Application of machine learning algorithms in MBR simulation under big data platform

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...

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Bibliographic Details
Published in:Water practice and technology 2020-12, Vol.15 (4), p.1238-1247
Main Authors: Li, Weiwei, Li, Chunqing, Wang, Tao
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Bioreactors
Classification
Computer simulation
Decision trees
Eigenvalues
Fault diagnosis
Fluctuations
Flux
Fouling
Learning algorithms
Machine learning
Membrane processes
Membrane separation
Membranes
Model testing
Neural networks
Prediction models
Principal components analysis
Random variables
Sewage
Sewage treatment
Software
Statistical analysis
Support vector machines
Wastewater treatment
Water treatment
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Summary: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.
ISSN:1751-231X
1751-231X
DOI:10.2166/wpt.2020.095