[1] S. Bolisetty, M. Peydayesh, R. Mezzenga, Sustainable technologies for water purification from heavy metals:review and analysis, Chem. Soc. Rev. 48(2) (2019) 463-487. [2] J.L. Simon, The Economics of Population Growth, Princeton University Press, USA, 2019. [3] A. Iratni, N.-B. Chang, Advances in control technologies for wastewater treatment processes:status, challenges, and perspectives, IEEE/CAA Journal of Automatica Sinica 6(2) (2019) 337-363. [4] R. Boiocchi, K.V. Gernaey, G. Sin, Systematic design of membership functions for fuzzy-logic control:A case study on one-stage partial nitritation/anammox treatment systems, Water Res. 102(2016) 346-361. [5] J. Qiao, W. Zhang, H. Han, Self-organizing fuzzy control for dissolved oxygen concentration using fuzzy neural network 1, J. Intell. Fuzzy Syst. 30(6) (2016) 3411-3422. [6] M. Sadeghassadi, C.J. Macnab, B. Gopaluni, D. Westwick, Application of neural networks for optimal-setpoint design and mpc control in biological wastewater treatment, Comput. Chem. Eng. 115(2018) 150-160. [7] G. Harja, I. Nascu, C. Muresan, I. Nascu, Improvements in dissolved oxygen control of an activated sludge wastewater treatment process, Circuits, Systems, and Signal Processing 35(6) (2016) 2259-2281. [8] K.B. Newhart, R.W. Holloway, A.S. Hering, T.Y. Cath, Data-driven performance analyses of wastewater treatment plants:A review, Water Res. 157(15) (2019) 498-513. [9] C. Foscoliano, S.D. Vigo, M. Mulas, S. Tronci, Predictive control of an activated sludge process for long term operation, Chem. Eng. J. 304(2016) 1031-1044. [10] W.H. Shen, X.Q. Chen, M.N. Pons, J.P. Corriou, Model predictive control for wastewater treatment process with feedforward compensation, Chem. Eng. J. 155(1) (2009) 161-174. [11] H.G. Han, H.H. Qian, J.F. Qiao, Nonlinear multiobjective model-predictive control scheme for wastewater treatment process, J. Process Control 24(3) (2014) 47-59. [12] X.Y. Geng, L.D. Wei, L. Shu, Model predictive control-status and challenges, Acta Automat. Sin. 39(3) (2013) 222-236. [13] S. Syafiie, F. Tadeo, E. Martinez, T. Alvarez, Model-free control based on reinforcement learning for a wastewater treatment problem, Appl. Soft Comput. 11(1) (2011) 73-82. [14] E. Kayacan, E. Kayacan, H. Ramon, W. Saeys, Adaptive neuro-fuzzy control of a spherical rolling robot using sliding-mode-control-theory-based online learning algorithm, IEEE Transactions on Cybernetics 43(1) (2013) 170-179. [15] H. Han, X. Wu, J. Qiao, A self-organizing sliding-mode controller for wastewater treatment processes, IEEE Trans. Control Syst. Technol. 27(4) (2019) 1480-1491. [16] C. Muñoz, H. Young, C. Antileo, C. Bornhardt, Sliding mode control of dissolved oxygen in an integrated nitrogen removal process in a sequencing batch reactor (sbr), Water Sci. Technol. 60(10) (2009) 2545-2553. [17] J. Han, Auto-disturbances-rejection controller and its applications, Control and Decision 13(1998) 19-23. [18] J. Han, From pid to active disturbance rejection control, IEEE Trans. Ind. Electron. 56(3) (2009) 900-906. [19] Z. Gao, Scaling and Bandwidth-parameterization Based on Control Tuning, in:Proceedings of the 2003 American Control Conference, Denver, 2003. [20] W. Wei, P.F. Xia, W.C. Xue, M. Zuo, On the disturbance rejection of a piezoelectric driven nanopositioning system, IEEE Access 8(2020) 74771-74781. [21] Y. Cheng, Z. Chen, M. Sun, Q. Sun, Cascade active disturbance rejection control of a high-purity distillation column with measurement noise, Ind. Eng. Chem. Res. 57(13) (2018) 4623-4631. [22] W. Wei, W.C. Xue, D.H. Li, On Disturbance Rejection in Magnetic Levitation, Control. Eng. Pract. 82(2019) 24-35. [23] X. Wang, F. Wang, W. Wei, Linear active disturbance rejection control of dissolved oxygen concentration based on benchmark simulation model number 1, Math. Probl. Eng. 2015(178953) (2015) 1-9. [24] R. Madoński, P. Herman, Survey on methods of increasing the efficiency of extended state disturbance observers, ISA Trans. 56(2015) 18-27. [25] J.F. Qiao, Y. Hou, H.G. Han, Optimal control for wastewater treatment process based on an adaptive multi-objective differential evolution algorithm, Neural Comput. & Applic. 31(7) (2019) 2537-2550. [26] J. Ruan, C. Zhang, Y. Li, P. Li, Z. Yang, X. Chen, M. Huang, T. Zhang, Improving the efficiency of dissolved oxygen control using an on-line control system based on a genetic algorithm evolving fwnn software sensor, J. Environ. Manag. 187(2017) 550-559. [27] Y. Jiang, Q. Sun, X. Zhang, Z. Chen, Pressure regulation for oxygen mask based on active disturbance rejection control, IEEE Trans. Ind. Electron. 64(8) (2017) 6402-6411. [28] J. Alex, L. Benedetti, J. Copp, K. Gernaey, U. Jeppsson, I. Nopens, M. Pons, L. Rieger, C. Rosen, J. Steyer, et al., Benchmark simulation model no. 1(bsm1), Report by the IWA Taskgroup on Benchmarking of Control Strategies for WWTPs 2008, pp. 19-20. |