4.5 Article

Incipient Biofouling Detection via Fiber Optical Sensing and Image Analysis in Reverse Osmosis Processes

期刊

MEMBRANES
卷 13, 期 6, 页码 -

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MDPI
DOI: 10.3390/membranes13060553

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reverse osmosis; biofouling; in-situ detection; polymer optical fiber sensors; image analysis

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Reverse osmosis (RO) is a widely used membrane technology for water production, but biofouling poses a significant challenge. This study introduces two methods for early detection of biofouling using optical fiber sensors and image analysis. The effectiveness of these methods was validated through accelerated biofouling experiments.
Reverse osmosis (RO) is a widely used membrane technology for producing process water or tap water that is receiving increased attention due to water scarcity caused by climate change. A significant challenge in any membrane filtration is the presence of deposits on the membrane surfaces, which negatively affect filtration performance. Biofouling, the formation of biological deposits, poses a significant challenge in RO processes. Early detection and removal of biofouling are essential for effective sanitation and prevention of biological growth in RO-spiral wound modules. This study introduces two methods for the early detection of biofouling, capable of identifying initial stages of biological growth and biofouling in the spacer-filled feed channel. One method utilizes polymer optical fibre sensors that can be easily integrated into standard spiral wound modules. Additionally, image analysis was used to monitor and analyze biofouling in laboratory experiments, providing a complementary approach. To validate the effectiveness of the developed sensing approaches, accelerated biofouling experiments were conducted using a membrane flat module, and the results were compared with common online and offline detection methods. The reported approaches enable the detection of biofouling before known online parameters become indicative, effectively providing an online detection with sensitivities otherwise only achieved through offline characterization methods.

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