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Arabian Journal for Science and Engineering

, Volume 44, Issue 3, pp 2033–2044 | Cite as

Chlorine Soft Sensor Based on Extreme Learning Machine for Water Quality Monitoring

  • Mohamed Djerioui
  • Mohamed Bouamar
  • Mohamed Ladjal
  • Azzedine ZerguineEmail author
Research Article - Electrical Engineering
  • 29 Downloads

Abstract

A major problem in water treatment plants is the continuous difficulty faced in online measurement by means of dedicated measuring hardware and laboratory analysis of certain variables related to the composition of water. Actually, for several reasons, such as the high cost of some sensors, their number, the dedicated time to check out the sensors, cleaning operation, calibration routines and sensor replacement, make their proper operation hard to ensure high-quality composition of water. Furthermore, in water quality monitoring, there is a huge number of heterogeneous sensors which may be time-consuming in the measurement and processing stages. Nevertheless, soft sensor approach can provide an effective and economic way to solve this problem for any cases of sensor failure. This work presents a contribution to the study and development of a soft sensor used in water quality monitoring using chlorine. A comparative study between support vector machine (SVM) and extreme learning machine (ELM) techniques in terms of learning time and other parameters for regression and classification is presented. The main objective is to set up a system architecture based on a soft sensor for water quality in order to make an adapted decision to the control and monitoring of water quality issues. ELM is shown to be the most suitable technique to address the previously mentioned problems as it has better characteristics than those of the SVM technique. An example of application is provided to focus on the interest of using a chlorine soft sensor as it is accurate, efficient and less cost-effective tool.

Keywords

Water quality monitoring Chlorine soft sensor Regression Classification Support vector machine Extreme learning machine 

