Modeling the kinetics of a photochemical water treatment process by means of artificial neural networks

Chemical Engineering and Processing: Process Intensification

Volumn 38, Issue 4-6, 1999, Pages 373-382

  Göb, Sabine ^a   Oliveros, Esther ^a   Bossmann, Stefan H ^a   Braun, André M ^a   Guardani, Roberto ^b   Nascimento, Claudio A O ^b  

^a KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

^b UNIVERSITY OF SÃO PAULO   (Brazil)

Author keywords

2,4 dimethyl aniline; Advanced oxidation processes; Experimental design; Fenton reaction (photochemically enhanced); Neural networks; Water treatment

Indexed keywords

HYDROGEN PEROXIDE; IRON COMPOUNDS; MATHEMATICAL MODELS; NEURAL NETWORKS; OXIDATION; PHOTOLYSIS; REACTION KINETICS; WATER POLLUTION CONTROL;

PHOTOCHEMICAL WATER TREATMENT;

CHEMICAL WATER TREATMENT;

KINETICS; NEURAL NETWORK; OXIDATION; WASTEWATER; WATER TREATMENT;

EID: 20644466663     PISSN: 02552701     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0255-2701(99)00028-8     Document Type: Article

References (33)

1. Legrini O., Oliveros E., Braun A.M. Photochemical processes for water treatment. Chem. Rev. 93:1993;671-698.
2. Ollis D.F., Al-Ekabi H. Photocatalytic Purification and Treatment of Water and Air. 1993;Elsevier, Amsterdam.
3. Walling C. Fenton's reagent revisited. Acc. Chem. Res. 8:1975;125-131.
4. Bishop D.F., Stern G., Fleischman M., Marshall L.S. Hydrogen peroxide catalytic oxidation of refractory municipal waste waters, I&EC Proc. Design Dev. 7(1):1968;110-117.
5. Feuerstein W., Gilbert E., Eberle S.H. Modellversuche zur Oxidation aromatischer Verbindungen mittels Wasserstoffperoxid in der Abwasserbehandlung. Vom Wasser. 56:1981;35-54.
6. Barbeni M., Minero C., Pelizzetti E., Bogarello E., Serpone N. Chemical degradation of chlorophenols with Fenton's reagent. Chemosphere. 16:1987;2225-2237.
7. Lipczynska-Kochany E. Degradation of aqueous nitrophenols and nitrobenzene by means of the Fenton reaction. Chemosphere. 22(5/6):1991;529-536.
8. Pignatello J.J., Chapa G. Degradation of PCBs by ferric ion, hydrogen peroxide and UV light. Environ. Toxicol. Chem. 13:1994;423-427.
9. Safarzadeh-Amiri A., Bolton J.R., Cater S.R. The use of iron in advanced oxidation processes. J. Adv. Oxid. Technol. 1:1996;18-26.
10. Bandara J., Morrison C., Kiwi J., Pulgarin C., Péringer P. Degradation/decoloration of concentrated solutions of orange II. Kinetics and quantum yield for sunlight induced reactions via Fenton type reagents. J. Photochem. Photobiol. A: Chem. 99:1996;57-66.
11. Oliveros E., Legrini O., Hohl M., Müller T., Braun A.M. Large scale development of a light-enhanced Fenton reaction by optimal experimental design. Wat. Sci. Technol. 35(4):1997;223-230.
12. Oliveros E., Legrini O., Hohl M., Müller T., Braun A.M. Industrial waste water treatment: large scale development of a light-enhanced Fenton reaction. Chem. Eng. Proc. 36:1997;397-405.
13. Wink D.A., Nims R.W., Saavedra J.E., Utermahlen Jr W.E., Ford P.C. The Fenton oxidation mechanism: reactivities of biologically relevant substrates with two oxidizing intermediates differ from those predicted for the hydroxyl radical. Proc. Natl. Acad. Sci. USA. 91:1994;6604-6608.
