Prediction of Membrane Fouling based on SSA-LSTM Neural Network
DOI:
https://doi.org/10.54691/fse.v2i8.1717Keywords:
MBR, Membrane Flux Prediction, LSTM, SSA-LSTM ModelAbstract
In view of the difficulty in obtaining the membrane bioreactor (MBR) membrane flux in real time and stable control, a sparrow search algorithm (SSA) is proposed to optimize the SSA-LSTM prediction model of long short-term memory (LSTM) neural network. Firstly, principal component analysis (PCA) is used to realize the dimensionality reduction of auxiliary variables. Second, use the sparrow search algorithm to determine the relevant hyper-parameters of the LSTM neural network. Finally, the selected auxiliary variables are used as the input of the SSA-LSTM prediction model, and the membrane flux is used as the prediction output, the measured data is used as the sample for experimental verification. The accuracy rate reaches 94.31%, which is much higher than 63.63% of LSTM. The results show that the proposed membrane flux prediction model has higher prediction performance.
Downloads
References
Qin, X.; Gao, F.; Chen, G. Wastewater quality monitoring system using sensor fusion and machine learning techniques. Water Res. 2012, 46, 1133-1144.
Tsui, T.H.; Zhang, L.; Zhang, J.X.; Dai, Y.J.; Tong, Y.W. Engineering interface between bioenergy recovery and biogas desulfurization: Sustainability interplays of biochar application. Renew sust energ rev. 2022, 157,112053.
Han, H.G.; Zhang, S.; Qiao, J.F. Soft-sensor Method for Permeability of the Membrane Bio-Reactor Based on Recurrent Radial Basis Function Neural Network. J. Beijing Univ. Technol. 2017, 43, 1168–1174.
Deb, A.; Gurung, K.; Rumky, J.; Sillanpaa, M.; Manttari, M.; Kallioinen, M. Dynamics of microbial community and their ef-fects on membrane fouling in an anoxic-oxic gravity-driven membrane bioreactor under varying solid retention time: A pilot-scale study. Science of The Total Environment. 2022, 807, 150878.
Shi, Y.K.; Wang Z.W.; Du, X.J.; Gong B.; Jegatheesan, V.; Haq UI. Recent advances in the prediction of fouling in membrane bioreactors. Membranes. 2021, 11, 381.
Al-Zoubi, H.; Hilal, N.; Darwish, N.A.; Mohammad, A.W. Rejection and modelling of sulphate and potassium salts by nanofiltration membranes: Neural network and Spiegler-Kedem model. Desalination. 2006, 206, 42–60.
Liu, Z.F.; Pan, D.; Wang, J.H.; Yang, S.X. The film pollution forecast of PSO-BP neural network in MBR technology. Journal of Beijing University of Technology. 2012, 38, 126-131.
Liu, Y.L. Study on prediction model of coal gangue subgrade settlement based on SSA-SVR. Journal of Hebei University of Geosciences. 2021, 44, 99-104.
Liu, D.; Wei, X.; Wang, W.Q.; Ye, J.H.; Ren, J. Short-term wind power prediction based on SSA-ELM. Smart Power. 2021, 49, 53-59.
T. Fischer and C. Krauss, ‘‘Deep learning with long short-term memory networks for financial market predictions,’’ Eur. J. Oper. Res., vol. 270,no. 2, pp. 654–669, Oct. 2018.
A. Alahi, K. Goel, V. Ramanathan, A. Robicquet, L. Fei-Fei, and S. Savarese, ‘‘Social LSTM: Human trajectory prediction in crowded spaces,’’ in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR),Jun. 2016, pp. 961–971.
Q. Zhang, H. Wang, J. Dong, G. Zhong, and X. Sun, ‘‘Prediction of sea surface temperature using Long Short-term memory,’’ IEEE Geosci. Remote Sens. Lett., vol. 14, no. 10, pp. 1745–1749, Oct. 2017.
Y. Zhu, X. Fan, J.Wu, X. Liu, J. Shi, and C.Wang, ‘‘Predicting ICU mortal-ity by supervised bidirectional LSTM networks,’’ in Proc. 1st Joint Work-shop AI Health, Organized Part Federated AI Meeting, 2018, pp. 49–60.
Zhu, J.; Liu, S.; Fan, N.; Shen, X.; Guo, X. A Short-term power load forecasting Method Based on LSTM Neural Network. China New Telecommun. 2021, 23, 167–168.
Xue, J.; Shen, B. A novel swarm intelligence optimization approach: sparrow search algorithm. Systems Science & Control Engineering an Open Access Journal. 2020, 8, 22-34.
Yoon, S.H. Membrane Bioreactor Processes (Advances in Water and Wastewater Transport and Treatment), 1st ed.; CRC Press: Florida, USA, 2020; pp. 55–67.
Downloads
Published
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
