Multivariable identification of membrane fouling based on compacted cascade neural network

doi:10.1016/j.cjche.2022.01.028

Chinese Journal of Chemical Engineering ›› 2023, Vol. 53 ›› Issue (1): 37-45.DOI: 10.1016/j.cjche.2022.01.028

Previous Articles Next Articles

Multivariable identification of membrane fouling based on compacted cascade neural network

Kun Ren^1,2,3, Zheng Jiao^1,2,3, Xiaolong Wu^1,3, Honggui Han^1,2,3

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:2023-04-08 Published:2023-01-28
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).

Multivariable identification of membrane fouling based on compacted cascade neural network

Kun Ren^1,2,3, Zheng Jiao^1,2,3, Xiaolong Wu^1,3, Honggui Han^1,2,3

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

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).

Abstract

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

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

Kun Ren, Zheng Jiao, Xiaolong Wu, Honggui Han. Multivariable identification of membrane fouling based on compacted cascade neural network[J]. Chinese Journal of Chemical Engineering, 2023, 53(1): 37-45.

Kun Ren, Zheng Jiao, Xiaolong Wu, Honggui Han. Multivariable identification of membrane fouling based on compacted cascade neural network[J]. 中国化学工程学报, 2023, 53(1): 37-45.

References

[1] T. Yang, F. Liu, H.F. Xiong, Q.Y. Yang, F.S. Chen, C.C. Zhan, Fouling process and anti-fouling mechanisms of dynamic membrane assisted by photocatalytic oxidation under sub-critical fluxes, Chin. J. Chem. Eng. 27 (8) (2019) 1798–1806.10.1016/j.cjche.2018.10.019
[2] Y.J. Shao, Z. Zhou, J. Jiang, L.M. Jiang, J.P. Huang, Y. Zuo, Y.Q. Ren, X.D. Zhao, Membrane fouling in anoxic/oxic membrane reactors coupled with carrier-enhanced anaerobic side-stream reactor: Effects of anaerobic hydraulic retention time and mechanism insights, J. Membr. Sci. 637 (2021) 119657.10.1016/j.memsci.2021.119657
[3] R.K. Dereli, X.F. Wang, F.P. van der Zee, J.B. van Lier, Biological performance and sludge filterability of anaerobic membrane bioreactors under nitrogen limited and supplied conditions, Water Res. 137 (2018) 164–172.10.1016/j.watres.2018.03.015
[4] R.H. Peiris, H. Budman, C. Moresoli, R.L. Legge, Fouling control and optimization of a drinking water membrane filtration process with real-time model parameter adaptation using fluorescence and permeate flux measurements, J. Process. Control 23 (1) (2013) 70–77.10.1016/j.jprocont.2012.10.001
[5] K. Kimura, K. Shikato, Y. Oki, K. Kume, S.A. Huber, Surface water biopolymer fractionation for fouling mitigation in low-pressure membranes, J. Membr. Sci. 554 (2018) 83–89.10.1016/j.memsci.2018.02.024
[6] K.L. Zeng, J. Zhou, Z.L. Cui, Y. Zhou, C. Shi, X.Z. Wang, L.Y. Zhou, X.B. Ding, Z.H. Wang, E. Drioli, Insight into fouling behavior of poly(vinylidene fluoride) (PVDF) hollow fiber membranes caused by dextran with different pore size distributions, Chin. J. Chem. Eng. 26 (2) (2018) 268–277.10.1016/j.cjche.2017.04.008
[7] Q. Liu, J.Y. Ren, Y.S. Lu, X.L. Zhang, F.A. Roddick, L.H. Fan, Y.F. Wang, H.R. Yu, P. Yao, A review of the current in situ fouling control strategies in MBR: Biological versus physicochemical, J. Ind. Eng. Chem. 98 (2021) 42–59.10.1016/j.jiec.2021.03.042
