Baek; Jongcheol Pyo; Gahyun Baek; Jingyeong Shin; Jaai Kim; Changsoo Lee; Young Mo Kim; Kyung Hwa Cho. 2020. "Designing a marine outfall to reduce microbial risk on a recreational beach: Field experiment and modeling." Journal of Hazardous Materials 409, no. : 124587.
Jaegyu Shim; Nakyung Yoon; Sanghun Park; Jongkwan Park; Moon Son; Kwanho Jeong; Kyung Hwa Cho. Corrigendum to “Influence of natural organic matter on membrane capacitive deionization performance” [Chemosphere 264 (2021) 128519]. Chemosphere 2020, 262, 128680 .
AMA StyleJaegyu Shim, Nakyung Yoon, Sanghun Park, Jongkwan Park, Moon Son, Kwanho Jeong, Kyung Hwa Cho. Corrigendum to “Influence of natural organic matter on membrane capacitive deionization performance” [Chemosphere 264 (2021) 128519]. Chemosphere. 2020; 262 ():128680.
Chicago/Turabian StyleJaegyu Shim; Nakyung Yoon; Sanghun Park; Jongkwan Park; Moon Son; Kwanho Jeong; Kyung Hwa Cho. 2020. "Corrigendum to “Influence of natural organic matter on membrane capacitive deionization performance” [Chemosphere 264 (2021) 128519]." Chemosphere 262, no. : 128680.