[1] Niu Dan, Chen Xisong, Yang JunZhou Xingpeng,. Composite control for coagulation processwith time delay and disturbances [J]. Journal of Southeast University (English Edition), 2016, 32 (3): 285-292. [doi:10.3969/j.issn.1003-7985.2016.03.005]
Copy
Composite control for coagulation processwith time delay and disturbances(
)
)Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]
- Volumn:
- 32
- Issue:
- 2016 3
- Page:
- 285-292
- Research Field:
- Automation
- Publishing date:
- 2016-09-20
Info
- Title:
- Composite control for coagulation processwith time delay and disturbances
- Author(s):
- Niu Dan; Chen Xisong; Yang JunZhou Xingpeng
- School of Automation, Southeast University, Nanjing 210096, China
Key Laboratory of Measurement and Control of Complex System Engineering of Ministry of Education, Southeast University, Nanjing 210096, China
- PACS:
- TP273
- DOI:
- 10.3969/j.issn.1003-7985.2016.03.005
- Abstract:
- A composite control scheme consisting of model predictive control(MPC)and disturbance observer(DOB)is proposed to solve the control performance degradation problem of the turbidity of the treated water in the presence of significant changes in raw water quality, water flow rate and internal model mismatch disturbances. The MPC is employed as a feedback controller for the coagulation process with a large time delay. The DOB is adopted to estimate the severe disturbances in the turbidity control, such as large changes in raw water quality and water flow rate. The estimated values are applied for feed-forward compensation to reject disturbances. Finally, the disturbance rejection performances for step disturbances and time-varying disturbances in the nominal case and model mismatch case are tested. The simulation results illustrate that, compared with the MPC method, the proposed method can significantly improve the disturbance rejection performance in the turbidity control of the treated water, no matter if in the presence of external disturbances or internal model mismatch disturbances.
References:
[1] Wu G D, Lo S L. Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system [J]. Engineering Applications of Artificial Intelligence, 2008, 21(8): 1189-1195.
[2] Maier H R, Morgan N, Chow C W K. Use of artificial neural networks for predicting optimal alum doses and treated water quality parameters [J]. Environmental Modelling & Software, 2004, 19(5): 485-494. DOI:10.1016/s1364-8152(03)00163-4.
[3] Sanchez N P, Skeriotis A T, Miller C M. A PARAFAC-based long-term assessment of DOM in a multi-coagulant drinking water treatment scheme [J]. Environmental Science & Technology, 2014, 48(3): 1582-1591. DOI:10.1021/es4049384.
[4] Li Z F, Zou Z Y, Luo Y H, et al. Intelligent control of turbidity before filtration in waterworks [J]. Water Purification Technology, 2009, 28(4): 64-67.
[5] Akbarizadeh M, Daghbandan A, Yaghoobi M. Modeling and optimization of poly electrolyte dosage in water treatment process by GMDH type-NN and MOGA [J]. International Journal of Chemoinformatics and Chemical Engineering, 2013, 3(2): 94-106.
[6] Griffiths K A, Andrews R C. The application of artificial neural networks for the optimization of coagulant dosage [J]. Water Science and Technology: Water Supply, 2011, 11(5): 605-611.
[7] Lamrini B, Benhammou A, Le Lann M V, et al. A neural software sensor for online prediction of coagulant dosage in a drinking water treatment plant [J]. Transactions of the Institute of Measurement and Control, 2005, 27(3): 195-213. DOI:10.1191/0142331205tm141oa.
[8] Heddam S, Bermad A, Dechemi N. ANFIS-based modelling for coagulant dosage in drinking water treatment plant: A case study [J]. Environmental Monitoring and Assessment, 2012, 184(4): 1953-1971. DOI:10.1007/s10661-011-2091-x.
[9] Apostol G, Kouachi R, Constantinescu I. Optimization of coagulation-flocculation process with aluminum sulfate based on response surface methodology [J]. UPB Buletin Stiintific, Series B: Chemistry and Materials Science, 2011, 73(2): 77-84.