14. Bossmann S.H., Oliveros E., Göb S., Siegwart S., Dahlen E.P., Payawan L. Jr., Straub M., Wörner M., Braun A.M. New evidence against hydroxyl radicals as reactive intermediates in the thermal and photochemically enhanced Fenton reactions. J. Phys. Chem. A. 102(28):1998;5542-5550.
15. Nascimento A.O., Oliveros E., Braun A.M. Neural network modelling for photochemical processes. Chem. Eng. Proc. 33:1994;319-324.
16. Braun A.M., Jakob L., Oliveros E., Nascimento C.A.O. Up-scaling photochemical reactions. Adv. Photochem. 18:1993;235-313.
17. Box G.E.P., Hunter W.G., Hunter J.S. Statistics for Experimenters: an Introduction to Design, Data Analysis and Model Building. 1978;Wiley, New York.
18. Khuri A.I., Cornell J.A. Response Surfaces, Designs and Analyses. 1987;Marcel Dekker, ASQC Quality Press, New York.
19. Droesbeke J.-J., Fine J., Saporta G. Plans d'expériences. Applications à l'entreprise. 1997;Editions Technip, Paris.
20. Eisenhauer H.R. Oxidation of phenolic wastes. J. WPCF. 36(9):1964;1116-1128.
21. Mathieu D., Phan-Tan-Luu R. Approche méthodologique des surfaces de réponse. Droesbeke J.-J., Fine J., Saporta G. Plans d'expériences. Applications à l'entreprise. 1997;Editions Technip, Paris.
22. Oliveros E., Benoit-Marquié F., Puech-Costes E., Maurette M.-T., Nascimento C.A.O. Neural network modeling of the photocatalytic degradation of 2,4-dihydroxybenzoic acid in aqueous solution. Analysis. 26:1998;326-332.
23. D.E. Rumelhart, G.E. Hinton, R.J. Williams, in: J.L. McClelland (Ed.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol.1, Foundations D.E. Rumelhart, Bradford Books, The MIT Press, Cambridge, MA, 1986 (Chapter 1).
24. Zupan J., Gasteiger J. Neural Networks for Chemists: an Introduction. 1993;VCH, Weinheim.
25. Chan W.M., Nascimento C.A.O. Use of neural networks for modeling of olefin polymerization in high pressure tubular reactors. J. Appl. Pol. Sci. 53:1994;1277-1289.
26. C.A.O. Nascimento, R. Giudici, N. Scherbakoff, Modeling of industrial nylon-6,6 in a twin-screw extruder reactor. II. Neural networks and hybrid models, J. Appl. Pol. Sci., 72 (1999) 905-912.
27. Nascimento C.A.O., Giudici R. Neural network based approach applied to an industrial nylon-6,6 polymerisation process. Comp. Chem. Eng. 22:1998;595-600.
28. Nascimento C.A.O., Guardani R., Giulietti M. Use of neural networks in the analysis of particle size distributions by laser diffraction. Powder Technol. 80(1):1997;89-94.
29. 2-photocatalytic treatment of water. Reactor design and optimization experiments. Ollis D.F., Al-Ekabi H. Photocatalytic Purification and Treatment of Water and Air. 1993;511-532 Elsevier, Amsterdam.
30. Guardani R., Nascimento C.A.O., Guardani M.L.G., Martins M.H.R.B., Romano J. Study of atmospheric ozone formation by means of a neural network based model. J. Air Waste Manag. Assoc. 49:1998;174-185.
31. Bhat N.V., McAvoy T. Use of neural nets for dynamic modeling and control of chemical process systems. Comp. Chem. Eng. 14(4/5):1990;573-583.
32. Su H.T., McAvoy T., Werbos P. Long term predictions of chemical processes using recurrent neural networks: a parallel training approach. Ind. Eng. Chem. Res. 31:1992;1338-1352.
33. Pollard J.D., Broussard M.R., Garrison D.B., San K.Y. Process identification using neural networks. Comp. Chem. Eng. 16(4):1992;253-258.