Neural Network Model Identification and Advanced Control of a Membrane Biological Reactor
DOI:
https://doi.org/10.6000/1929-6037.2013.02.04.4Keywords:
Backwash, flux, optimization, sensitivity analysis, fouling control, wastewater treatment, neuro-model predictive controlAbstract
System identification with different input-output structures, for a membrane biological reactor (MBR), was performed using artificial neural networks (ANN) black-box modeling. The ANN models were able to capture the dynamic flux experimental literature data. Sensitivity analyses were applied on the ANN models to quantify the effects of variation in the process inputs (backwash pressure, vacuum pressure, backwash and vacuum time) on the process output (flux rate. Sensitivity analysis was applied on the developed NN in order to find the optimum backwash scheduling. The maximum flux was attained at around 165 (L/m2·day) that corresponded to an optimum vacuum-to-backwash time ratio of 10 minutes vacuum to 2 minutes backwash. Advanced control strategy using neuro-model predictive control (NN-MPC) methodology was applied to control the MBR system. The NN-MPC parameters were tuned to attain an optimum performance. The NN-MPC was efficient in tracking the flux set-point changes by adjusting vacuum-to-backwash time ratio within the operation constraints.
References
Kim MJ, Sankararao B, Yoo CK. Determination of MBR fouling and chemical cleaning interval using statistical methods applied on dynamic index data. J Membr Sci 2011; 375: 345-53. http://dx.doi.org/10.1016/j.memsci.2011.04.001 DOI: https://doi.org/10.1016/j.memsci.2011.04.001
Pierre LC, Vicki C, Tony A. Fouling in membrane bioreactors used in wastewater treatment. J Membr Sci 2006; 284: 17-26. http://dx.doi.org/10.1016/j.memsci.2006.08.019 DOI: https://doi.org/10.1016/j.memsci.2006.08.019
Chang IS, Clech PL, Jeffersom B, Judd S. Membrane fouling in membrane bioreactors for wastewater treatment. J Environ Technol 2002; 11: 1018-29. DOI: https://doi.org/10.1061/(ASCE)0733-9372(2002)128:11(1018)
Huyskens C, Brauns E, Van HE, Wever DH. A new method for the evaluation of the reversible and irreversible fouling propensity of MBR mixed liquor. J Membr Sci 2008; 323: 185-94. http://dx.doi.org/10.1016/j.memsci.2008.06.021 DOI: https://doi.org/10.1016/j.memsci.2008.06.021
Liu QF, Kim SH. Evaluation of membrane fouling models based on benchscale experiments: a comparison between constant flow rate blocking laws and artificial neural network (ANNs) model. J Membr Sci 2008; 310: 393-401. http://dx.doi.org/10.1016/j.memsci.2007.11.020 DOI: https://doi.org/10.1016/j.memsci.2007.11.020
Vargas A, Moreno-Andrade I, Buitro G. Controlled backwashing in a membrane sequencing batch reactor used for toxic wastewater treatment. J Membr Sci 2008; 320: 185-90. http://dx.doi.org/10.1016/j.memsci.2008.03.073 DOI: https://doi.org/10.1016/j.memsci.2008.03.073
Smith PJ, Vigneswaran S, Ngo HH, Ben-Aim R, Nguyen H. A new approach to backwash initiation in membrane systems. J Membr Sci 2006; 278: 381-89. http://dx.doi.org/10.1016/j.memsci.2005.11.024 DOI: https://doi.org/10.1016/j.memsci.2005.11.024
Rahmanian B, Pakizeh M, Esfandyari M, Heshmatnezhad F, Maskooki A. Fuzzy modeling and simulation for lead removal using micellar-enhanced ultrafiltration (MEUF). J Hazardous Mater 2011; 192: 585-92. http://dx.doi.org/10.1016/j.jhazmat.2011.05.051 DOI: https://doi.org/10.1016/j.jhazmat.2011.05.051
Busch J, Marquardt W. Model-based control of MF/UF filtration processes: pilot plant implementation and results. Water Sci Technol 2009; 59(9): 1713-20. http://dx.doi.org/10.2166/wst.2009.648 DOI: https://doi.org/10.2166/wst.2009.648
Bowen W, Jones MG, Welfoot JS, Yousef H. Predicting salt rejections at nanofiltration membranes using artificial neural networks. Desalination 2000; 129(2): 147-62. http://dx.doi.org/10.1016/S0011-9164(00)00057-6 DOI: https://doi.org/10.1016/S0011-9164(00)00057-6
Rostamizadeh M, Rizi SM. Predicting gas flux in silicalite-1 zeolite membrane using artificial neural networks. J Membr Sci 2012; 403: 146-51. http://dx.doi.org/10.1016/j.memsci.2012.02.036 DOI: https://doi.org/10.1016/j.memsci.2012.02.036
Abbas A, Al-Bastaki N. Modeling of an RO water desalination unit using neural networks. Chem Eng J 2005; 114: 139-43. http://dx.doi.org/10.1016/j.cej.2005.07.016 DOI: https://doi.org/10.1016/j.cej.2005.07.016
