Intelligent Optimal Design of a Membrane Bioreactor Based on Flow Field Quantitative Analysis
Publication: Journal of Environmental Engineering
Volume 148, Issue 7
Abstract
The application of intelligent algorithms in the optimal design of a membrane bioreactor (MBR) is helpful in improving reactor performance. Our study constructed a numerical flow field model of a MBR. Membrane thickness, porosity, and inlet velocity were used as independent input variables. Flow field uniformity and turbulent kinetic energy were used as characteristic parameters to evaluate the flow field effect of the reactor. Based on the numerical calculation of samples, the back-propagation (BP) neural network model was used for prediction. Genetic algorithms (GA), artificial bee colony algorithms, and particle swarm optimization (PSO) algorithms were used to optimize the BP neural network. PSO–BP was screened out by error analysis as the best intelligent prediction algorithm. The function model between the reactor parameters and the flow field effect was derived by multivariate nonlinear regression analysis in combination with the results of computational fluid dynamics and PSO–BP prediction. The following optimal design parameters were determined using GA: membrane structure thickness of 45.6 mm, porosity of 76%, and inlet velocity of 2 V. A feasible intelligent optimal design method of MBR was established by combining fluid dynamics and modern intelligent algorithms to provide a new idea and method for the optimal design of an MBR.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was supported by Science Foundation of Dalian Maritime University (Grant No. 17210222), which is gratefully acknowledged.
References
Alam, T., S. Qamar, A. Dixit, and M. Benaida. 2020. “Genetic algorithm: Reviews, implementations, and applications.” Int. J. Eng. Pedagogy 10 (6): 57–77. https://doi.org/10.3991/ijep.v10i6.14567.
Bae, J., C. Shin, E. Lee, J. Kim, and P. L. McCarty. 2014. “Anaerobic treatment of low-strength wastewater: A comparison between single and staged anaerobic fluidized bed membrane bioreactors.” Bioresour. Technol. 165 (Aug): 75–80. https://doi.org/10.1016/j.biortech.2014.02.065.
Bolaji, A. L., Khader, A. T., M. A. Al-betar, and A. A. Mohammed. 2013. “Artificial bee colony algorithm, its variants and applications: A survey.” J. Theor. Appl. Inf. Technol. 47 (2): 434–459.
Brannock, M., Y. Wang, and G. Leslie. 2008. “Optimising mixing in full-scale MBRs: CFD modelling and validation CFD will prove to be a valuable design and optimisation tool.” Water 35 (2): 114–115.
Charfi, A., E. Park, M. Aslam, and J. Kim. 2018. “Particle-sparged anaerobic membrane bioreactor with fluidized polyethylene terephthalate beads for domestic wastewater treatment: Modelling approach and fouling control.” Bioresour. Technol. 258 (Jun): 263–269. https://doi.org/10.1016/j.biortech.2018.02.093.
Chen, Y., L. Shen, R. Li, X. Xu, H. Hong, H. Lin, and J. Chen. 2020. “Quantification of interfacial energies associated with membrane fouling in a membrane bioreactor by using BP and GRNN artificial neural networks.” J. Colloid Interface Sci. 565 (Apr): 1–10. https://doi.org/10.1016/j.jcis.2020.01.003.
Deng, Y., H. Xiao, J. Xu, and H. Wang. 2019. “Prediction model of PSO-BP neural network on coliform amount in special food.” Saudi J. Biol. Sci. 26 (6): 1154–1160. https://doi.org/10.1016/j.sjbs.2019.06.016.
Dou, K., and X. Sun. 2021. “Long-term weather prediction based on GA-BP neural network.” In Vol. 668 of Proc., IOP Conf. Series: Earth and Environmental Science, 012015. Shandong, China: Qingdao Univ.
Gao, J., and Y. Wang. 2013. “BP neural network prediction on heat-transfer performance of direct air-cooled condensers.” J. Chin. Soc. Power Eng. 33 (6): 443–447.
Jiao, B. B. 2020. “Nuclear binding energy predictions based on BP neural network.” Int. J. Moder. Phys. E 29 (5): 2050024. https://doi.org/10.1142/S021830132050024X.
Jin, Y., Z. Li, Y. Han, X. Li, P. Li, G. Li, and H. Wang. 2021. “A research on line loss calculation based on BP neural network with genetic algorithm optimization.” In Vol. 675 of Proc., IOP Conf. Series: Earth and Environmental Science, 012155. Shenyang, China: Northeastern Univ.
Karaboga, D., and B. Basturk. 2008. “On the performance of artificial bee colony (ABC) algorithm.” Appl. Soft Comput. 8 (1): 687–697. https://doi.org/10.1016/j.asoc.2007.05.007.
Li, H., Y. Lu, C. Zheng, M. Yang, and S. Li. 2019. “Groundwater level prediction for the arid oasis of Northwest China based on the artificial bee colony algorithm and a back-propagation neural network with double hidden layers.” Water 11 (4): 860. https://doi.org/10.3390/w11040860.
Li, W., G.-G. Wang, and A. H. Gandomi. 2021. “A survey of learning-based intelligent optimization algorithms.” Arch. Comput. Methods Eng. 28 (5): 3781–3799. https://doi.org/10.1007/s11831-021-09562-1.
