Fouling is an inevitable problem in membrane water treatment plant. It can be measured by trans-membrane pressure (TMP) in the constant flux operation, and chemical cleaning is carried out when TMP reaches a critical value. An early fouilng alarm is defined as warning the critical TMP value appearance in advance. The alarming method was developed using one of machine learning algorithms, decision tree, and applied to a ceramic microfiltration (MF) pilot plant. First, the decision tree model that classifies the normal/abnormal state of the filtration cycle of the ceramic MF pilot plant was developed and it was then used to make the early fouling alarm method. The accuracy of the classification model was up to 96.2% and the time for the early warning was when abnormal cycles occurred three times in a row. The early fouling alram can expect reaching a limit TMP in advance (e.g., 15-174 hours). By adopting TMP increasing rate and backwash efficiency as machine learning variables, the model accuracy and the reliability of the early fouling alarm method were increased, respectively.

The production process of ultra-pure water (UPW) involves dozens of unit processes such as reverse osmosis (RO), pretreatments, membrane degasifier, and several ion exchange processes. Recently, continuous electrodeionization (CEDI) has replaced the 2-bed and 3-tower (2B3T) ion exchange process. As a result, the majority of wastewater in UPW production now comes from the RO concentrate. The important of RO in UPW production is to produce high-quality water with a low ion concentration (around 1 mg/L) for CEDI feed water. Minimizing RO concentrate is essential to reduce the wastewater produced in the UPW production process. This can be achieved by maximizing the recovery of the RO system. However, increasing the recovery is limited by the water quality of the RO permeate. To ensure high-quality permeate water, the RO system is designed with a two-pass configuration. The recovery of each pass in the RO system is limited (e.g., < 85%) due to the expected increase in permeate water concentration at higher RO feed water concentrations. Interestingly, tests using 4-inch RO modules with low concentration feed water (≤ 35 mg/L as NaCl) revealed that the permeate concentration remains almost constant regardless of the feed water concentration. This implies that the recovery of the first RO pass can be increased as long as the average feed/concentrate concentration of the second RO pass is less than 35 mg/L. According to this design criterion for the RO system, the recovery of the first and second RO pass, with a feed water concentration of 250 mg/L as NaCl, can be increased up to 94.8% and 96.0%, respectively. Compared to the conventional RO system design (e.g., 70% and 80% for the first and second RO pass) for UPW production, this maximum recovery design reduces the volume of RO feed and concentrate by up to 38.4% and 89.2%, respectively.

Forward osmosis (FO) process is a chemical potential driven process, where highly concentrated draw solution (DS) is used to take water through semi-permeable membrane from feed solution (FS) with lower concentration. Recently, commercial FO membrane modules have been developed so that full-scale FO process can be applied to seawater desalination or water reuse. In order to design a real-scale FO plant, the performance prediction of FO membrane modules installed in the plant is essential. Especially, the flux prediction is the most important task because the amount of diluted draw solution and concentrate solution flowing out of FO modules can be expected from the flux. Through a previous study, a theoretical based FO module model to predict flux was developed. However it needs an intensive numerical calculation work and a fitting process to reflect a complex module geometry. The idea of this work is to introduce deep learning to predict flux of FO membrane modules using 116 experimental data set, which include six input variables (flow rate, pressure, and ion concentration of DS and FS) and one output variable (flux). The procedure of optimizing a deep learning model to minimize prediction error and overfitting problem was developed and tested. The optimized deep learning model (error of 3.87%) was found to predict flux better than the theoretical based FO module model (error of 10.13%) in the data set which were not used in machine learning.

High-pressure membrane system like nanofiltration(NF) and reverse osmosis(RO) was investigated as a part of water treatment processes to produce high quality potable water with low organic matter concentration through membrane module tests and design simulation. River water and sand filtration permeate in Busan D water treatment plant were selected as feed water, and NE4040-90 and RE4040-Fen(Toray Chemical Korea) were used as NF and RO membranes, respectively. Total organic carbon(TOC) concentrations of NF and RO permeates were mostly below 0.5 mg/l and the average TOC removal rates of NF and RO membranes were 93.99% and 94.28%, respectively, which means NF used in this study is competitive with RO in terms of organic matter removal ability. Different from ions rejection tendency, the TOC removal rate increases at higher recovery rates, which is because the portion of higher molecular weight materials in the concentrated raw water with increasing recovery rate increases. Discharge of NF/RO concentrates to rivers may not be acceptable because the increased TDS concentration of the concentrates can harm the river eco-system. Thus, the idea of using NF/RO concentrate as the raw water for industrial water production was introduced. The design simulation results with feed water and membranes used in this work reveal that the raw water guideline can be satisfied if the recovery rate of NF/RO system is designed below 80%.

Gas hydrate (GH)-based desalination process have a potential as a novel unit desalination process. GHs are nonstoichiometric crystalline inclusion compounds formed at low temperature and a high pressure condition by water and a number of guest gas molecules. After formation, pure GHs are separated from the remaining concentrated seawater and they are dissociated into guest gas and pure water in a low temperature and a high pressure condition. The condition of GH formation is different depending on the type of guest gas. This is the reason why the guest gas is a key to success of GH desalination process. The salt rejection of GH based desalination process appeared 60.5-93%, post treatment process is needed to finally meet the product water quality. This study adopted reverse osmosis (RO) as a post treatment. However, the test about gas rejection by RO process have to be performed because the guest gas will be dissolved in a GH product (RO feed). In this research, removal potential of dissolved gas by RO process is performed using lab-scale RO system and GC/MS analysis. The relation between RO membrane characteristics and gas removal rate were analyzed based on the GC/MS measurement.

