• Sridhar Natarajan Jayam College of Engineering, Dharmapuri.
  • S. Senthil kumaar Podhigai College of Engineering, Tirupattur




Lyapunov Exponents, Kolmogorov-Sinai Entropy.


This paper aims at presenting a new optimization proposal to enhance the flocculation process in Water Treatment (WT) plant using a better flash mixing, located at KELAVERAPALLY, in Krishnagiri district, Tamil Nadu, India. Further, Sludge removal is done efficiently which decreases the water wastage as well as improvement in output water quality. Though WT plants are already equipped with systematic and sequential physicochemical processes, still they need to be optimized to obtain a better treated drinking water to maintain the quality standards as prescribed by World Health Organization. Chaotic behavior in chemical systems has been used to optimize the performance of WT plant. Measurement systems implemented in WT plant yield several chaotic based measurement parameters which are used to control the system operations to maintain the target water quality.  This intelligible data extraction through the proposed measurement  systems in a short span of time improves the plant performance without adding any costly systems except few changes in the existing plant setup.  Chaotic behavior is ensured through Lyapunov Exponents and Kolmogorov-Sinai Entropies. Both, water quality improvement and water wastage reduction is achieved simultaneously in the proposed work when a dosage prediction is done using Feed Forward Neural Networks. The treatment plant investigated has a maximum capacity of 14 MLD (Million litres per day) using two parallel streams with 7 MLD each


Download data is not yet available.

Author Biography

Sridhar Natarajan, Jayam College of Engineering, Dharmapuri.

Working as Head of the Department in Civil Engineering, Jayam College of Engineering, Dharmapuri, Actively particiapating in technical developments and overall upgradation of the educational system inside the college premises.


1. Guan-De Wu & Shang-Lien Lo, “Predicting real-time coagulant dosage in water treatment by artificial neural networks and adaptive network-based fuzzy inference system”, Engineering Applications of Artificial Intelligence, pp.1189-1195.Amirtharajah, A & Mills, KM 1982, ‘Rapid Mix Design for Mechanisms of Alum Coagulation’, J. AWWA, 74(2008), 210-216.
2. Rietveld, A.W.e. van der Helm, K.M. van Schagen &.T.J. van der Aa, “Good modeling practice in drinking water, applied to Weesperkarspel plant of Watemet”. Environ. Modeling & Software, 2009, 1-9.
3. Backslapper, Th, GJ, Rietveld, LC, Babuska, R, Smaal, B &Timmer, “Integrated operation of drinking water treatment plant at Amsterdam water supply”. Water Sci. Technology.: Water Supply, 4(2004), 263-270.
4. Zodi, S, O. Potier, F. Lapicque& J.P. Leclerc, “Treatment of the industrial wastewaters by electrocoagulation: Optimization of coupled electrochemical and sedimentation processes”, Desalination, 261 (2010),186-190.
5. Casares, JJ & Rodriguez, J, “Analysis and evaluation of a wastewater treatment plant model by stochastic optimization”, Appl. Math. Modelling, 13 (July 1989), 420-424.
6. Issa, P, Iman, H & Paria, A , “Investigation on Optimization of Conventional Drinking Water Treatment Plant”, Proceedings of 2nd International Conference on Chemical, Biological and Environmental Engineering (ICBEE 2010), 304-310.
7. Tongneng, H & Peijun, C, “Prediction of water-quality based on wavelet transform using vector machine”, Proceedings of Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and Science, (2010), 76-81.
8. Tian, ZX, Jiang, JP, Guo, L & Wang P, “Anomaly detection of Municipal Wastewater Treatment Plant operation using Support Vector Machine”, International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), .518-521. DOI: 10.1049/cp.2012.1030
9. Daniel N. Rockmore, J. Byrnes (ed.), “Recent Progress And Applications In Group FFTs”, Computational Non commutative Algebra and Applications, (2004), 227-254. Available from:Kluwer Academic Publishers, Netherlands.
10. Heideman, MT.; Johnson, DH& Burrus, CS, “Gauss and the history of the fast Fourier transform”. IEEE ASSP Magazine. .1(4) (1984),14–21. doi:10.1109/MASSP.1984.1162257.
11. Johnson, SG. & Frigo, M, “A modified split-radix FFT with fewer arithmetic operations”, IEEE Trans. Signal Processing. vol.55 (1) (2007), 111–119. doi:10.1109/tsp.2006.882087.
12. Shibata, H, “KS entropy and mean Lyapunov exponent for coupled map lattices”, Physica A, vol. 292 (2001),182-192.
13. Holden, AV & Zhang, H, “Lyapunov exponent spectrum for a generalized coupled map lattice”, Chaos Solitons Fractals, vol.2 (1992), 155-164.
14. Wolf, FA, Swift, JB,Swinney, HL&Vastano, JA, “Determining Lyapunov exponents from a time series”,Physica D vol. 16 (1985), 285-317
15. Sano, M &Sawana, Y, “Measurement of the Lyapunov spectrum from a chaotic time series”, Physical Review Letters 55 (1985), 1082-1085.
16. Abarbanel, HDI, Brown, R &Kennel, MB, “Vibration of Lyapunov exponents on a strange attractor”, Journal of Nonlinear Science vol.1 (1991), 175-199.
17. Ying-Qian Zhang & Xing-Yuan Wang, “Spatiotemporal chaos in Arnold coupled logistic map lattice”, Nonlinear Analysis: Modelling and Control, vol. 18 (4) (2013), 526-541.
18. Zhang Ying-Qian & Wang Xing-Yuan, “Spatiotemporal chaos in mixed linear–nonlinear coupled logistic map lattice”, Physica A, vol. 402 (2014), 104-118.
19. Amirtharajah, A & Mills, KM, “Rapid Mix Design for Mechanisms of Alum Coagulation”, J. AWWA, vol. 74 (1982), 210-216
20. Edzwald, James K, “Coagulant mixing revisited: theory and practice”, vol.62 (2) (2013), 67-77; DOI: 10.2166/aqua.2013.142.
21. Kunihiko, Kaneko, “Overview of coupled map lattices”, Chaos 2, American Institute of Physics. (1992), 279-282. doi: 10.1063/1.165869.
22. Chua, Leon O, Motomasa, Komuro, & Takashi, Matsumoto, “The Double Scroll Family”, IEEE Transactions on Circuits And Systems, vol. CAS-33, 11 (1986), 1072-1118.
23. Alvarez-Hernandez, MM., Shinbrot, T, Zalc, J& Muzzio FJ, “Practical chaotic mixing”, Chemical Engineering Science, 57 (2002).
24. Zhong Zhang & Guanrong Chen, “Chaotic motion generation with applications to liquid mixing”, Proceedings of the 2005 European Conference on Circuit Theory and Design, vol.1 (2005), I/225-I/228., DOI: 10.1109/ECCTD.2005.1522951.
25. Kennedy, MP, “Three steps to Chaos - Part II: A Chua's Circuit Primer”, IEEE Transactions on circuits and systems-I, Fundamental theory and applications, Vol. 40, (10) (1993),.657-674.




How to Cite

Natarajan, S., & kumaar, S. S. (2016). A NOVEL APPLICATION OF CHAOTIC PROPERTIES IN WATER TREATMENT PLANT. JOURNAL OF ADVANCES IN CHEMISTRY, 12(12), 4749–4763. https://doi.org/10.24297/jac.v12i14.479