Application of machine learning algorithms in MBR simulation under big data platform
Weiwei Li, Chunqing Li, Tao Wang
Abstract
Abstract Membrane bioreactors (MBRs) are a sewage treatment process that combines membrane separation with bioreactor technology. It has great advantages in sewage treatment. Membrane fouling hinders MBR process development, however. Studies have shown that the degree of membrane fouling can be judged using the membrane flux rate. In this study, principal component analysis was used to extract the main factors affecting membrane fouling, then the random forest algorithm on the Hadoop big data platform was used to establish an MBR membrane flux prediction model, which was tested. In order to verify the model's effectiveness, BP neural network and SVM support vector machine models were established using the same experimental data. The experimental results from the different models were compared, and the results showed that the random forest algorithm gave the best MBR membrane flux predictions.