[8] W.W. Cao, Q.M. Yang, Online sequential extreme learning machine based adaptive control for wastewater treatment plant, Neurocomputing 408 (2020) 169–175.10.1016/j.neucom.2019.05.109
[9] M.B. Asif, B. Ren, C. Li, K. He, X. Zhang, Z. Zhang, Understanding the role of in-situ ozonation in Fe(II)-dosed membrane bioreactor (MBR) for membrane fouling mitigation, J. Membr. Sci, 633 (2021) 1–11.
[10] J. Xu, C.D. Li, X. He, T.W. Huang, Recurrent neural network for solving model predictive control problem in application of four-tank benchmark, Neurocomputing 190 (2016) 172–178.10.1016/j.neucom.2016.01.020
[11] Y.F. Xie, J.J. Yu, S.W. Xie, T.W. Huang, W.H. Gui, On-line prediction of ferrous ion concentration in goethite process based on self-adjusting structure RBF neural network, Neural Netw. 116 (2019) 1–10.https://pubmed.ncbi.nlm.nih.gov/30986722/
[12] M.C.S. Gomes, W.M. Moreira, S.M. Paschoal, C.C. Sipoli, R.M. Suzuki, J.G. Sgorlon, N.C. Pereira, Modeling of fouling mechanisms in the biodiesel purification using ceramic membranes, Sep. Purif. Technol. 269 (2021) 118595.10.1016/j.seppur.2021.118595
[13] M.F. Wu, Y.F. Chen, H.J. Lin, L.H. Zhao, L.G. Shen, R.J. Li, Y.C. Xu, H.C. Hong, Y.M. He, Membrane fouling caused by biological foams in a submerged membrane bioreactor: Mechanism insights, Water Res. 181 (2020) 115932.10.1016/j.watres.2020.115932
[14] M.M. Amin, E. Taheri, A. Fatehizadeh, M. Rezakazemi, T.M. Aminabhavi, Anaerobic membrane bioreactor for the production of bioH₂: Electron flow, fouling modeling and kinetic study, Chem. Eng. J. 426 (2021) 130716.10.1016/j.cej.2021.130716
[15] K. Kimura, K. Kume, Irreversible fouling in hollow-fiber PVDF MF/UF membranes filtering surface water: Effects of precoagulation and identification of the foulant, J. Membr. Sci. 602 (2020) 117975.10.1016/j.memsci.2020.117975
[16] M. Dalmau, N. Atanasova, S. Gabarrón, I. Rodriguez-Roda, J. Comas, Comparison of a deterministic and a data driven model to describe MBR fouling, Chem. Eng. J. 260 (2015) 300–308.10.1016/j.cej.2014.09.003
[17] N. Philippe, A.E. Stricker, Y. Racault, A. Husson, M. Sperandio, P. Vanrolleghem, Modelling the long-term evolution of permeability in a full-scale MBR: Statistical approaches, Desalination 325 (2013) 7–15.10.1016/j.desal.2013.04.027
[18] G. Hu, X.S. Liu, Z. Wang, X.P. Du, X. Wang, Comparison of fouling behaviors between activated sludge suspension in MBR and EPS model solutions: A new combined model, J. Membr. Sci. 621 (2021) 119020.10.1016/j.memsci.2020.119020
[19] S.G. Zhu, H.G. Han, M. Guo, J.F. Qiao, A data-derived soft-sensor method for monitoring effluent total phosphorus, Chin. J. Chem. Eng. 25 (12) (2017) 1791–1797.10.1016/j.cjche.2017.06.008
[20] A.V. Santos, A.R.A. Lin, M.C.S. Amaral, S.M.A.C. Oliveira, Improving control of membrane fouling on membrane bioreactors: A data-driven approach, Chem. Eng. J. 426 (2021) 131291.10.1016/j.cej.2021.131291
[21] X.L. Wu, H.G. Han, J.F. Qiao, Data-driven intelligent warning method for membrane fouling, IEEE Trans. Neural Netw. Learn. Syst. 32 (8) (2021) 3318–3329.https://pubmed.ncbi.nlm.nih.gov/33417565/
[22] H.G. Han, X.L. Wu, L.M. Ge, J.F. Qiao, A sludge volume index (SVI) model based on the multivariate local quadratic polynomial regression method, Chin. J. Chem. Eng. 26 (5) (2018) 1071–1077.10.1016/j.cjche.2017.08.007
