• Title/Summary/Keyword: Membrane Module

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Simulation of Pervaporation Process Through Hollow Fiber Module for Treatment of Reactive Waste Stream from a Phenolic Resin Manufacturing Process (페놀수지 생산공정에서 배출되는 반응성 폐수처리를 위한 중공사막 모듈 투과증발 공정모사)

  • C. K Yeom;F. U. Baig
    • Membrane Journal
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    • v.13 no.4
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    • pp.257-267
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    • 2003

  • For the treatment of reactive phenolic resin waste, a simulation model of pervaporative dehydration process has been developed through hollow fiber membrane module. Some of basic parameters were determined directly from dehydration of the waste liquid through a flat sheet membrane to get realistic values. The simulation model was verified by comparing the simulated values with experimental data obtained from hollow fiber membrane module. Hollow fiber membranes with active layer coated on inside fiber were used, and feed flew through inside hollow fiber. Feed flow rate affected membrane performances and reaction by providing a corresponding temperature distribution of feed along with fiber length. Feed temperature is also a crucial factor to determine dehydration and reaction behavior by two competing ways; increasing temperature increases permeation rate as well as water formation rate. Once the permeate pressure is well below the saturated vapor pressure of feed, permeate pressure had a slightly negative effect on permeation performance by slightly reducing driving force. As the pressure approached the vapor pressure of feed, dehydration performances declined considerably due to the activity ratio of feed and permeate.

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Restoration of Membrane Performance for Damaged Reverse Osmosis Membranes through in-situ Healing (손상된 역삼투막의 in-situ 힐링을 통한 막 성능 복원)

  • Yun, Won Seob;Rhim, Ji Won;Cho, Young Ju
    • Membrane Journal
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    • v.29 no.2
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    • pp.96-104
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    • 2019

  • The purpose of this paper is whether or not the in-situ restoration of the reverse osmosis (RO) membranes which its membrane function is lost is possible. The damaged RO membranes are double coated through the salting-out method by the poly(styrene sulfonic acid) sodium salt as the cationic exchange polymer and the polyethyleneimine as the anionic exchange polymer and also conducted the opposite order of the coating materials. And according to the concentration, time and ionic strength, the flux and rejection are measured for the coated membranes. Then the best coating condition is to apply for the RO membrane module of the household water purifier to know the possibility of the in-situ restoration for the commercial module. When the condition of the PEI 30,000 ppm (IS = 0.1)/PSSA 20,000 ppm (IS = 0.7) is applied, the rejection was enhance from 69% for the damaged module to 86% (90% for the pristine module).

  • https://doi.org/10.14579/MEMBRANE_JOURNAL.2019.29.2.96 인용 PDF KSCI

Predicting flux of forward osmosis membrane module using deep learning (딥러닝을 이용한 정삼투 막모듈의 플럭스 예측)

  • Kim, Jaeyoon;Jeon, Jongmin;Kim, Noori;Kim, Suhan
    • Journal of Korean Society of Water and Wastewater
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    • v.35 no.1
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    • pp.93-100
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    • 2021

  • 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.

  • https://doi.org/10.11001/jksww.2021.35.1.093 인용 PDF KSCI