Neural Computing and Applications

, Volume 31, Supplement 2, pp 957–968 | Cite as

Black box modeling and multiobjective optimization of electrochemical ozone production process

  • Seyed Reza NabaviEmail author
  • Mahmoud Abbasi
Original Article


In this paper, simultaneous maximization of generated ozone concentration and minimization of electrical energy consumption is investigated in a laboratory-scale electrochemical ozone production system (EOP). Neural network simulation of EOP was carried out for generated ozone concentration prediction by Abbasi et al. (Chem Eng Res Des 92(11):2618–2625, 2014). In this study, neural network models (as black box models) were developed to predict both generated ozone concentration and electrical energy consumption. The models then were used for optimization. Altruistic non-dominated sorting genetic algorithm with jumping gene variant and termination criterion was used for MOO. Generational distance and spread were used in the termination criterion in order to stop algorithm after the right number of generations. Moreover, several optimal solutions from the Pareto-optimal set are chosen and then validated experimentally.


Ozone production Electrochemical process Neural networks Multiobjective optimization Alt-NSGA-II-aJG Termination criteria Black box model 



Authors appreciate Professor G.P. Rangaiah from National University of Singapore (NUS) for his valuable comments and editing the manuscript.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


  1. 1.
    Abbasi M, Soleynami AR, Basiri Parsa J (2014) Operation simulation of a recycled electrochemical ozone generator using artificial neural network. Chem Eng Res Des 92(11):2618–2625CrossRefGoogle Scholar
  2. 2.
    Abbasi M, Soleynami AR, Basiri Parsa J (2015) Degradation of Rhodamine B by an electrochemical ozone generating system consist of a Ti anode coated with nanocomposite of Sn–Sb–Ni oxide. Process Saf Environ Prot 94:140–148CrossRefGoogle Scholar
  3. 3.
    Acharya BR, Mohanty CP, Mahapatra SS (2013) Multi-objective optimization of electrochemical machining of hardened steel using NSGAII. Proc Eng 51:554–560CrossRefGoogle Scholar
  4. 4.
    Agrawal A, Gupta SK (2008) Jumping gene adaptations of NSGA-II and their use in the multi-objective optimal design of shell and tube heat exchangers. Chem Eng Res Des 86(2):123–139CrossRefGoogle Scholar
  5. 5.
    Agrawal N, Rangaiah GP, Ray AK, Gupta SK (2006) Multiobjective optimization of the operation of an Industrial low density polyethylene tubular reactor using genetic algorithm and its jumping gene adaptations. Ind Eng Chem Res 2006(45):3182CrossRefGoogle Scholar
  6. 6.
    Arihara K, Terashima C, Fujishima A (2007) Electrochemical production of high-concentration ozone-water using freestanding perforated diamond electrodes. J Electrochem Soc 154:E71–E75CrossRefGoogle Scholar
  7. 7.
    Basiri Parsa J, Abbasi M (2012) Application of in situ electrochemically generated ozone for degradation of anthraquninone dye Reactive Blue 19. J Appl Electrochem 42:435–442CrossRefGoogle Scholar
  8. 8.
    Basiri Parsa J, Abbasi M (2012) High-efficiency ozone generation via electrochemical oxidation of water using Ti anode coated with Ni–Sb–SnO2. J Solid State Electrochem 16:1011–1018CrossRefGoogle Scholar
  9. 9.
    Basiri Parsa J, Golmirzaei M, Abbasi M (2014) Degradation of azo dye C.I. Acid Red 18 in aqueous solution by ozone-electrolysis process. J Ind Eng Chem 20:689–694CrossRefGoogle Scholar
  10. 10.
    Bhaskar V, Gupta SK, Ray AK (2000) Applications of multiobjective optimization in chemical engineering. Rev Chem Eng 16(1):1–54CrossRefGoogle Scholar
  11. 11.
    Bhat SA, Saraf DN, Gupta S, Gupta SK (2006) On-line optimizing control of bulk free radical polymerization reactors under temporary loss of temperature regulation: experimental study on a 1-L batch reactor. Ind Eng Chem Res 45(22):7530–7539CrossRefGoogle Scholar