[23] Y.H. Cai, X. Li, A.A. Zaidi, Y. Shi, K. Zhang, R.Z. Feng, A.Q. Lin, C. Liu, Effect of hydraulic retention time on pollutants removal from real ship sewage treatment via a pilot-scale air-lift multilevel circulation membrane bioreactor, Chemosphere 236 (2019) 124338.10.1016/j.chemosphere.2019.07.069
[24] J.F. Qiao, W. Li, H.G. Han, Soft computing of biochemical oxygen demand using an improved T-S fuzzy neural network, Chin. J. Chem. Eng. 22 (11–12) (2014) 1254–1259.10.1016/j.cjche.2014.09.023
[25] M. Barello, D. Manca, R. Patel, I.M. Mujtaba, Neural network based correlation for estimating water permeability constant in RO desalination process under fouling, Desalination 345 (2014) 101–111.10.1016/j.desal.2014.04.016
[26] F. Schmitt, R. Banu, I.T. Yeom, K.U. Do, Development of artificial neural networks to predict membrane fouling in an anoxic-aerobic membrane bioreactor treating domestic wastewater, Biochem. Eng. J. 133 (2018) 47–58.10.1016/j.bej.2018.02.001
[27] A. Hosseinzadeh, J.L. Zhou, A. Altaee, M. Baziar, X.W. Li, Modeling water flux in osmotic membrane bioreactor by adaptive network-based fuzzy inference system and artificial neural network, Bioresour. Technol. 310 (2020) 123391.10.1016/j.biortech.2020.123391
[28] A. Giwa, S. Daer, I. Ahmed, P.R. Marpu, S.W. Hasan, Experimental investigation and artificial neural networks ANNs modeling of electrically-enhanced membrane bioreactor for wastewater treatment, J. Water Process. Eng. 11 (2016) 88–97.10.1016/j.jwpe.2016.03.011
[29] W.Y. Lin, L. Jing, Z.W. Zhu, Q.H. Cai, B.Y. Zhang, Removal of heavy metals from mining wastewater by micellar-enhanced ultrafiltration (MEUF): Experimental investigation and Monte Carlo-based artificial neural network modeling, Water Air Soil Pollut. 228 (6) (2017) 1–11.10.1007/s11270-017-3386-5
[30] H.G. Han, S.G. Zhu, J.F. Qiao, M. Guo, Data-driven intelligent monitoring system for key variables in wastewater treatment process, Chin. J. Chem. Eng. 26 (10) (2018) 2093–2101.10.1016/j.cjche.2018.03.027
[31] K. Nam, S. Heo, G. Rhee, M. Kim, C. Yoo, Dual-objective optimization for energy-saving and fouling mitigation in MBR plants using AI-based influent prediction and an integrated biological-physical model, J. Membr. Sci. 626 (2021) 119208.10.1016/j.memsci.2021.119208
[32] H. Hazrati, A.H. Moghaddam, M. Rostamizadeh, The influence of hydraulic retention time on cake layer specifications in the membrane bioreactor: Experimental and artificial neural network modeling, J. Environ. Chem. Eng. 5 (3) (2017) 3005–3013.10.1016/j.jece.2017.05.050
[33] N.D. Viet, A. Jang, Development of artificial intelligence-based models for the prediction of filtration performance and membrane fouling in an osmotic membrane bioreactor, J. Environ. Chem. Eng. 9 (4) (2021) 105337.10.1016/j.jece.2021.105337
[34] A.R. Pendashteh, A. Fakhru’l-Razi, N. Chaibakhsh, L.C. Abdullah, S.S. Madaeni, Z.Z. Abidin, Modeling of membrane bioreactor treating hypersaline oily wastewater by artificial neural network, J. Hazard. Mater. 192 (2) (2011) 568–575.10.1016/j.jhazmat.2011.05.052
