Artificial Intelligence Application in Membrane Processes and Prediction of Fouling for Better Resource Recovery

Abstract

Water contamination is a global issue due to the emergence of new contaminants from solvents, personal care products, and pharmaceutical compounds. Membrane processes appear to be effective and promising in water treatment. While membrane processes can significantly reduce the levels of contaminants, problems continue to arise, such as fouling. The utilization of artificial intelligence (AI) to predict fouling and enhance the characteristics of membranes is currently receiving attention. Various artificial intelligence (AI) models can be employed to optimize the input parameters based on the output, which helps in predicting membrane performance and assessing its ability to reject contaminants effectively. The possibilities for improvement in membrane technologies and filtration processes using AI techniques are discussed in this paper. Membrane fouling causes significant issues during the operation due to the accumulation of impurities onto the membrane, which reduces the membrane’s ability to function properly. AI algorithms can be used to predict permeate flux and fouling growth properties. The paper concludes that AI utilization for the prediction of membrane fouling can enhance the membrane selection for the processes, reduce costs with better fouling control system development and make the process more scalable on an industrial scale. The literature showed that there are models, such as the Neural-fuzzy interference system, that can predict forward osmosis membranes’ performance with a high correlation of 0.997 and a root mean square error of 0.04. The paper also concludes that the exploration of more novel deep learning architectures like GANs would facilitate better resource recovery from wastewater and improved prediction of fouling in membrane processes.