SCI和EI收录∣中国化工学会会刊

中国化学工程学报 ›› 2023, Vol. 53 ›› Issue (1): 37-45.DOI: 10.1016/j.cjche.2022.01.028

• Full Length Article • 上一篇    下一篇

Multivariable identification of membrane fouling based on compacted cascade neural network

Kun Ren1,2,3, Zheng Jiao1,2,3, Xiaolong Wu1,3, Honggui Han1,2,3   

  1. 1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
    2. Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China;
    3. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
  • 收稿日期:2021-09-13 修回日期:2022-01-12 出版日期:2023-01-28 发布日期:2023-04-08
  • 通讯作者: Honggui Han,E-mail:Rechardhan@sina.com
  • 基金资助:
    We acknowledge financial supports by National Key Research and Development Project (2018YFC1900800-5), National Natural Science Foundation of China (61890930-5, 62021003, 61903010 and 62103012), Beijing Outstanding Young Scientist Program (BJJWZYJH01201910005020), Beijing Natural Science Foundation (KZ202110005009 and 4214068).

Multivariable identification of membrane fouling based on compacted cascade neural network

Kun Ren1,2,3, Zheng Jiao1,2,3, Xiaolong Wu1,3, Honggui Han1,2,3   

  1. 1. Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China;
    2. Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China;
    3. Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China
  • Received:2021-09-13 Revised:2022-01-12 Online:2023-01-28 Published:2023-04-08
  • Contact: Honggui Han,E-mail:Rechardhan@sina.com
  • Supported by:
    We acknowledge financial supports by National Key Research and Development Project (2018YFC1900800-5), National Natural Science Foundation of China (61890930-5, 62021003, 61903010 and 62103012), Beijing Outstanding Young Scientist Program (BJJWZYJH01201910005020), Beijing Natural Science Foundation (KZ202110005009 and 4214068).

摘要: The membrane fouling phenomenon, reflected with various fouling characterization in the membrane bioreactor (MBR) process, is so complicated to distinguish. This paper proposes a multivariable identification model (MIM) based on a compacted cascade neural network to identify membrane fouling accurately. Firstly, a multivariable model is proposed to calculate multiple indicators of membrane fouling using a cascade neural network, which could avoid the interference of the overlap inputs. Secondly, an unsupervised pretraining algorithm was developed with periodic information of membrane fouling to obtain the compact structure of MIM. Thirdly, a hierarchical learning algorithm was proposed to update the parameters of MIM for improving the identification accuracy online. Finally, the proposed model was tested in real plants to evaluate its efficiency and effectiveness. Experimental results have verified the benefits of the proposed method.

关键词: Membrane fouling, Permeability, Cascade neural networks, Model, Prediction

Abstract: The membrane fouling phenomenon, reflected with various fouling characterization in the membrane bioreactor (MBR) process, is so complicated to distinguish. This paper proposes a multivariable identification model (MIM) based on a compacted cascade neural network to identify membrane fouling accurately. Firstly, a multivariable model is proposed to calculate multiple indicators of membrane fouling using a cascade neural network, which could avoid the interference of the overlap inputs. Secondly, an unsupervised pretraining algorithm was developed with periodic information of membrane fouling to obtain the compact structure of MIM. Thirdly, a hierarchical learning algorithm was proposed to update the parameters of MIM for improving the identification accuracy online. Finally, the proposed model was tested in real plants to evaluate its efficiency and effectiveness. Experimental results have verified the benefits of the proposed method.

Key words: Membrane fouling, Permeability, Cascade neural networks, Model, Prediction