Prediction of membrane purification by membrane fouling based on mathematic and machine learning models combined with image processing technology

Apples have been widely cultivated due to their multiple functional nutrients such as polyphenols. In this study, polyphenol of apple skins were extracted and purified by ultrasound treatment and membrane filtration, respectively. The effects of feed liquid concentration, transmembrane pressure, and rotational speed on the purification of polyphenols by membrane filtration were mainly investigated. The efficiency of polyphenol purification was analyzed based on the particle deposition effect, steric rejection effect, and pectin sieving effect. Single and multi-stage Hermia models were applied to corroborate the aforementioned theory. The degree of membrane fouling was quantitatively analyzed using a self-written image processing program. The study compared the quantitative prediction models between the degree of membrane fouling and polyphenol purification using mathematical models and two machine learning (ML) models, namely Support Vector Machine Regression (SVR) and Back Propagation Artificial neural networks (BPNN). The degree of membrane fouling and the degree of polyphenol purification were used as input parameters and output parameters of the ML prediction model, respectively. The ratio of the training set (98 data points) and the test set (42 data points) was 7:3. The obtained results showed that the prediction efficiency of BPNN model (R2 =0.99968) outperformed the SVR model (R2 =0.97697), which was better than the mathematical model in terms of prediction performance due to the high generalization ability of the ML model and the high robustness of BPNN. The study provided an effective and accurate method for predicting membrane fouling and filtration purification. Thus, the use of ML as an artificial intelligence technique has a promising future in membrane separation and purification.