  12. 12.
    Bhutani N, Rangaiah GP, Ray AK (2006) First-principles, data-based, and hybrid modeling and optimization of an industrial hydrocracking unit. Ind Eng Chem Res 45(23):7807–7816CrossRefGoogle Scholar
  13. 13.
    Buffle M-O, Schumacher J, Salhi E, Jekel M, Gunten UV (2006) Measurement of the initial phase of ozone decomposition in water and wastewater by means of a continuous quench-flow system: application to disinfection and pharmaceutical oxidation. Water Res 40:1884–1894CrossRefGoogle Scholar
  14. 14.
    Chaudhari P, Gupta SK (2012) Multiobjective optimization of a fixed bed maleic anhydride reactor using an improved biomimetic adaptation of NSGA-II. Ind Eng Chem Res 51:3279–3294CrossRefGoogle Scholar
  15. 15.
    Coello Coello CA, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Springer, New YorkzbMATHGoogle Scholar
  16. 16.
    Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, ChichesterzbMATHGoogle Scholar
  17. 17.
    Deb K, Agarwal S, Pratap A, Meyarivan T (2000) A fast and elitist multi-objective genetic algorithm: NSGA-II, Technical Report 2000001, IIT Kanpur, KanGAL. Accessed Mar 2017
  18. 18.
    Deb K, Pratap A, Agarwal S, Meyarivan TA (2002) Fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197CrossRefGoogle Scholar
  19. 19.
    Dufresne S, Hewitt A, Robitaille S (2004) Ozone sterilization: another option for healthcare in the 21st century. Am J Infect Control 32(3):E26–E27CrossRefGoogle Scholar
  20. 20.
    Gujarathi AM, Babu BV (2009) Optimization of adiabatic styrene reactor: a hybrid multiobjective differential evolution (H-MODE) approach. Ind Eng Chem Res 48(24):11115–11132CrossRefGoogle Scholar
  21. 21.
    Gujarathi AM, Babu BV (2010) Multi-objective optimization of industrial styrene reactor: adiabatic and pseudo-isothermal operation. Chem Eng Sci 65(6):2009–2026CrossRefGoogle Scholar
  22. 22.
    Gujarathi AM, Babu BV (2011) Multiobjective optimization of industrial processes using elitist multiobjective differential evolution (Elitist-MODE). Mater Manuf Process 26(3):455–463CrossRefGoogle Scholar
  23. 23.
    Gujarathi AM, Motagamwala AH, Babu BV (2013) Multiobjective optimization of industrial naphtha cracker for production of ethylene and propylene. Mater Manuf Process 28(7):803–810CrossRefGoogle Scholar
  24. 24.
    Gujarathi AM, Sadaphal A, Bathe GA (2015) Multi-objective optimization of solid state fermentation process. Mater Manuf Process 30(4):511–519CrossRefGoogle Scholar
  25. 25.
    Guria C, Verma M, Mehrotra SP, Gupta SK (2005) Multi-objective optimal synthesis and design of froth flotation circuits for mineral processing, using the jumping gene adaptation of genetic algorithm. Ind Eng Chem Res 44(8):2621–2633CrossRefGoogle Scholar
  26. 26.
    Hadi N, Niaei A, Nabavi SR, Alizadeh R, Navaei Shirazi M, Izadkhah B (2016) An intelligent approach to design and optimization of M-Mn/H-ZSM-5 (M: Ce, Cr, Fe, Ni) catalysts in conversion of methanol to propylene. J Taiwan Inst Chem Eng 59:173–185CrossRefGoogle Scholar
  27. 27.
    Heng S, Yeung KL, Djafer M, Schrotter JC (2007) A novel membrane reactor for ozone water treatment. J Membr Sci 289(1–2):67–75CrossRefGoogle Scholar
  28. 28.
    Himmelblau D (2008) Accounts of experiences in the application of artificial neural networks in chemical engineering. Ind Eng Chem Res 47(16):5782–5796CrossRefGoogle Scholar
  29. 29.
    Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366CrossRefzbMATHGoogle Scholar
  30. 30.
    Hussain MA (1999) Review of the applications of neural networks in chemical process control simulation and online implementation. Artif Intell Eng 13(1):55–68CrossRefGoogle Scholar
  31. 31.
    Ikehata K, Naeimeh JN, Gamal El-Din M (2006) Degradation of aqueous pharmaceuticals by ozonation and advanced oxidation processes: a review. Ozone Sci Eng 28(6):353–414CrossRefGoogle Scholar