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References

  1. 1.
    Liukkonen, M.; Juntunen, P.; Laakso, I.; Hiltunen, Y.: A software platform for process monitoring: applications to water treatment. Exp. Syst. Appl. 40, 2631–2639 (2013)CrossRefGoogle Scholar
  2. 2.
    World Health Organization. Guidelines for Drinking-Water Quality, 4\({\rm th}\) ed. http://whqlibdoc.who.int/publications/2011/9789241548151_eng.pdf
  3. 3.
    Qiao, J.; Hu, Z.; Li, W.: Soft measurement modeling based on chaos theory for biochemical oxygen demand (BOD). Water 8, 581 (2016)CrossRefGoogle Scholar
  4. 4.
    Banna, M.H.; Najjaran, H.; Sadiq, R.: Miniaturized water quality monitoring pH and conductivity sensors. Sens. Actuators B 193, 434–441 (2014)CrossRefGoogle Scholar
  5. 5.
    Zhuiykov, S.: Solid-state sensors monitoring parameters of water quality for the next generation of wireless sensor networks. Sens. Actuators B 161, 1–20 (2012)CrossRefGoogle Scholar
  6. 6.
    Murphy, K.; Heery, B.; Sullivan, T.: A low-cost autonomous optical sensor for water quality monitoring. Talanta 132, 520–527 (2015)CrossRefGoogle Scholar
  7. 7.
    Fortuna, L.; Graziani, S.; Rizzo, A.; Xibilia, M.G.: Soft Sensors for Monitoring and Control of Industrial Processes. Springer, London (2007)zbMATHGoogle Scholar
  8. 8.
    Kadlec, P.; Gabrys, B.; Strandt, S.: Data-driven soft sensors in the process industry. Comput. Chem. Eng. 33, 795–814 (2009)CrossRefGoogle Scholar
  9. 9.
    Pani, A.K.; Vadlamudi, V.K.; Mohanta, H.K.: Development and comparison of neural network based soft sensors for online estimation of cement clinker quality. ISA Trans. 52, 19–29 (2013)CrossRefGoogle Scholar
  10. 10.
    Sharma, S.; Tambe, S.S.: Soft-sensor development for biochemical systems using genetic programming. Biochem. Eng. J. 85, 89–100 (2014)CrossRefGoogle Scholar
  11. 11.
    Sagmeister, P.; Wechselberger, P.; Jazini, M.; Meitz, A.: Soft sensor assisted dynamic bioprocess control: efficient tools for bioprocess development. Chem. Eng. Sci. 96, 190–198 (2013)CrossRefGoogle Scholar
  12. 12.
    Huang, M.; Mab, Y.; Wan, J.: A sensor-software based on a genetic algorithm-based neural fuzzy system for modeling and simulating a wastewater treatment process. Appl. Soft Comput. 27, 1–10 (2015)CrossRefGoogle Scholar
  13. 13.
    Lamrini, B.; Benhammou, A.; Le Lann, M.-V.; Karama, A.: A neural software sensor for on-line prediction of coagulant dosage in a drinking water treatment plant. Trans. Inst. Meas. Control 27, 195–213 (2005)CrossRefGoogle Scholar
  14. 14.
    Wang, L.; Shao, C.; Wang, H.; WU, H.: Radial basis function neural networks-based modeling of the membrane separation process: hydrogen recovery from refinery gases. J. Nat. Gas Chem. 15, 230–234 (2006)CrossRefGoogle Scholar
  15. 15.
    Chen, S.; Samingan, A.K.; Hanzo, L.: Support vector machine multiuser receiver for DS-CDMA signals in multipath channels. IEEE Trans. Neural Netw. 12, 604–611 (2001)CrossRefGoogle Scholar
  16. 16.
    Cortes, C.; Vapnik, V.: Support vector networks. Mach. Learn. 20, 273–297 (1995)zbMATHGoogle Scholar
  17. 17.
    Kang, F.; Qing, X.; Li, J.: Slope reliability analysis using surrogate models via new support vector machines with swarm intelligence. Appl. Math. Model. 40, 6105–6120 (2016)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Shang, C.; Gao, X.; Yang, F.; Huang, D.: Novel Bayesian framework for dynamic soft sensor based on support vector machine with finite impulse response. IEEE Trans. Control Syst. Technol. 22, 1550–1557 (2014)CrossRefGoogle Scholar
  19. 19.
    Jieqiong, S.; Wang, X.; Zhao, S.; Chen, B.; Li, C.; Yang, Z.: A structurally simplified hybrid model of genetic algorithm and support vector machine for prediction of chlorophyll a in reservoirs. Water 7, 1610–1627 (2015)CrossRefGoogle Scholar
  20. 20.
    Huang, G.B.; Zhu, Q.Y.; Siew, C.K.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)CrossRefGoogle Scholar
  21. 21.
    Zong, W.W.; Huang, G.B.; Chen, Y.Q.: Weighted extreme learning machine for imbalance learning. Neurocomputing 101, 229–242 (2013)CrossRefGoogle Scholar
  22. 22.
    Han, H.-G.; Wang, L.-D.; Qiao, J.-F.: Hierarchical extreme learning machine for feedforward neural network. Neurocomputing 128, 128–135 (2014)CrossRefGoogle Scholar
  23. 23.
    Wang, W.; Deng, C.; Li, X.: Soft sensing of dissolved oxygen in fishpond via extreme learning machine. In: Proceeding of the 11th World Congress on Intelligent Control and Automation Shenyang. China, pp. 3393–3395 (2014)Google Scholar
  24. 24.
    Huang, G.-B.; Zhou, H.; Ding, X.; Zhang, R.: Extreme learning machine for regression and multiclass classification. IEEE Trans Syst. Man Cybern. B Cybern. 42, 513–529 (2012)CrossRefGoogle Scholar
  25. 25.
    Kang, F.; Liu, J.: Li, Junjie; Li, Shouju: Concrete dam deformation prediction model for health monitoring based on extreme learning machine. Struct. Control Health Monit. 24, e1997 (2017)CrossRefGoogle Scholar
  26. 26.
    Jolliffe, I.: Principal Component Analysis, 2nd edn. Springer Series in StatisticsSpringer, New York (2002)zbMATHGoogle Scholar
  27. 27.
    Mazlum, N.; Ozer, A.; Mazlum, S.: Interpretation of water quality data by principal components analysis. Tr. J. Eng. Environ. Sci. 23, 19–26 (1999)zbMATHGoogle Scholar
  28. 28.
    Yue, H.H.; Tomoyasu, M.: Weighted principal component analysis and its applications to improve FDC performance. CDC. In: 43rd IEEE Conference on Decision and Control, 2004, IEEE, vol. 4, pp. 4262–4267 (2004)Google Scholar
  29. 29.
    Zheng, X.X.; Qian, F.: Soft sensor modeling based on PCA and support vector machines. J. Syst. Simul. 3, 52 (2006)Google Scholar
  30. 30.
    Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (2000)CrossRefzbMATHGoogle Scholar
  31. 31.
    Schölkopf, B.; Smola, A.: Learning with Kernels, Support Vector Machines, Regularization, Optimization and Beyond. MIT Press, Cambridge (2002)Google Scholar
  32. 32.
    Kordon, A.; Smits, G.; Jordaan, E.; Rightor, Ed.: Robust soft sensors based on integration of genetic programming, analytical neural networks, and support vector machines. In: Proceedings of the 2002 Congress on Evolutionary Computation CEC2002, IEEE Press, pp. 896–901 (2002)Google Scholar
  33. 33.
    Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Upper Saddle River (1999)zbMATHGoogle Scholar
  34. 34.
    Radhika, Y.; Shashi, M.: Atmospheric temperature prediction using support vector machine. Int. J. Comput. Theory Eng. 1, 1793–8201 (2009)Google Scholar
  35. 35.
    Huang, G.-B.; Babri, H.A.: Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Trans. Neural. Netw. 9, 224–229 (1998)CrossRefGoogle Scholar
  36. 36.
    Ahmad, N.; Janahiraman, T.V.; Tarlochan, F.: Modeling of surface roughness in turning operation using extreme learning machine. Arab. J. Sci. Eng. 40, 595–602 (2015)CrossRefGoogle Scholar
  37. 37.
    Ding, S.; Zhao, H.; Zhang, Y.; Xinzheng, X.; Nie, R.: Extreme learning machine: algorithm, theory and applications. Artif. Intell. Rev. 44, 103–115 (2013)CrossRefGoogle Scholar
  38. 38.
    Chen, F.L.; Ou, T.Y.: Sales forecasting system based on Gray extreme learning machine with Taguchi method in retail industry. Exp. Syst. Appl. 38, 1336–1345 (2011)CrossRefGoogle Scholar
  39. 39.
    Bartlett, P.L.: The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans. Inform. Theory 44, 525–536 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  40. 40.
    Canu, S; Grandvalet, Y; Rakotomamonjy, A: SVM and kernel methods MATLAB toolbox. Perception Systèmes et Information, INSA de Rouen, Rouen. France. http://asi.insarouen.fr/~arakotom/toolbox/index (2003)
  41. 41.
    Feng, G.; Huang, G.-B.; Lin, Q.; Gay, R.: Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans. Neural Netw. 20, 1352–1357 (2009)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Mohamed Djerioui
    • 1
  • Mohamed Bouamar
    • 1
  • Mohamed Ladjal
    • 1
  • Azzedine Zerguine
    • 2
    Email author
  1. 1.LASS Laboratory, Department of Electronics, Faculty of TechnologyUniversity of M’silaM’SilaAlgeria
  2. 2.Electrical Engineering DepartmentKing Fahd University of Petroleum and MineralsDhahranSaudi Arabia

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