Aydiner C, Demir I, Keskinler B, Lnce O. Joint analysis of transient flux behaviors via membrane fouling in hybrid PAC/MF processes using neural network. Desalination 2009; 250: 188-96. http://dx.doi.org/10.1016/j.desal.2009.06.025 DOI: https://doi.org/10.1016/j.desal.2009.06.025
Yu DL, Gomm JB. Implementation of neural network predictive control to a multivariable chemical reactor. Control Eng Pract 2003; 11: 1315-23. http://dx.doi.org/10.1016/S0967-0661(02)00258-7 DOI: https://doi.org/10.1016/S0967-0661(02)00258-7
Aidan A, Abdel-Jabbar N, Ibrahim T, Nenov V, Mjalli F. Neural network modeling and optimization of scheduling backwash for membrane bioreactor. Clean Technol Environ Policy 2007; 10(4): 389-95. http://dx.doi.org/10.1007/s10098-007-0129-0 DOI: https://doi.org/10.1007/s10098-007-0129-0
Comas J, Meabe E, Sancho L, Ferrero G, Sipma J, Monclus H, Rodriguez-Roda I. Knowledge-based system for automatic MBR control. Water Sci Technol 2010; 62(12): 2829-36. http://dx.doi.org/10.2166/wst.2010.693 DOI: https://doi.org/10.2166/wst.2010.693
Fatone F, Battistoni P, Bolzonella D, Pavan P, Checchi F. Long-term experience with an automatic process control for nitrogen removal in membrane bioreactors. Desalination 2008; 227: 72-84. http://dx.doi.org/10.1016/j.desal.2007.05.036 DOI: https://doi.org/10.1016/j.desal.2007.05.036
Ferrero G, Monclu SH, Buttiglieri G, Comas J, Rodriguez-Roda I. Automatic control system for energy optimization in membrane bioreactors. Desalination 2011; 268: 276-80. http://dx.doi.org/10.1016/j.desal.2010.10.024 DOI: https://doi.org/10.1016/j.desal.2010.10.024
Ferrero G, Monclu SH, Buttiglieri G, Comas J, Rodriguez-Roda I. Development of a control algorithm for air scour reduction in membrane bioreactors for wastewater treatment. J Chem Technol Biotechnol 2011; 86(6): 784-89. http://dx.doi.org/10.1002/jctb.2587 DOI: https://doi.org/10.1002/jctb.2587
Ferrero G, Monclu SH, Buttiglieri G, Comas J, Rodriguez-Roda I. A knowledge-based control system for air scour optimization in membrane bioreactors. Water Sci Technol 2011; 63(9): 2025-31. http://dx.doi.org/10.2166/wst.2011.455 DOI: https://doi.org/10.2166/wst.2011.455
Huyskens C, Brauns E, Van Hoof E, Diels E, De Wever H. Validation of a supervisory control system for energy saving in membrane bioreactors. Water Res 2011; 45: 1443-53. http://dx.doi.org/10.1016/j.watres.2010.11.001 DOI: https://doi.org/10.1016/j.watres.2010.11.001
Madyastha VK, Prasad V, Mahendraker V. Reduced order model monitoring and control of a membrane bioreactor system via delayed measurements. Water Sci Technol 2011; 64(8): 1675-84. http://dx.doi.org/10.2166/wst.2011.437 DOI: https://doi.org/10.2166/wst.2011.437
Maere T, Verrecht B, Moerenhout S, Judd S, Nopens I. BSM-MBR: a benchmark simulation model to compare control and operational strategies for membrane bioreactors. Water Res 2011; 45(6): 2181-90. http://dx.doi.org/10.1016/j.watres.2011.01.006 DOI: https://doi.org/10.1016/j.watres.2011.01.006
Seborg DE, Edgar TF, Mellichamp DA, Doyle FJ. Process Dynamics and Control.3rd edition, John Wiley & Sons, New York 2010.
Bequette BW. Process Control: Modeling, Design and Simulation. Prentice Hall, New Jersey, USA 2003.
Shilnikov LP, Shilnikov AL, Turaev DV, Chua LO. Methods of qualitative theory in nonlinear dynamics. World Scientific 1998. http://dx.doi.org/10.1142/9789812798596 DOI: https://doi.org/10.1142/9789812798596
Galluzzo M, Cosenza B. Control of the biodegradation of mixed wastes in a continuous bioreactor by type-2 fuzzy logic controller. Comp Chem Eng 2008; 33: 1475-83. http://dx.doi.org/10.1016/j.compchemeng.2009.04.003 DOI: https://doi.org/10.1016/j.compchemeng.2009.04.003
Qin SJ, Badgwell TA. An overview of industrial model predictive control technology. Proc. Fifth International Conference on Chemical Process Control, AIChE/CACHE 1997.
Alnaizy R, Abdel Jabbar N, Aidan A, Abachi N. Modeling and dynamic analysis of a membrane bioreactor with backwash scheduling. Desalination Water Treat 2012; 41: 186-94. http://dx.doi.org/10.1080/19443994.2012.664713 DOI: https://doi.org/10.1080/19443994.2012.664713
MATLAB NN Predictive Control, Control Systems: Neural Network Toolbox, Natick, MA, USA 2012.
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