References

  1. C.G. White, Handbook of Chlorination and Alternative Disinfectants, 5th ed., John Wiley & Sons Inc., New York, 2010.
  2. S.M. Acharya, F. Kurisu, I. Kasuga, H. Furumai, Chlorine dose determines bacterial community structure of subsequent regrowth in reclaimed water, J. Water Environ. Technol., 14 (2016) 15–24.
  3. T. Bond, N. Graham, Predicting chloroform production from organic precursors, Water Res., 124 (2017) 167–176.
  4. Ministry of Health, Drinking Water Health Standards GB5749- 2006, Ministry of Health, China, 2007.
  5. WHO, Guidelines for Drinking-Water Quality, 4th ed., World Health Organisation, Geneva, 2011.
  6. G. Hua, D.A. Reckhow, DBP formation during chlorination and chloramination: Effect of reaction time, pH, dosage, and temperature, J. Am. Water Works Assn., 100 (2008) 82–95.
  7. P. Roccaro, H.-S. Chang, F.G.A. Vagliasindi, G.V. Korshin, Differential absorbance study of effects of temperature on chlorine consumption and formation of disinfection by-products in chlorinated water, Water Res., 42 (2008) 1879–1888.
  8. S.H. Maier, R.S. Powell, C.A. Woodward, Calibration and comparison of chlorine decay models for a test water distribution system, Water Res., 34 (2000) 2301–2309.
  9. T. Fuchigami, K. Terashima, H. Bandow, Residual chlorine management based on quantitative estimation of chlorine consumption by inner wall surfaces of distribution pipes, Water Sci. Technol. Water Supply, 12 (2012) 157–167.
  10. Y. Hosaka, Examination of rechlorination at water supply stations using a model for residual chlorine consumption, J. Water Environ. Technol., 10 (2012) 101–115.
  11. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, 1995.
  12. V. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
  13. G. Loosli, S. Canu, S.O. Cheng, Learning SVM in Krein spaces, IEEE Trans. Software Eng., 38 (2015) 1204–1216.
  14. F. Girosi, An equivalence between sparse approximation and support vector machines, Neural Comput., 10 (1998) 1455–1480.
  15. R.G. Negri, L.V. Dutra, S.J.S. Sant’Anna, Comparing support vector machine contextual approaches for urban area classification, Remote Sens. Lett., 7 (2016) 485–494.
  16. Z. Liu, J. Shao, W. Wu, H. Chen, C. Shi, Comparison on landslide nonlinear displacement analysis and prediction with computational intelligence approaches, Landslides, 11 (2014) 889–896.
  17. D. Zibar, J. Thrane, J. Wass, J. Diniz, M. Piels, Machine learning techniques for optical performance monitoring from directly detected PDM-QAM signals, J. Lightwave Technol., 35 (2017) 868–875.
  18. L. Sun, J. Bao, Y. Chen, M. Yang, Research on parameter selection method for support vector machines, Appl. Intell., 48 (2017) 331–342.
  19. O. Chapelle, V. Vapnik, O. Bousquet, S. Mukherjee, Choosing multiple parameters for support vector machines, Mach. Learn., 46 (2002) 131–159.
  20. M.M. Adankon, M. Cheriet, Optimizing resources in model selection for support vector machine, Pattern Recognit., 40 (2007) 953–963.
  21. S.U. Khan, S. Yang, L. Wang, L. Liu, A modified particle swarm optimization algorithm for global optimizations of inverse problems, IEEE Trans. Magn., 52 (2016) 1–4.
  22. Y. Ren, G. Bai, Determination of optimal SVM parameters by using GA/PSO, J. Comput., 5 (2010) 1160–1168.
  23. L. Shen, H. Chen, Z. Yu, W. Kang, B. Zhang, H. Li, B. Yang, D. Liu, Evolving support vector machines using fruit fly optimization for medical data classification, Knowledge Based Syst., 96 (2016) 61–75.
  24. S.T. Hong, A.K. Lee, H.H. Lee, N.S. Park, S.H. Lee, Application of neuro-fuzzy PID controller for effective post-chlorination in water treatment plant, Desal. Wat. Treat., 47 (2012) 211–220.
  25. X.M. Sun, K.Z. Liu, C.Y. Wen, W. Wang, Predictive control of nonlinear continuous networked control systems with large time-varying transmission delays and transmission protocols, Automatica, 64 (2016) 76–85.
