Alex, J., Benedetti, L., Copp, J., Gernaey,K.V., Jeppsson, U., Nopens, I., Rieger, M. N. L., Rosen, C., Steyer, J. P.,Vanrolleghem, P. and Winkler, S. (2008). Benchmark simulation model no. 1. Technical report. Department of industrial electrical engineering and automation. Lund University.
Alvares, J. (2000). Nonlinear state estimation with robust convergence. J. Process Control, 10; 59-71.
Astrom, K. J. and Hagglund, T. (1995). PID controllers. 2nd edition. Research Triangle Park. USA.
Chachuat, B., Roche, N. and Latiffi, M. A. (2001). Dynamic optimization of small size wastewater treatment plant including nitrification and denitrification processes. Computer and Chemical Engineering, 25; 585-593.
Copp, I. B. (2002). The cost simulation benchmark: description and simulator manual (COST Action 624 and Action 682). Office for official publication of the European union. Luxembourg.
Francisco, M., Vega, P. and Revollar, S. (2011). Model predictive control of BSM1 benchmark of wastewater treatment processes: a tuning procedure. 50th IEEE conference on decision and control and European control conference. Orlando. FL.
Grune, L. and Pannek, J. (2010). Nonlinear model predictive control: Theory and Algorithms. 2nd edition. Springer.
Hahn, J. and Edgar, T. F. (2002). An improved method for nonlinear model reduction using balancing of empirical Grampians. Computer and Chemical Engineering, 26; 1379-1397.
Han, H. G., Wu, X. L. and Qiao, J. F. (2014). Nonlinear model predictive control for industrial process: An application to wastewater treatment process. IEEE Trans. Ind. Electron, 61; 1970-1980.
Hasanlou, H., Torabian, A., Mehrdadi, N., Kosari, A. R. and Aminzadeh, A. R. (2018). Determination of the Most Effective Process Control Parameters of Activated Sludge and Investigating the Process Performance in Unconventional Loading Using Hybrid Numerical Solution of BSM1. J Health Syst. Res., 14(3); 347-55.
Henze, M., Gujer, W., Mino, T. and Loesdrecht, M. V. (2000). Activated sludge models ASM1, ASM2, ASM2d and ASM3. Technical report. London. UK: IWA Publishing.
Jeppsson, U. and Olsson, G. (1993). Reduced order models for on-line parameters identification of the activated sludge process. Water Science and Technology, 28; 173-183
Julien, S., Babary, J. P. and Lessard, J. P. (1998). Theoretical and practical identifiability of a reduced order model in an activated sludge process doing nitrification and denitrification. Water Science and Technology, 37; 309-316.
Lee, T. T., Wang, F. Y. and Newell, R. B. (2002). Robust model-order reduction of complex biological processes. Journal of Process Control, 12; 807-821.
Lopez, T. (2000). Estimation y control no linear de reactors de copolimerizacion. Ph. D. thesis. Universidad Autonoma Metropolitana, Mexico.
Mulas, M. (2005). Modeling and control of activated sludge processes. Ph. D thesis. Universita DEGLI STUDI DI CAGLIARI. Spain.
Muller, M. A., Angeli, D. and Allgower, F. (2014). On the performance of Economic model predictive control with self-tuning terminal cost. J. Process Control, 24; 1179−1186.
Olsson, G. and Newell, B. (2002). Wastewater treatment systems- modelling. Diagnosis and control. IWA Publishing.
Olsson, G., Nielsen, M. K., Yuan, Z., Lyngaard-Jensen, A. and Steyer, J. P. (2005). Instrumentation, control and automation in wastewater systems. Technical Report 15. IWA Publishing.
Qin, S. J. and Badgwell, T. A. (2003). A survey of industrial model predictive control technology. Control Engineering Practice, 11; 733-764.
Hasanlou, H., et al.
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Revollar, S., Vega, P., Vilanova, R. and Francisco, M. (2017). Optimal Control of Wastewater Treatment Plants Using Economic-Oriented Model Predictive Dynamic Strategies. J. Appl. Sci. 2017, 7; 813
Rossiter, J. A. (2003). Model-Based Predictive Control: A Practical Approach. CRC Press.
Santin, I., Pedret, C. and Vilanova, R. (2015). Applying variable dissolved oxygen set point in a two level hierarchical control structure to a wastewater treatment process. J. Process Control, 28; 40-55.
Santin, I., Pedret, C. and Vilanova, R. (2015). Fuzzy control and model predictive control configurations for effluent violations removal in wastewater treatment plants. Ind. Eng. Chem. Res., 51; 2763-2775.
Santin, I., Pedret, C., Vilanova, R. and Meneses. M. (2015). Removing violations of the effluent pollution in a wastewater treatment process. Chem. Eng. J.; 207-218.
Shen, W., Chen, X. Q. and Corriou, J. P. (2008). Application of model predictive control to the BSM1 benchmark of wastewater treatment process. Comput. Chem. Eng., 32; 2849-2856.
Shen, W., Chen, X., Pons, M. and Corriou, J. (2009). Model predictive control for wastewater treatment process with feed forward compensation. Chem. Eng. J., 155; 161-174.
Smets, I. Y., Haegebaert, V. J., Carrette, P. and Van Impe, J. F. (2003). Linearization of the activated sludge model ASM1 for fast and reliable prediction. Water Research, 37; 1831-1851
Takacs, I., Patrys, G. G. and Nolasco, D. (1991). A dynamic model of the clarification thickening process. Water Res., 25(10); 1263-1271.
Weijers, S. (2000). Modeling, identification and control of activated sludge plants for nitrogen removal. Ph.D. thesis. Technische Universitit Eindhoven, The Netherlands.
Zeng, J. and Liu, J. (2015). Economic model predictive control for waste water treatment processes. Ind. Eng. Chem. Res., 54; 5710-5721.