A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 10
Issue 1
IEEE/CAA Journal of Automatica Sinica
| Citation: | P. H. Du, W. M. Zhong, X. Peng, L. L. Li, and Z. Li, “Data-driven fault compensation tracking control for coupled wastewater treatment process,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 1, pp. 294–297, Jan. 2023. doi: 10.1109/JAS.2023.123054
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Š. Kozák, “State-of-the-art in control engineering,” J. Electrical Systems and Information Technology, vol. 1, no. 1, pp. 1–9, 2014. doi: 10.1016/j.jesit.2014.03.002
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A. Iratni and N. Chang, “Advances in control technologies for wastewater treatment processes: Status, challenges, and perspectives,” IEEE/CAA J,Autom,Sinica, vol. 6, no. 2, pp. 337–363, 2019. doi: 10.1109/JAS.2019.1911372
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H. Han, Z. Liu, Y. Hou, and J. Qiao, “Data-driven multiobjective predictive control for wastewater treatment process,” IEEE Trans. Industrial Informatics, vol. 16, no. 4, pp. 2767–2775, 2019.
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D. Wang, M. Ha, and J. Qiao, “Data-driven iterative adaptive critic control toward an urban wastewater treatment plant,” IEEE Trans. Industrial Electronics, vol. 68, no. 8, pp. 7362–7369, 2020.
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D.-J. Choi and H. Park, “A hybrid artificial neural network as a software sensor for optimal control of a wastewater treatment process,” Water Research, vol. 35, no. 16, pp. 3959–3967, 2001. doi: 10.1016/S0043-1354(01)00134-8
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H. Honggui, L. Ying, and Q. Junfei, “A fuzzy neural network approach for online fault detection in waste water treatment process,” Computers &Electrical Engineering, vol. 40, no. 7, pp. 2216–2226, 2014.
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C. A. C. Belchior, R. A. M. Araújo, F. A. A. Souza, and J. A. C. Landeck, “Sensor-fault tolerance in a wastewater treatment plant by means of anfis-based soft sensor and control reconfiguration,” Neural Computing and Applications, vol. 30, no. 10, pp. 3265–3276, 2018. doi: 10.1007/s00521-017-2901-3
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I. Santín, M. Barbu, C. Pedret, and R. Vilanova, “Dissolved oxygen control in biological wastewater treatments with non-ideal sensors and actuators,” Industrial &Engineering Chemistry Research, vol. 58, no. 45, pp. 20 39–20 654, 2019.
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D. Wang, M. Zhao, M. Ha, and J. Ren, “Neural optimal tracking control of constrained nonaffine systems with a wastewater treatment application,” Neural Networks, vol. 143, pp. 121–132, 2021. doi: 10.1016/j.neunet.2021.05.027
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P. Du, X. Peng, Z. Li, L. Li, and W. Zhong, “Performance-guaranteed adaptive self-healing control for wastewater treatment processes,” J. Process Control, vol. 116, pp. 147–158, 2022. doi: 10.1016/j.jprocont.2022.06.004
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Q. Yang, W. Cao, W. Meng, and J. Si, “Reinforcement-learning-based tracking control of waste water treatment process under realistic system conditions and control performance requirements,” IEEE Trans. Systems,Man,and Cybernetics: System, vol. 52, no. 8, pp. 5284–5294, 2022. doi: 10.1109/TSMC.2021.3122802
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J. Alex, L. Benedetti, J. B. Copp, K. V. Garnaey, U. Jeppsson, I. Nopens, M. N. Pons, L. Rieger, C. Rosen, J. P. Steyer, P. Vanrolleghem, and S. Winkler, “Benchmark simulation model 1,” IWA Taskgroup on Benchmarking of Control Strategies for WWTPs, pp. 19–20, 2008.
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B. Holenda, E. Domokos, A. Redey, 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, 2008.
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C. Foscoliano, S. Del Vigo, M. Mulas, and S. Tronci, “Predictive control of an activated sludge process for long term operation,” Chemical Engineering J., vol. 304, pp. 1031–1044, 2016. doi: 10.1016/j.cej.2016.07.018
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