  32. 32.
    Izadkhah B, Nabavi SR, Niaei A, Salari D, Mahmuodi Badikia T, Çaylakc N (2012) Design and optimization of Bi-metallic Ag-ZSM5 catalysts for catalytic oxidation of volatile organic compounds. J Ind Eng Chem 18(6):2083–2091CrossRefGoogle Scholar
  33. 33.
    Kasat RB, Gupta SK (2003) Multi-objective optimization of an industrial fluidized-bed catalytic cracking unit (FCCU) using genetic algorithm (GA) with the jumping genes operator. Comput Chem Eng 27(12):1785–1800CrossRefGoogle Scholar
  34. 34.
    Khataee AR, Bagherzadeh Kasiri M (2011) Artificial neural network modeling of water and wastewater treatment processes. NOVA Science Publisher, Inc, HauppaugeGoogle Scholar
  35. 35.
    Kobayashi Y, Omata K, Yamada M (2010) Screening of additives to a Co/SrCO3 catalyst by artificial neural network for preferential oxidation of CO in excess H2. Ind Eng Chem Res 49(4):1541–1549CrossRefGoogle Scholar
  36. 36.
    Lerouge S (2012) Non-traditional sterilization techniques for biomaterials and medical devices. In: Lerouge S, Simmons A (eds) Sterilisation of biomaterials and medical devices, ch. 8, pp 97–116, Woodhead Publishing, Philadelphia, USAGoogle Scholar
  37. 37.
    Li X, Zecchin AC, Maier HR (2014) Selection of smoothing parameter estimators for general regression neural networks applications to hydrological and water resources modelling. Environ Model Softw 59:162–186CrossRefGoogle Scholar
  38. 38.
    Maier HR, Kapelan Z, Kasprzyk J, Kollat J, Matott LS, Cunha MC, Dandy GC, Gibbs MS, Keedwell E, Marchi A, Ostfeld A, Savic D, Solomatine DP, Vrugt JA, Zecchin AC, Minsker BS, Barbour EJ, Kuczera G, Pasha F, Castelletti A, Giuliani M, Reed PM (2014) Evolutionary algorithms and other metaheuristics in water resources: current status, research challenges and future directions. Environ Model Softw 62:272–299CrossRefGoogle Scholar
  39. 39.
    Masuduzzaman, Rangaiah GP (2009) Multi-objective optimization applications in chemical engineering. In: Rangaiah GP (ed) Multi-objective optimization: techniques and applications in chemical engineering. World Scientific, SingaporeGoogle Scholar
  40. 40.
    Miller J, Miller J (2010) Statistics and Chemometrics for Analytical Chemistry, 4th edn. New York, USAzbMATHGoogle Scholar
  41. 41.
    Molga E (2003) Neural network approach to support modelling of chemical reactors: problems, resolutions, criteria of application. Chem Eng Process Process Intensif 42(8):675–695CrossRefGoogle Scholar
  42. 42.
    Nabavi SR (2016) Preparation conditions of asymmetric polyetherimide membrane for prevaporation of isopropanol. Chem Product Process Model 11(1):47–50Google Scholar
  43. 43.
    Nabavi R, Niaei A, Salari D, Towfighi J (2007) Modeling of thermal cracking of LPG: application of artificial neural network in prediction of the main product yields. J Anal Appl Pyrolysis 80(1):175–181CrossRefGoogle Scholar
  44. 44.
    Nabavi R, Salari D, Niaei A, Vakil-Baghmisheh MT (2009) A neural network approach for prediction of main product yields in methanol to olefins process. Int J. Chem React Eng 7(1):1542–6580Google Scholar
  45. 45.
    Nabavi R, Rangaiah GP, Niaei A, Salari D (2009) Multiobjective optimization of an industrial LPG thermal cracker using a first principles model. Ind Eng Chem Res 48(21):9523–9533CrossRefGoogle Scholar
  46. 46.
    Nabavi R, Rangaiah GP, Niaei A, Salari D (2009) Design optimization of an LPG thermal cracker for multiple objectives. Int J Chem React Eng 9(1):1542–1580Google Scholar
  47. 47.
    Nascimento CAO, Giudici R, Guardani R (2000) Neural network based approach for optimization of industrial chemical processes. Comput Chem Eng 24(9–10):2303–2314CrossRefGoogle Scholar
  48. 48.