[10] Wei A I, Zhu X F. Research on VRFT-based IMC-PID method and simulation of turbidity control in water supply plant [J]. Control and Instruments in Chemical Industry, 2011, 2: 141-144.(in Chinese)
[11] Heddam S, Bermad A, Dechemi N. ANFIS-based modelling for coagulant dosage in drinking water treatment plant: A case study [J]. Environmental Monitoring and Assessment, 2012, 184(4): 1953-1971. DOI:10.1007/s10661-011-2091-x.
[12] van der Walt J J, Smith J N, Nel M C. Fuzzy logic production control: The vaalkop water treatment plant case study [C]//Biennial Conference of the Water Institute of Southern Africa. Durban, South Africa, 2002: 1-12.
[13] Robenson A, Shukor S R A, Aziz N. Development of process inverse neural network model to determine the required alum dosage at segama water treatment plant Sabah, Malaysia [J]. Computer Aided Chemical Engineering, 2009, 27: 525-530. DOI:10.1016/s1570-7946(09)70308-6.
[14] Griffiths K A, Andrews R C. The application of artificial neural networks for the optimization of coagulant dosage [J]. Water Science and Technology Water Supply, 2011, 11(5): 605-611.
[15] Heddam S, Dechemi N. A new approach based on the dynamic evolving neural-fuzzy inference system(DENFIS)for modelling coagulant dosage(Dos): Case study of water treatment plant of Algeria [J]. Desalination and Water Treatment, 2015, 53(4): 1045-1053. DOI:10.1080/19443994.2013.878669.
[16] Bello O, Hamam Y, Djouani K. Fuzzy dynamic modelling and predictive control of a coagulation chemical dosing unit for water treatment plants [J]. Journal of Electrical Systems and Information Technology, 2014, 1(2): 129-143. DOI:10.1016/j.jesit.2014.08.001.
[17] Pekel L C, Zeybek Z, Hapoglu H, et al. Textile wastewater treatment with coagulation and GPC control [J]. Chemical Engineering, 2010, 21: 817-822.
[18] Maciejowski J M. Predictive control: With constraints [M]. London: Prentice Hall, 2001.
[19] Prakash J, Patwardhan S C, Shah S L. State estimation and nonlinear predictive control of autonomous hybrid system using derivative free state estimators [J]. Journal of Process Control, 2010, 20(7): 787-799. DOI:10.1016/j.jprocont.2010.04.001.
[20] Kobayashi H, Katsura S, Ohnishi K. An analysis of parameter variations of disturbance observer for motion control [J]. IEEE Transactions on Industrial Electronics, 2007, 54(6): 3413-3421.
[21] Li S H, Yang J. Autopilot design for bank-to-turn missiles using robust state feedback control and disturbance observers [J]. IEEE Transactions on Aerospace and Electronic Systems, 2013, 49(1): 558-579. DOI:10.1109/taes.2013.6404120.
[22] Li Shihua, Yang Jun, Chen Wenhua, et al. Disturbance observer-based control: Methods and applications [M]. Boca Raton, FL, USA: CRC Press, 2014.
[23] Mo X M. Dynamic matrix control based on multi-model synthesis and its application [C]//The 26th Chinese Control Conference. Zhangjiajie, China, 2007: 167-170. DOI:10.1109/chicc.2006.4346778.(in Chinese)
[24] Zhu X F, Liu G X, Chen J, et al. Forward and feedback intelligent control of out water coming from water factory [J]. Control Engineering of China, 2010, 17(3): 290-296.(in Chinese)
Memo
- Memo:
-
Biographies: Niu Dan(1986—), male, doctor; Chen Xisong(corresponding author), male, doctor, professor, chenxisong@263.net.
Foundation items: The National Natural Science Foundation of China(No.61504027), the Natural Science Foundation of Jiangsu Province(No.BK20140647), the Priority Academic Program Development of Jiangsu Higher Education Institutions.
Citation: Niu Dan, Chen Xisong, Yang Jun, et al. Composite control for coagulation process with time delay and disturbances[J].Journal of Southeast University(English Edition), 2016, 32(3):285-292.DOI:10.3969/j.issn.1003-7985.2016.03.005.