[35] S.A. Mirbagheri, M. Bagheri, Z. Bagheri, A.M. Kamarkhani, Evaluation and prediction of membrane fouling in a submerged membrane bioreactor with simultaneous upward and downward aeration using artificial neural network-genetic algorithm, Process. Saf. Environ. Prot. 96 (2015) 111–124.10.1016/j.psep.2015.03.015
[36] J.W. Liu, X.Y. Kang, X.R. Luan, L.T. Gao, H.Y. Tian, X.L. Liu, Performance and membrane fouling behaviors analysis with SVR-LibSVM model in a submerged anaerobic membrane bioreactor treating low-strength domestic sewage, Environ. Technol. Innov. 19 (2020) 100844.10.1016/j.eti.2020.100844
[37] R.Y. Cao, J.J. Zhou, W.W. Chen, Insights into membrane fouling implicated by physical adsorption of soluble microbial products onto D3520 resin, Chin. J. Chem. Eng. 28 (2) (2020) 429–439.10.1016/j.cjche.2019.06.005
[38] S.Z. Hao, X.H. Zhao, H.W. Zhang, Y. Wu, C. Fang, X.J. Wang, Effects of a novel bimetallic catalytic biofilter-based pretreatment technique on the form of ultrafiltration membrane fouling, Chin. J. Chem. Eng. 28 (10) (2020) 2513–2522.10.1016/j.cjche.2020.03.015
[39] Y. Suo, Y.S. Ren, Research on the mechanism of nanofiltration membrane fouling in zero discharge process of high salty wastewater from coal chemical industry, Chem. Eng. Sci. 245 (2021) 116810.10.1016/j.ces.2021.116810
[40] M. Bagheri, A. Akbari, S.A. Mirbagheri, Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: A critical review, Process. Saf. Environ. Prot. 123 (2019) 229–252.10.1016/j.psep.2019.01.013
[41] C.H. Wang, A.S. Wei, H. Wu, F.S. Qu, W.X. Chen, H. Liang, G.B. Li, Application of response surface methodology to the chemical cleaning process of ultrafiltration membrane, Chin. J. Chem. Eng. 24 (5) (2016) 651–657.10.1016/j.cjche.2016.01.002
[42] W.J. Ding, M. Chen, M. Zhou, Z.X. Zhong, Z.L. Cui, W.H. Xing, Fouling behavior of poly(vinylidene fluoride) (PVDF) ultrafiltration membrane by polyvinyl alcohol (PVA) and chemical cleaning method, Chin. J. Chem. Eng. 28 (12) (2020) 3018–3026.10.1016/j.cjche.2020.05.032
[43] F. A, W. de Wilde, M. Gaertner, M. Weemaes, G. de Gueldre, B.V. de Steene, Elaborating the membrane life concept in a full scale hollow-fibers MBR, J. Membr. Sci. 421-422 (2012) 349–354.10.1016/j.memsci.2012.08.001
[44] C. Niewersch, C. Rieth, L. Hailemariam, G.G. Oriol, J. Warczok, Reverse osmosis membrane element integrity evaluation using imperfection model, Desalination 476 (2020) 114175.10.1016/j.desal.2019.114175
[45] B. Zhao, H. Chen, D.K. Gao, L.Z. Xu, Y.Y. Zhang, Cleaning decision model of MBR membrane based on Bandelet neural network optimized by improved Bat algorithm, Appl. Soft Comput. 91 (2020) 106211.10.1016/j.asoc.2020.106211
[46] H.G. Han, S. Zhang, J.F. Qiao, X.S. Wang, An intelligent detecting system for permeability prediction of MBR, Water Sci. Technol. 77 (1–2) (2018) 467–478.https://pubmed.ncbi.nlm.nih.gov/29377831/
[47] H. Guo, Y. Wyart, J. Perot, F. Nauleau, P. Moulin, Low-pressure membrane integrity tests for drinking water treatment: A review, Water Res 44 (1) (2010) 41–57.https://pubmed.ncbi.nlm.nih.gov/19836818/
[48] P. Cote, Z. Alam, J. Penny, Hollow fiber membrane life in membrane bioreactors (MBR), Desalination 288 (2012) 145–151.10.1016/j.desal.2011.12.026
[49] D.F. Ayala, V. Ferre, S.J. Judd, Membrane life estimation in full-scale immersed membrane bioreactors, J. Membr. Sci. 378 (1–2) (2011) 95–100.