    Niaei A, Mahmuodi Badikia T, Nabavi SR, Salari D, Izadkhah B, Çaylakc N (2013) Neuro-genetic aided design of modified H-ZSM-5 catalyst for catalytic conversion of methanol to gasoline range hydrocarbons. J Taiwan Inst Chem Eng 44(2):247–256CrossRefGoogle Scholar
  49. 49.
    Pirdashti M, Curteanu S, Hashemi Kamangar M, Hassim MH, Khatami MA (2013) Artificial neural networks: applications in chemical engineering. Rev Chem Eng 29(4):205–239CrossRefGoogle Scholar
  50. 50.
    Ramteke M, Gupta SK (2009) Biomimicking altruistic behavior of honey bees in multi-objective genetic algorithm. Ind Eng Chem Res 48(21):9671–9685CrossRefGoogle Scholar
  51. 51.
    Rangaiah GP (2009) Multi-objective optimization: techniques and applications in chemical engineering. World Scientific, SingaporeGoogle Scholar
  52. 52.
    Rangaiah GP, Bonilla-Petriciolet A (2013) Multi-objective Optimization in chemical engineering: developments and applications. John Wiley & Sons, ChichesterCrossRefGoogle Scholar
  53. 53.
    Sharma S, Rangaiah GP (2013) Multi-objective optimization applications in chemical engineering. In: Rangaiah GP, Bonilla-Petriciolet A (eds) Multi-objective optimization in chemical engineering: developments and applications. Wiley, ChichesterCrossRefGoogle Scholar
  54. 54.
    Sharma S, Rangaiah GP (2013) An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes. Comput Chem Eng 56:155–173CrossRefGoogle Scholar
  55. 55.
    Sharma N, Singh K (2012) Model predictive control and neural network predictive control of TAME reactive distillation column. Chem Eng Process Process Intensif 59:9–21CrossRefGoogle Scholar
  56. 56.
    Sharma S, Nabavi SR, Rangaiah GP (2013) Performance comparison of jumping gene adaptations of the elitist non-dominated sorting genetic algorithm. In: Rangaiah GP, Bonilla-Petriciolet A (eds) Multi-objective optimization in chemical engineering: developments and applications. Wiley, ChichesterCrossRefGoogle Scholar
  57. 57.
    Sharma S, Nabavi SR, Rangaiah GP (2014) Jumping gene adaptations of NSGA-II with altruism approach: performance comparison and application to Williams–Otto process. In: Valadi J, Siarry P (eds) Applications of metaheuristics in process engineering. Springer, BerlinGoogle Scholar
  58. 58.
    Shatalov AA, Pereira H (2008) Arundo donax L. reed: new perspectives for pulping and bleaching. 5. Ozone-based TCF bleaching of organosolv pulps. Bioresour Technol 99(3):472–478CrossRefGoogle Scholar
  59. 59.
    Sinhaa SK, Kumara M, Guria C, Kumara A, Banerjee C (2017) Biokinetic model-based multi-objective optimization of Dunaliella tertiolecta cultivation using elitist non-dominated sorting genetic algorithm with inheritance. Bioresour Technol. doi: 10.1016/j.biortech.2017.03.146 (in press) Google Scholar
  60. 60.
    Van Ornum SG, Champeau RM, Pariza R (2006) Ozonolysis applications in drug synthesis. Chem Rev 106(7):2990–3001CrossRefGoogle Scholar
  61. 61.
    VanVeldhuizen DA, Lamont GB (1998) Evolutionary computation and convergence to a Pareto front, Accessed Aug 2015
  62. 62.
    Wang YH, Cheng Sh, Chan KY, Li XY (2005) Electrolytic generation of ozone on antimony and nickel doped tin oxide electrode. J Electrochem Soc 152(11):D197–D200CrossRefGoogle Scholar
  63. 63.
    Wieland R, Mirschel W, Zbell B, Groth K, Pechenick A, Fukuda K (2010) A new library to combine artificial neural networks and support vector machines with statistics and a database engine for application in environmental modeling. Environ Model Softw 25(4):412–420CrossRefGoogle Scholar
  64. 64.
    Zitzler E, Thiele L (1998) Multi-objective optimization using evolutionary algorithms: a comparative case study. In: Parallel problem solving from nature, pp 292–301Google Scholar

Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  1. 1.Department of Applied ChemistryUniversity of MazandaranBabolsarIran

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