Gradient Descent Optimization Control of an Activated Sludge Process based on Radial Basis Function Neural Network
Most systems in science and engineering can be described in the form of ordinary differential equations, but only a limited number of these equations can be solved analytically. For that reason, numerical methods have been used to get the approximate solutions of differential equations. Among these methods, the most famous is the Euler method. In this paper, a new proposed control strategy utilizing the Euler and the gradient method based on Radial Basis Function Neural Network (RBFNN) model have been used to control the activated sludge process of wastewater treatment. The aim was to maintain the Dissolved Oxygen (DO) level in the aerated tank and have the substrate concentration Chemical Oxygen Demand (COD5) within the standard limits. The simulation results of DO show the robustness of the proposed control method compared to the classical method. The proposed method can be applied in wastewater treatment systems.
J. Boer and P. Blaga, “Optimizing Production Costs by Redesigning the Treatment Process of the Industrial Waste Water,” Procedia Technology, vol. 22, pp. 419–424, Jan. 2016. DOI: https://doi.org/10.1016/j.protcy.2016.01.071
Y. Song, Y. Xie, and D. Yudianto, “Extended activated sludge model no. 1 (ASM1) for simulating biodegradation process using bacterial technology,” Water Science and Engineering, vol. 5, no. 3, pp. 278–290, Sep. 2012.
H. A. Maddah, “Numerical Analysis for the Oxidation of Phenol with TiO2 in Wastewater Photocatalytic Reactors,” Engineering, Technology & Applied Science Research, vol. 8, no. 5, pp. 3463–3469, Oct. 2018.
M. Yong, P. Yongzhen, and U. Jeppsson, “Dynamic evaluation of integrated control strategies for enhanced nitrogen removal in activated sludge processes,” Control Engineering Practice, vol. 14, no. 11, pp. 1269–1278, Nov. 2006.
R. Tzoneva, “Optimal PID control of the dissolved oxygen concentration in the wastewater treatment plant,” in AFRICON 2007, Windhoek, South Africa, Sep. 2007.
A. Traoré et al., “Fuzzy control of dissolved oxygen in a sequencing batch reactor pilot plant,” Chemical Engineering Journal, vol. 111, no. 1, pp. 13–19, Jul. 2005. DOI: https://doi.org/10.1016/j.cej.2005.05.004
C.-S. Chen, “Robust Self-Organizing Neural-Fuzzy Control With Uncertainty Observer for MIMO Nonlinear Systems,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 4, pp. 694–706, Aug. 2011. DOI: https://doi.org/10.1109/TFUZZ.2011.2136349
B. Holenda, E. Domokos, Á. Rédey, and J. Fazakas, “Dissolved oxygen control of the activated sludge wastewater treatment process using model predictive control,” Computers & Chemical Engineering, vol. 32, no. 6, pp. 1270–1278, Jun. 2008.
M. Li, L. Zhou, J. Wang, “Neural network predictive control for dissolved oxygen based on Leven berg-Marquardt algorithm,” Trans. Chin. Soc. Agric. Mach, vol. 47, pp. 297–302, 2016.
G. S. Ostace, V. M. Cristea, and P. Ş. Agachi, “Cost reduction of the wastewater treatment plant operation by MPC based on modified ASM1 with two-step nitrification/denitrification model,” Computers & Chemical Engineering, vol. 35, no. 11, pp. 2469–2479, Nov. 2011.
C. A. C. Belchior, R. A. M. Araújo, and J. A. C. Landeck, “Dissolved oxygen control of the activated sludge wastewater treatment process using stable adaptive fuzzy control,” Computers & Chemical Engineering, vol. 37, pp. 152–162, Feb. 2012. DOI: https://doi.org/10.1016/j.compchemeng.2011.09.011
Y. Han, M. A. Brdys, and R. Piotrowski, “Nonlinear PI control for dissolved oxygen tracking at wastewater treatment plant,” IFAC Proceedings Volumes, vol. 41, no. 2, pp. 13587–13592, Jan. 2008.
C. Vlad, M. I. Sbarciog, M. Barbu, and A. V. Wouwer, “Indirect Control of Substrate Concentration for a Wastewater Treatment Process by Dissolved Oxygen Tracking,” Journal of Control Engineering and Applied Informatics, vol. 14, no. 1, pp. 38-47–47, Mar. 2012.
T. Yang, W. Qiu, Y. Ma, M. Chadli, and L. Zhang, “Fuzzy model-based predictive control of dissolved oxygen in activated sludge processes,” Neurocomputing, vol. 136, pp. 88–95, Jul. 2014. DOI: https://doi.org/10.1016/j.neucom.2014.01.025
M. Bahita and K. Belarbi, “Fuzzy Adaptive Control of Dissolved Oxygen in a Waste Water Treatment Process,” IFAC-PapersOnLine, vol. 48, no. 24, pp. 66–70, Jan. 2015. DOI: https://doi.org/10.1016/j.ifacol.2015.12.058
R. Piotrowski and A. Skiba, “Nonlinear fuzzy control system for dissolved oxygen with aeration system in sequencing batch reactor,” Information Technology And Control, vol. 44, no. 2, pp. 182–195, Jun. 2015. DOI: https://doi.org/10.5755/j01.itc.44.2.7784
R. Piotrowski, K. Błaszkiewicz, and K. Duzinkiewicz, “Analysis the Parameters of the Adaptive Controller for Quality Control of Dissolved Oxygen Concentration,” Information Technology And Control, vol. 45, no. 1, pp. 42–51, Mar. 2016. DOI: https://doi.org/10.5755/j01.itc.45.1.9246
M.-J. Lin and F. Luo, “Adaptive neural control of the dissolved oxygen concentration in WWTPs based on disturbance observer,” Neurocomputing, vol. 185, pp. 133–141, Apr. 2016. DOI: https://doi.org/10.1016/j.neucom.2015.12.045
M.-J. Syu and B.-C. Chen, “Back-propagation Neural Network Adaptive Control of a Continuous Wastewater Treatment Process,” Industrial & Engineering Chemistry Research, vol. 37, no. 9, pp. 3625–3630, Sep. 1998.
C. J. B. Macnab, “Stable neural-adaptive control of activated sludge bioreactors,” presented at the 2014 American Control Conference, Portland, OR, USA, Jun. 2014, pp. 2869–2874. DOI: https://doi.org/10.1109/ACC.2014.6858627
M. Mahshidnia and A. Jafarian, “Forecasting Wastewater Treatment Results with an ANFIS Intelligent System,” Engineering, Technology & Applied Science Research, vol. 6, no. 5, pp. 1175–1181, Oct. 2016.
J. Qiao, W. Fu, and H. Han, “Dissolved oxygen control method based on self-organizing T-S fuzzy neural network,” CIESC Journal, vol. 67, pp. 960–966, Mar. 2016.
H. Hasanpour, M. H. Beni, and M. Askari, “Adaptive PID Control Based on RBF NN For Quadrotor,” International Research Journal of Applied and Basic Sciences, vol. 11, no. 2, pp. 177–186, 2017.
G. Olsson and B. Newell, Wastewater Treatment Systems: Modelling, Diagnosis and Control, vol. 4. London, UK: IWA Publishing, 2015.
H. Zhou, “Dissolved oxygen control of wastewater treatment process using self-organizing fuzzy neural network,” CIESC J. Vol. 68, pp.1516–1524, 2017.
MetricsAbstract Views: 159
PDF Downloads: 115
Copyright (c) 2020 Authors
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.