Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Neural Computing and Applications
Published in: Neural Computing and Applications 2/2019

19-06-2017 | Original Article

Black box modeling and multiobjective optimization of electrochemical ozone production process

Authors: Seyed Reza Nabavi, Mahmoud Abbasi

Published in: Neural Computing and Applications | Special Issue 2/2019

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

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.
previous article Optimization of friction stir welding process using NSGA-II and DEMO
next article Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now
Appendix
Available only for authorised users
Literature
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):2618–2625CrossRef
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 94:140–148CrossRef
3.
Acharya BR, Mohanty CP, Mahapatra SS (2013) Multi-objective optimization of electrochemical machining of hardened steel using NSGAII. Proc Eng 51:554–560CrossRef
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):123–139CrossRef
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):3182CrossRef
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–E75CrossRef
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 42:435–442CrossRef
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 16:1011–1018CrossRef
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 20:689–694CrossRef
10.
Bhaskar V, Gupta SK, Ray AK (2000) Applications of multiobjective optimization in chemical engineering. Rev Chem Eng 16(1):1–54CrossRef
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):7530–7539CrossRef
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):7807–7816CrossRef
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 40:1884–1894CrossRef
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 51:3279–3294CrossRef
15.
Coello Coello CA, Lamont GB, Van Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Springer, New YorkMATH
16.
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, ChichesterMATH
17.
18.
Deb K, Pratap A, Agarwal S, Meyarivan TA (2002) Fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evolut Comput 6(2):182–197CrossRef
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–E27CrossRef
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):11115–11132CrossRef
21.
Gujarathi AM, Babu BV (2010) Multi-objective optimization of industrial styrene reactor: adiabatic and pseudo-isothermal operation. Chem Eng Sci 65(6):2009–2026CrossRef
22.
Gujarathi AM, Babu BV (2011) Multiobjective optimization of industrial processes using elitist multiobjective differential evolution (Elitist-MODE). Mater Manuf Process 26(3):455–463CrossRef
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):803–810CrossRef
24.
Gujarathi AM, Sadaphal A, Bathe GA (2015) Multi-objective optimization of solid state fermentation process. Mater Manuf Process 30(4):511–519CrossRef
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):2621–2633CrossRef
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 59:173–185CrossRef
27.
Heng S, Yeung KL, Djafer M, Schrotter JC (2007) A novel membrane reactor for ozone water treatment. J Membr Sci 289(1–2):67–75CrossRef
28.
Himmelblau D (2008) Accounts of experiences in the application of artificial neural networks in chemical engineering. Ind Eng Chem Res 47(16):5782–5796CrossRef
29.
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2(5):359–366CrossRefMATH
30.
Hussain MA (1999) Review of the applications of neural networks in chemical process control simulation and online implementation. Artif Intell Eng 13(1):55–68CrossRef
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):353–414CrossRef
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):2083–2091CrossRef
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):1785–1800CrossRef
34.
Khataee AR, Bagherzadeh Kasiri M (2011) Artificial neural network modeling of water and wastewater treatment processes. NOVA Science Publisher, Inc, Hauppauge
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):1541–1549CrossRef
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, USA
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 59:162–186CrossRef
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 62:272–299CrossRef
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, Singapore
40.
Miller J, Miller J (2010) Statistics and Chemometrics for Analytical Chemistry, 4th edn. New York, USAMATH
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):675–695CrossRef
42.
Nabavi SR (2016) Preparation conditions of asymmetric polyetherimide membrane for prevaporation of isopropanol. Chem Product Process Model 11(1):47–50
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):175–181CrossRef
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):1542–6580
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):9523–9533CrossRef
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):1542–1580
47.
Nascimento CAO, Giudici R, Guardani R (2000) Neural network based approach for optimization of industrial chemical processes. Comput Chem Eng 24(9–10):2303–2314CrossRef
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):247–256CrossRef
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):205–239CrossRef
50.
Ramteke M, Gupta SK (2009) Biomimicking altruistic behavior of honey bees in multi-objective genetic algorithm. Ind Eng Chem Res 48(21):9671–9685CrossRef
51.
Rangaiah GP (2009) Multi-objective optimization: techniques and applications in chemical engineering. World Scientific, Singapore
52.
Rangaiah GP, Bonilla-Petriciolet A (2013) Multi-objective Optimization in chemical engineering: developments and applications. John Wiley & Sons, ChichesterCrossRef
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, ChichesterCrossRef
54.
Sharma S, Rangaiah GP (2013) An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes. Comput Chem Eng 56:155–173CrossRef
55.
Sharma N, Singh K (2012) Model predictive control and neural network predictive control of TAME reactive distillation column. Chem Eng Process Process Intensif 59:9–21CrossRef
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, ChichesterCrossRef
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, Berlin
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):472–478CrossRef
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)
60.
Van Ornum SG, Champeau RM, Pariza R (2006) Ozonolysis applications in drug synthesis. Chem Rev 106(7):2990–3001CrossRef
61.
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–D200CrossRef
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):412–420CrossRef
64.
Zitzler E, Thiele L (1998) Multi-objective optimization using evolutionary algorithms: a comparative case study. In: Parallel problem solving from nature, pp 292–301
Metadata
Title
Black box modeling and multiobjective optimization of electrochemical ozone production process
Authors
Seyed Reza Nabavi
Mahmoud Abbasi
Publication date
19-06-2017
Publisher
Springer London
Published in
Neural Computing and Applications / Issue Special Issue 2/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-3057-x

Other articles of this Special Issue 2/2019

Neural Computing and Applications 2/2019 Go to the issue

Original Article

Optimum section selection procedure for horizontal axis tidal stream turbines

Original Article

Self-organizing neural networks for image segmentation based on multiphase active contour

Original Article

Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network

Original Article

An MLP-based representation of neural tensor networks for the RDF data models

Original Article

Knowledge building through optimized classification rule set generation using genetic based elitist multi objective approach

Original Article

Evolution of the size distribution of Al–B4C nano-composite powders during mechanical milling: a comparison of experimental results with artificial neural networks and multiple linear regression models

Premium Partner

    Image Credits