Multivariable identification of membrane fouling based on compacted cascade neural network

Multivariable identification of membrane fouling based on compacted cascade neural network

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

[1]	Huiqi Wang, Jianpo Ren, Shihao Zhang, Jiayu Dai, Yue Niu, Ketao Shi, Qiuxiang Yin, Ling Zhou. Measurement and correlation of solubility of 9-fluorenone in 11 pure organic solvents from T = 283.15 to 323.15 K [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 235-241.
[2]	Jindong Dai, Chi Zhai, Jiali Ai, Guangren Yu, Haichao Lv, Wei Sun, Yongzhong Liu. A cellular automata framework for porous electrode reconstruction and reaction-diffusion simulation [J]. Chinese Journal of Chemical Engineering, 2023, 60(8): 262-274.
[3]	Borui Liu, Tao Zhang, Yi Zheng, Kailong Li, Hui Pan, Hao Ling. A dynamic control structure of liquid-only transfer stream distillation column [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 135-145.
[4]	Zhonghao Li, Yuanyuan Yang, Huanong Cheng, Yun Teng, Chao Li, Kangkang Li, Zhou Feng, Hongwei Jin, Xinshun Tan, Shiqing Zheng. Measurement and model of density, viscosity, and hydrogen sulfide solubility in ferric chloride/trioctylmethylammonium chloride ionic liquid [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 210-221.
[5]	Yaran Bu, Changchun Wu, Lili Zuo, Qian Chen. The calculation and optimal allocation of transmission capacity in natural gas networks with MINLP models [J]. Chinese Journal of Chemical Engineering, 2023, 59(7): 251-261.
[6]	Junhao Wang, Shugang Ma, Peng Chen, Zhipeng Li, Zhengming Gao, J. J. Derksen. Mixing of miscible shear-thinning fluids in a lid-driven cavity [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 112-123.
[7]	Weikai Ren, Runsong Dai, Ningde Jin. Modeling of liquid film thickness around Taylor bubbles rising in vertical stagnant and co-current slug flowing liquids [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 179-194.
[8]	Hongwei Liang, Wenling Li, Zisheng Feng, Jianming Chen, Guangwen Chu, Yang Xiang. Numerical simulation of gas-liquid flow in the bubble column using Wray-Agarwal turbulence model coupled with population balance model [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 205-223.
[9]	Yun-Zhang Liu, Lu-Yao Zhang, Dan He, Li-Zhen Chen, Zi-Shuai Xu, Jian-Long Wang. Solubility measurement, correlation and thermodynamic properties of 2, 3, 4-trichloro-1, 5-dinitrobenzene in fifteen mono-solvents at temperatures from 278.15 to 323.15 K [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 224-233.
[10]	Danlei Chen, Yiqing Luo, Xigang Yuan. Cascade refrigeration system synthesis based on hybrid simulated annealing and particle swarm optimization algorithm [J]. Chinese Journal of Chemical Engineering, 2023, 58(6): 244-255.
[11]	Guangyuan Chen, Tong Zhou, Meng Zhang, Zhongxiang Ding, Zhikun Zhou, Yuanhui Ji, Haiying Tang, Changsong Wang. Effects of heavy metal ions Cu²⁺/Pb²⁺/Zn²⁺ on kinetic rate constants of struvite crystallization [J]. Chinese Journal of Chemical Engineering, 2023, 57(5): 10-16.
[12]	Li Xia, Yule Pan, Tingting Zhao, Xiaoyan Sun, Shaohui Tao, Yushi Chen, Shuguang Xiang. Estimating heat capacities of liquid organic compounds based on elements and chemical bonds contribution [J]. Chinese Journal of Chemical Engineering, 2023, 57(5): 30-38.
[13]	Xiongzhuo Zhu, Dali Gao, Chong Yang, Chunjie Yang. A blast furnace fault monitoring algorithm with low false alarm rate: Ensemble of greedy dynamic principal component analysis-Gaussian mixture model [J]. Chinese Journal of Chemical Engineering, 2023, 57(5): 151-161.
[14]	Pengbao Lian, Lizhen Chen, Dan He, Guangyuan Zhang, Zishuai Xu, Jianlong Wang. Crystallization thermodynamics of 2,4(5)-dinitroimidazole in binary solvents [J]. Chinese Journal of Chemical Engineering, 2023, 57(5): 173-182.
[15]	Haoshan Duan, Xi Meng, Jian Tang, Junfei Qiao. Prediction of NO_x concentration using modular long short-term memory neural network for municipal solid waste incineration [J]. Chinese Journal of Chemical Engineering, 2023, 56(4): 46-57.