Gas hydrate desalination process is based on a liquid to solid (Gas Hydrate, GH) phase change followed by a physical process to separate the GH from the remaining salty water. The GH based desalination process show 60.5-90% of salt rejection, post treatment like reverse osmosis (RO) process is needed to finally meet the product water quality. In this study, the energy consumption of the GH and RO hybrid system was investigated. The energy consumption of the GH process is based on the cooling and heating of seawater and the heat of GH formation reaction while RO energy consumption is calculated using the product of pressure and flow rate of high pressure pumps used in the process. The relation between minimum energy consumption of RO process and RO recovery depending on GH salt rejection, and (2) energy consumption of electric based GH process can be calculated from the simulation. As a result, energy consumption of GH-RO hybrid system and conventional seawater RO process (with/without enregy recovery device) is compared. Since the energy consumption of GH process is too high, other solution used seawater heat and heat exchanger instead of electric energy is suggested.

Desalination is a key technology to overcome water shortage problem in a near future. High energy consumption is an Achilles’ heel in desalination technology. Osmotically driven membrane processes like forward osmosis(FO) was introduced to address this energy issue. Characterizing membrane properties such as water permeability(A), salt permeability(B), and the resistance to salt diffusion within the support layer(KICP) are very important to predict the performance of scaled-up FO processes. Currently, most of researches reported that the water permeability of FO membrane was measured by reverse osmosis(RO) type test. Permeating direction of RO and FO are different and RO test needs hydraulic pressure so that several problems can be occurred(i.e. membrane deformation, compaction and effect of concentration polarization). This study focuses on measuring water permeability of FO membrane by FO type test results in various experimental conditions. A statistical approach was developed to evaluate the three FO membrane properties(A, B, and KICP) and it predicted test result by the internal and external concentration polarization model.

Temperature correction trans-membrane pressure (TC-TMP) is frequently used as a fouling index in membrane water treatment plants. TC-TMP equation is derived based on an assumption that the total membrane resistance (i.e. the sum of the intrinsic membrane resistance and fouling resistance) is not affected by temperature. This work verified the validity of this assumption using microfiltration (MF) and ultrafiltration (UF) membranes with and without fouling. The foulants used in the work were kaolin (inorganic) and humic acid (organic). The intrinsic resistances of MF and UF membranes remains at constant values regardless of temperature change. When the same amount of foulants were accumulated on the membrane, inorganic fouling resistance with kaolin was constant regardless of temperature change while organic fouling resistance with humic acid decreased at higher temperatures, which means that TC-TMP cannot be used as a fouling index when organic fouling occurs in a real field application. Since TC-TMP underestimates the amount of fouling at higher temperatures, more attention should be necessary in the operation of membrane water treatment plant in a hotter season like summer.

Smart water grid is a water network with communication to save water and energy using various water resources. In smart water grid, water product from the various sources can be blended to be supplied to end-users. The product water blending was reported by literatures while feed water blending has been rarely reported so far. In this work, a commercial reverse osmosis (RO) system design software provided by a membrane manufacturer was used to elucidate the effect of feed water blending on the performance of seawater reverse osmosis (SWRO) plant. Fresh water from exisiting water resource was assumed to be blended to seawater to decrease salt concentration of the RO feed water. The feed water blending can simplify the RO system from double to single pass and decrease seawater intake amount, the unit prices of the RO system components including high pressure pump, and operation risk. Due to the increase in RO plant capacity with the feed water blending, however, the RO membrane area and total power consumption increase at higher water blending rates. Therefore, a specific benefit-cost analysis should be carried out to apply the feed water blending to SWRO plants.

Ultrapure water (UPW) is water containing nothing but water molecule (H2O). The use of UPW is increasing in many industries such as the thermal and nuclear power plants, petrochemical plants, and semiconductor manufacturers. In order to produce UPW, several unit processes such as ion exchange, reverse osmosis (RO), ultraviolet (UV) oxidation should be efficiently arranged. In particular, RO process should remove not only ions but also low molecular weight (LMW) organic matters in UPW production system. But, the LMW organic matter removal data of RO membranes provided by manufacturers does not seem to be reasonable because they tested the removal in high concentration conditions like 1,000 ppm of isopropyl alcohol (IPA, MW=60.1). In this study, bench-scale experiments were carried out using 4-inches RO modules. IPA was used as a model LMW organic matter with low concentration conditions less than 1 ppm as total organic carbon (TOC). As a result, the IPA removal data by manufacturers turned out to be trustable because the effect of feed concentration on the IPA removal was negligble while the IPA removal efficiency became higher at higher permeate flux.

In most tissues, apoptosis plays a pivotal role in normal development and in regulation of cell number. Therefore inappropriate apoptosis is revealed in a variety of diseases. This study was carried out to investigate the effects of acupuncture and needle electrode electrical stimulation on the change of caspase-3, 9 and neuronal nitric oxide synthase (nNOS) immunoreactive cells in the sprague dawley rats (SD rat). In immobilized SD rats (n=5), enhanced caspase-3 and caspase-9 expression were detected in the reticular part of substantia nigra, and enhanced nNOS was detected in the dorsolateral periaqueductal gray (DL-PAG) of midbrain and the paraventricular nucleus (PVN) of the hypothalamus using immunohistochemistry. Following the immobilization, acupuncture (n=5) and needle electrode electrical stimulation (n=5, 2 Hz) was applied at Hg (LI4) acupoint of SD rats, respectively. The stress-induced enhancement in the expression of caspase-3, 9 and nNOS were The present results demonstrate that and needle electrode electrical stimulation are effective in the modulation of expression of caspase-3, 9 and nNOS induced by immobilization.