Application Of Data Mining For Reverse Osmosis Process In Seawater Desalination

Reverse osmosis (RO) membrane process has been considered a promising technology for water treatment and desalination. However, it is difficult to predict the performance of pilot- or full-scale RO systems because numerous factors are involved in RO performance, including variations in feed water (quantity, quality, temperature, etc), membrane fouling, and time-dependent changes (deteriorations). Accordingly, this study intended to develop a practical approach for the analysis of operation data in pilot-scale reverse osmosis (RO) processes. Novel techniques such as artificial neural network (ANN) and genetic programming (GP) technique were applied to correlate key operating parameters and RO permeability statistically. The ANN and GP models were trained using a set of experimental data from a RO pilot plant with a capacity of 1,000 m3/day and then used to predict its performance. The comparison of the ANN and GP model calculations with the experiment results revealed that the models were useful for analyzing and classifying the performance of pilot-scale RO systems. The models were also applied for an in-depth analysis of RO system performance under dynamic conditions

Comparative evaluation of osmotically-driven cleaning methods for organic-inorganic fouling in pressure retarded osmosis (PRO)

Pressure retarded osmosis (PRO) is a promising technique of desalination techniques. However, one of the major problems is permeate flux decline by fouling. This study investigates the mitigation of organic and inorganic fouling in PRO by osmotically driven cleaning methods. This study comparatively evaluates the effective cleaning methods: (i) Osmotic Backwashing (OB), (ii) Reverse Osmosis Flushing (ROF), and (iii) Pressure Assisted Osmotic Backwashing (PAOB). The results showed that PAOB was a more effective method than the others in terms of cleaning efficiency and permeability. CaCO3 solution of 1000 mg/L and humic acid 100 mg/L were used as a representative inorganic and organic foulant, respectively. After cleaning, cleaning efficiency and flux decline rate were compared. The PAOB method showed the higher performance compared to other cleaning methods, had 92% recovery rate and lower flux decline after cleaning. Also, by using instrumental analysis-scanning electron microscope (SEM), it was proven to find out the proper cleaning method in PRO. Keywords: Membrane fouling, Cleaning method, PAO

Maritime Network Analysis Based on Geographic Information System for Water Supply Using Shipboard Seawater Desalination System

Small islands are supplied with water from underground sources, simple seawater desalination facilities, or water supply shipment. However, this water supply can be interrupted because of the sudden depletion of groundwater, as groundwater level prediction is inaccurate. Additionally, seawater desalination facilities are difficult to maintain, resulting in frequent breakdowns. When the water tank capacity is below a certain level, island residents contact the water supply shipment manager to request a shipment from land. In Korea, a seawater desalination plant project using ships was newly attempted to solve the water supply problem for island regions. Through this project, an attempt was made to supply water to many island areas suffering water supply disruptions due to drought. The purpose of this study is to compare water supply routes to multiple island regions using existing water supply shipment with desalination plants on ships through network analysis based on a geographic information system. To optimize sailing route, length (m), road connection type, and name of each road section, actual operation data, distance, etc., were set up on a network dataset and analyzed. In addition, the operational model predicted the stability of water supply using the GoldSim simulator. As a result, when sailing on the optimal route based on network analysis, the existing water supply routes could be reduced (2153 km -> 968 km) by more than 55%, and operational costs can be verified to be reduced. Additionally, the validity of the network analysis results was confirmed through actual travel of the representative route