Research on the factors influencing nanofiltration membrane fouling and the prediction of membrane fouling

The issue of membrane fouling poses a significant challenge to the extensive adoption of nanofiltration membrane technology in public water supply systems. The occurrence of bottlenecks is a common issue in the implementation of nanofiltration production. The mitigation of fouling in nanofiltration membranes has emerged as a significant research focus within the water treatment field. Numerous scholars have dedicated their efforts to researching the mechanisms of membrane fouling and constructing intricate mathematical and physical models to explain the relevant mechanisms. The goal of these endeavors is to achieve long-term stability and high throughput operation of nanofiltration processes. However, traditional mathematical models rely on simplifying assumptions and are less likely to capture the dynamics of membrane contamination in practical applications. Machine learning is rapidly emerging as a novel approach for predicting membrane fouling, owing to the rapid progress in artificial intelligence. Machine learning can autonomously learn from historical data, fully harness the value of data, and comprehend the inherent correlation between membrane pollution and various influencing factors. This makes it possible to predict trends in pollution and even facilitates autonomous decision-making, automatic adjustment, and optimization of membrane process flow. Ultimately, it helps create an intelligent water ecosystem. The present study incorporates both local and international research to examine the key factors that contribute to nanofiltration membrane fouling. This review focuses on the influence of raw water quality parameters, membrane material characteristics, and operating conditions. Additionally, this paper presents a comprehensive analysis of the application of machine learning techniques in predicting nanofiltration membrane fouling.