McCarty, P. L., J. Bae, and J. Kim. 2011. “Domestic wastewater treatment as a net energy producer—Can this be achieved?” Environ. Sci. Technol. 45 (17): 7100–7106. https://doi.org/10.1021/es2014264.
Nayak, V., H. A. Suthar, and J. Gadit. 2012. “Implementation of artificial bee colony algorithm.” IAES Int. J. Artif. Intell. 1 (3): 112–120.
Poli, R., J. Kennedy, and T. Blackwell. 2007. “Particle swarm optimization.” Swarm Intell. 1 (1): 33–57. https://doi.org/10.1007/s11721-007-0002-0.
Pyhnastyi, O. M., and O. S. Kozhyna. 2020. “Use of the complex of models of regression for analysis of the factors that determine the severity of bronchial asthma.” Int. Med. 2 (2): 107–118.
Qi, C., A. Fourie, and Q. Chen. 2018. “Neural network and particle swarm optimization for predicting the unconfined compressive strength of cemented paste backfill.” Constr. Build. Mater. 159 (Jan): 473–478. https://doi.org/10.1016/j.conbuildmat.2017.11.006.
Robles, Á., et al. 2018. “A review on anaerobic membrane bioreactors (AnMBRs) focused on modelling and control aspects.” Bioresour. Technol. 270 (Dec): 612–626. https://doi.org/10.1016/j.biortech.2018.09.049.
Settles, M. 2005. “An introduction to particle swarm optimization.” Appl. Mech. Mater. 2: 12.
Shaohui, C., Z. Jianguo, D. Aiguo, and L. Yuchun. 2011. “Principle of BP neural network and its simulation in diameter at breast height.” J. Northeast For. Univ. 39 (8): 116–119.
Shin, C., and J. Bae. 2018. “Current status of the pilot-scale anaerobic membrane bioreactor treatments of domestic wastewaters: A critical review.” Bioresour. Technol. 247 (Jan): 1038–1046. https://doi.org/10.1016/j.biortech.2017.09.002.
Stazi, V., and M. C. Tomei. 2018. “Enhancing anaerobic treatment of domestic wastewater: State of the art, innovative technologies and future perspectives.” Sci. Total Environ. 635 (Sep): 78–91. https://doi.org/10.1016/j.scitotenv.2018.04.071.
Sun, C., C. Li, Y. Liu, Z. Liu, X. Wang, and J. Tan. 2019. “Prediction method of concentricity and perpendicularity of aero engine multistage rotors based on PSO-BP neural network.” IEEE Access 7 (99): 132271–132278. https://doi.org/10.1109/ACCESS.2019.2941118.
Tu, J., Y. Liu, M. Zhou, and R. Li. 2020. “Prediction and analysis of compressive strength of recycled aggregate thermal insulation concrete based on GA-BP optimization network.” J. Eng. Des. Technol. 19 (2): 412–422.
Vukelic, D., K. Simunovic, Z. Kanovic, T. Saric, B. Tadic, and G. Simunovic. 2021. “Multi-objective optimization of steel AISI 1040 dry turning using genetic algorithm.” Neural Comput. Appl. 33 (19): 12445–12475. https://doi.org/10.1007/s00521-021-05877-z.
Wang, Y., M. Brannock, S. Cox, and G. Leslie. 2010. “CFD simulations of membrane filtration zone in a submerged hollow fibre membrane bioreactor using a porous media approach.” J. Membr. Sci. 363 (1–2): 57–66. https://doi.org/10.1016/j.memsci.2010.07.008.
Wang, Y., J. Liu, R. Li, X. Suo, and E. Lu. 2020. “Precipitation forecast of the Wujiang River Basin based on artificial bee colony algorithm and backpropagation neural network.” Alexandria Eng. J. 59 (3): 1473–1483. https://doi.org/10.1016/j.aej.2020.04.035.
Xiao, R., K. Li, L. Sun, J. Zhao, P. Xing, and H. Wang. 2020. “The prediction of liquid holdup in horizontal pipe with BP neural network.” Energy Sci. Eng. 8 (6): 2159–2168. https://doi.org/10.1002/ese3.655.
Zhao, Z., Y. Lou, Y. Chen, H. Lin, R. Li, and G. Yu. 2019. “Prediction of interfacial interactions related with membrane fouling in a membrane bioreactor based on radial basis function artificial neural network (ANN).” Bioresour. Technol. 282 (Jun): 262–268. https://doi.org/10.1016/j.biortech.2019.03.044.
Zhuang, L., B. Tang, L. Bin, P. Li, S. Huang, and F. Fu. 2021. “Performance prediction of an internal-circulation membrane bioreactor based on models comparison and data features analysis.” Biochem. Eng. J. 166 (Feb): 107850. https://doi.org/10.1016/j.bej.2020.107850.
Zuluaga, L. C., L. N. Naranjo, J. Svojitka, T. Wintgens, M. Rodríguez, and N. Ratkovich. 2015. “CFD simulation of an anaerobic membrane bioreactor (AnMBR) to treat industrial wastewater.” Revista de Ingeniería 42 (42): 23–29.
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History
Received: Nov 24, 2021
Accepted: Feb 26, 2022
Published online: May 3, 2022
Published in print: Jul 1, 2022
Discussion open until: Oct 3, 2022
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