  26. L. Teng, Y. Wang, W. Cai, H. Li, Robust model predictive control of discrete nonlinear systems with time delays and disturbances via T–S fuzzy approach, J. Process Control, 53 (2017) 70–79.
  27. H. Zhang, A. Chakrabarty, R. Ayoub, G.T. Buzzard, S. Sundaram, Sampling-Based Explicit Nonlinear Model Predictive Control for Output Tracking, IEEE 55th Conference on Decision and Control (CDC), 2016, pp. 4722–4727.
  28. A. Chakrabarty, G.T. Buzzard, S.H. Żak, Output-tracking quantized explicit nonlinear model predictive control using multiclass support vector machines, IEEE Trans. Ind. Electron., 64 (2017) 4130–4138.
  29. A. Chakrabarty, V. Dinh, M.J. Corless, A.E. Rundell, S.H. Żak, G.T. Buzzard, Support vector machine informed explicit nonlinear model predictive control using low-discrepancy sequences, IEEE Trans. Autom. Control, 62 (2017) 135–148.
  30. C. Wei, J. Luo, H. Dai, Z. Yin, W. Ma, J. Yuan, Globally robust explicit model predictive control of constrained systems exploiting SVM-based approximation, Int. J. Robust Nonlinear Control, 27 (2017) 3000–3027.
  31. M. Iancu, M.V. Cristea, P.S. Agachi, MPC vs. PID. the advanced control solution for an industrial heat integrated fluid catalytic cracking plant, Comput. Aided Chem. Eng., 29 (2011) 517–521.
  32. S. Kwon, M. Nayhouse, G. Orkoulas, N. Dong, P.D. Christofides, A method for handling batch-to-batch parametric drift using moving horizon estimation: application to run-to-run MPC of batch crystallization, Chem. Eng. Sci., 127 (2015) 210–219.
  33. B.Y. Gao, H.H. Hahn, E. Hoffmann, Evaluation of aluminumsilicate polymer composite as a coagulant for water treatment, Water Res., 36 (2002) 3573–3581.
  34. W. Jakubowski, G.F. Craun, Update on the Control of Giardia in Water Supplies, B.E. Olson, M.E. Olson, P.M. Wallis Eds., Giardia: The Cosmopolitan Parasite, CABI Publishing, Wallingford, 2002.
  35. D.A. Cornwell, S.H. Via, Demonstrating Cryptosporidium removal using spore monitoring at lime-softening plants, J. Am. Water Works Assn., 95 (2003) 124–133.
  36. T. Bond, M.R. Templeton, O. Rifai, N.J. Graham, Chlorinated and nitrogenous disinfection by-product formation from ozonation and post-chlorination of natural organic matter surrogates, Chemosphere, 111 (2014) 218–224.
  37. M. Deborde, G.U. Von, Reactions of chlorine with inorganic and organic compounds during water treatment—Kinetics and mechanisms: a critical review, Water Res., 42 (2008) 13–51.
  38. P. Hua, E. Vasyukova, W. Uhl, A variable reaction rate model for chlorine decay in drinking water due to the reaction with dissolved organic matter, Water Res., 75 (2015) 109–122.
  39. D.F. Lawler, P.C. Singer, Analyzing disinfection kinetics and reactor design: a conceptual approach versus the SWTR, J. AWWA, 85 (1993) 67–76.
  40. H. Haider, S. Haydar, M. Sajid, S. Tesfamariam, R. Sadiq, Framework for optimizing chlorine dose in small- to mediumsized water distribution systems: a case of a residential neighbourhood in Lahore, Pakistan, Water SA, 41 (2015) 614–623.
  41. X.M. Zhou, A new method with high confidence for validation of computer simulation models of flight systems, J. Syst. Eng. Electron, 4 (1993) 43–52.
  42. S.J. Qin, T.A. Badgwell, A survey of industrial model predictive control technology, Control Eng. Pract., 11 (2003) 733–764.
  43. D.E. Quevedo, G.C. Goodwin, J.A. De Doná, Finite constraint set receding horizon quadratic control, Int. J. Robust Nonlinear Control, 14 (2004) 355–377.
  44. R.P. Aguilera, D.E. Quevedo, Stability analysis of quadratic MPC with a discrete input alphabet, IEEE Trans. Autom. Control, 58 (2013) 3190–3196.