Osmotic power is to produce electric power by using the chemical potential of two flows with the difference of salinity. Water permeates through a semipermeable membrane from a low concentration feed solution to a high concentration draw solution due to osmotic pressure. In a pressure retarded osmosis (PRO) process, river water and wastewater are commonly used as low salinity feed solution, whereas seawater and brine from the SWRO plant are employed as draw solution. During the PRO process using wastewater effluent as feed solution, PRO membrane fouling is usually caused by the convective or diffusive transport of PRO which is the most critical step of PRO membrane in order to prevent membrane fouling. The main objective of this study is to assess the PRO membrane fouling reduction by pretreatment to remove organic matter using coagulation-UF membrane process. The experimental results obtained from the pretreatment test showed that the optimum ferric chloride and PAC dosage for removal of organic matter applied for the coagulation and adsorption process was 50 mg/L as FeCl3 (optimum pH 5.5). Coagulation-UF pretreatment process was higher removal efficiency of organic matter, as also resulting in the substantial improvement of water flux of PRO membrane.

물 오염, 지구 온난화, 기후 변화를 해결하기 위한 해결책이 시급한 상황에서, 담수의 수요를 충당하고 친환경 에너지를 생산하기 위한 방법으로 염도차를 이용한 압력지연삼투공정이 제시되고 있다. 압력지연삼투공정에 대한 꾸준한 연구에도 불구하고 최근 기술의 부족과 비싼 멤브레인의 가격 등의 한계로 인해 상용화가 되지 않고 있다. 한편 멤브레인은 압력 지연삼투공정과 염도차 발전 기술에 가장 중요한 구성품이다. 염도차 발전 기술에 사용되는 산화그래핀 멤브레인과 나노복합체 멤브레인의 기술 발전 연구가 지속되고 있다. 특히 낮은 온도의 폐기물 온도에서도 높은 에너지 효율 발전이 가능하도록 효율이 높은 멤브레인과 용매 및 용질에 대한 연구가 활발하다. 높은 투과도와 분리도를 가진 멤브레인, 특히 산화그래핀 멤브레인을 사용함으로써 농도 분극을 줄이고 전력 밀도를 높이는 연구들도 진행 중이다. 본 총설에서는 압력지연삼투 멤브레 인과 이를 통한 이론적 모델링, 그 외 기술을 통해 공정의 효율을 발전시키는 방법에 대해 논의한다.

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.

In this study, a pilot-scale (3 m3/day) membrane distillation (MD) process was operated to treat digestate produced from anaerobic digestion of livestock wastewater. In order to evaluate the performance and energy cost of MD process, it was compared with the pilot scale (10 m3/day) reverse osmosis (RO) process, expected competitive process, under same feed condition. As results, MD process shows stable permeate flux (average 10.1 L/m2/hr) until 150 hours, whereas permeate flux of RO process was decreased from 5.3 to 1.5 L/m2/hr within 24 hours. In the case of removal of COD, TN, and TP, MD process shows a high removal rate (98.7, 93.7, and 99% respectively) stably until 150 hours. However, in the case of RO process, removal rate was decreased from 91.6 to 69.5% in COD and from 93.7 to 76.0% in TP during 100 hours of operation. Removal rate of TN in RO process was fluctuated in the range of 34.5-62.9% (average 44.6%) during the operation. As a result of energy cost analysis, MD process using waste heat for heating the feed shows 18% lower cost compare with RO process. Thus, overall efficiency of the MD process is higher then that of the RO process in terms of permeate flux, removal rate of salts, and operating cost (in the case of using waste heat) in treating the anaerobic digestate of livestock wastewater.

2018년 환경부에서 발표된 수도정비기본계획에 따라 다양한 수자원 활용의 중요성이 증가하고 있으며, 여러 수원 을 혼합하여 원수 또는 생산수로 활용하는 워터 블렌딩 방식은 미국, 호주를 비롯한 여러 나라에서 시도되고 있다. 본 연구에 서는 공업용수 공급 목적으로 100,000 m3/일 규모 해수담수화 사업이 추진되고 있는 충남 대산 지역을 대상으로, 해수와 호 소수, 침전수, 폐수 방류수 등 타 수원을 블렌딩할 때 수종 및 혼합비율에 따른 영향을 분석하였다. 타 수원 혼합비율 10~50% 조건에서 혼합수 염분농도는 약 50%까지 감소하였지만, 탁질 및 유기물 농도는 1.6~2.0배 수준으로 증가하는 것을 확인하였 다. 실험실 규모 역삼투 공정 성능평가 결과, 해수의 단독활용 대비 원수 혼합 시 막오염 경향이 증가하였으며 혼합비율 10~50%에서 평균 4.1배의 플럭스 저감률을 나타내었다. 성능모사를 통한 역삼투 공정 성능분석에 따르면 혼합비율 50% 조 건에서 역삼투 공정 에너지 사용량이 평균 39% 절감될 수 있을 것으로 기대되나, 운영비용 등 혼합수 활용에 대한 전반적인 영향분석을 위해서는 모형플랜트 규모에서 장기간 성능평가가 필요하다.