Identification of water treatment plant based on feedforward neural network

• Autorzy: Mohamed A. F. , Radwa H. Z.
• Języki publikacji: EN

Abstrakty

EN

Coagulation process is the main process in conventional water treatment process sequence. It influences the following treatment process aspects: maintaining plant efficiency and increasing the quality of the produced water. This is accomplished by adding chemicals to raw water, such as alum sulphate. To secure the appropriate plant performance, a mathematical model is proposed in this paper for the coagulation unit, followed by the development of the control strategy. Classic PID and neural network based controller regulating the process are used. Tests were performed, based on the real data for water treatment, using MATLAB/SIMULINK. Simulation results showed better values for both settling time and overshoot in the case of using neural network based controller than PID.

Słowa kluczowe

EN

water treatment plant; coagulation process; PID; neural network control

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2017
• Tom: Vol. 46, no. 3
• Strony: 247--258
• Opis fizyczny: Bibliogr. 18 poz., rys.

Twórcy

autor

Mohamed A. F.

• Department of Industrial Electronic and Control Engineering, Faculty of Electronic Engineering, Menofia University, Egypt

autor

Radwa H. Z.

• Faculty of Electronic Engineering, Menofia University, Egypt

Bibliografia

• [1] Anuradha, D. B., Reddy, G. P., and Murthy, J. S. N. (2009) Direct Inverse Neural Network Control of A Continuous Stirred Tank Reactor (CSTR). Proceedings of the International Multi Conference of Engineers and Computer Scientists (IMECS 2009), II, 1–5.
• [2] Apostol, G., Kouachi, R., and Constantinescu, I. (2011) Optimization of Coagulation-Flocculation Process With Aluminum Sulfate Based on Response Surface Methodology. University Polytehnica of Bucharest Scientific Bulletin, Series B: Chemistry and Materials Science, 73(2), 77– 84.
• [3] Bello, O., Hamam, Y. and Djouani, K. (2013) Dynamic modelling and system identification of coagulant dosage system for water treatment plants. In: 3rd International Conference on Systems and Control, 146–152. IEEE. doi:10.1109/ICoSC.2013.6750850.
• [4] Bello, O., Hamam, Y. and Djouani, K. (2014) Modelling and validation of a coagulation chemical dosing unit for water treatment plants. In: 2014 IEEE Conference on Control Applications (CCA), 765–771. doi:10.1109/ CCA.2014.6981433.
• [5] Clesceri, L. S., Greenberg, A. E. and Eaton, A. D. (1998) Standard Methods for the Examination of Water and Wastewater, 20th Edition. APHA American Public Health Association. https://books.google.com.eg/ books?id=2BcoYAAACAAJ.
• [6] Cordoba, G. A. C., Tuhovˇ c´ak, L. and Tauˇs, M. (2014) Using Artificial Neural Network Models to Assess Water Quality in Water Distribution Networks. Procedia Engineering, 70, 399–408. doi:10.1016/j.proeng.2014. 02.045.
• [7] Engelhardt, T. (2010) Coagulation, Flocculation and Clarification of Drinking Water. Drinking water sector, Hach Company.
• [8] Ghernaout, D. (2015) Controlling Coagulation Process: From Zeta Potential to Streaming Potential. American Journal of Environmental Protection, 4(5), 16. doi:10.11648/j.ajeps.s.2015040501.12
• [9] Ghutke, P. C. (2015) Performance Analysis of Neural Network Based Single Neuron Control and Anfis Control for CSTR. International Journal for Innovative Research in Science & Technology, 1(8), 216–222. http://www. ijirst.org/articles/IJIRSTV1I8060.pdf.
• [10] Heddam, S., Bermad, A. and Dechemi, N. (2012) ANFIS-based modelling for coagulant dosage in drinking water treatment plant: a case study. Environmental Monitoring and Assessment, 184(4), 1953–1971. doi:10.1007/s10661-011-2091-x.
• [11] Jiang, Z. (2008) A Neural Network Controller for Trajectory Control of Industrial Robot Manipulators. Journal of Computers, 3(8), 1–8.
• [12] Kumar, J. S., Poongodi, P. and Balakumaran, P. (2013) Artificial Intelligence Based Alum Dosage Control in Water Treatment Plant. The International Journal of Engineering and Technology, 5(4), 3344–3350.
• [13] Olanrewaju, R., Muyibi, S. A., Salawudeen, T. O. and Aibinu, A. M. (2012) An intelligent modeling of coagulant dosing system for water treatment plants based on artificial neural network. Australian Journal of Basic and Applied Sciences, 93–99.
• [14] Rangeti, I. (2014) Determinants of Key Drivers for Potable Water Treatment Cost in uMngeni Basin. Durban University of Technology. Retrieved from https://ir.dut.ac.za/bitstream/handle/10321/1251/RANGETI 2014.pdf? sequence=1&isAllowed=y.
• [15] Tomperi, J., Pelo, M. and Leivisk¨a, K. (2013) Predicting the residual aluminum level in water treatment process. Drinking Water Engineering and Science, 6(1), 39–46. doi:10.5194/dwes-6-39-2013.
• [16] Vasickaninov´a, A., Bakoˇsov´a, M., M´esz´aros, A. and Klemeˇs, J. J. (2011) Neural network predictive control of a heat exchanger. Applied Thermal Engineering, 31(13), 2094–2100. doi:10.1016/j.applthermaleng. 2011.01.026.
• [17] Wills, B.A. and Finch, J.A. (2015) Dewatering. In: B.A. Wills and J.A. Finch, Will’s Mineral Processing Technology, 8th edition. Elsevier, 417– 438.
• [18] Wu, G.-D. and Lo, S.-L. (2008) Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system. Engineering Applications of Artificial Intelligence, 21(8), 1189–1195. doi:10.1016/j.engappai.2008.03.015.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).