Ten steps modeling of electrolysis processes by using neural networks

  • Authors:

  • C. G. Piuleac;M. A. Rodrigo;P. Cañizares;S. Curteanu;C. Sáez

  • Affiliations:

  • Department of Chemical Engineering, Faculty of Chemical Engineering and Environmental Protection, "Gh. Asachi" Technical University Iasi, Bd. D. Mangeron, No. 71A, 700050, Iasi, Romania;Department of Chemical Engineering, Faculty of Chemistry, Universidad de Castilla La Mancha, Campus Universitario s/n., 13071 Ciudad Real, Spain;Department of Chemical Engineering, Faculty of Chemistry, Universidad de Castilla La Mancha, Campus Universitario s/n., 13071 Ciudad Real, Spain;Department of Chemical Engineering, Faculty of Chemical Engineering and Environmental Protection, "Gh. Asachi" Technical University Iasi, Bd. D. Mangeron, No. 71A, 700050, Iasi, Romania;Department of Chemical Engineering, Faculty of Chemistry, Universidad de Castilla La Mancha, Campus Universitario s/n., 13071 Ciudad Real, Spain

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2010

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Abstract

Neural networks have been developed to model the electrolysis of wastes polluted with phenolic compounds, including phenol, 4-chlorophenol, 2,4-dichlorophenol, 2,4,6-trichlorophenol, 4-nitrophenol and 2,4-dinitrophenol. They enable the prediction the Chemical Oxygen Demand of a treated waste as a function of the initial characteristics (pollutant concentration, pH), operation conditions (temperature, current density) and current charge passed. A consistent set of experimental data was obtained by electrochemical oxidation with conductive diamond electrodes, used to treat synthetic aqueous wastes. Several modeling strategies based on simple and stacked neural networks, with different transfer functions into the hidden and output layers, have been considered to obtain a good accuracy of the model. Global errors during the training stage were under 3% and those of the validation stage were under 4%, demonstrating that the neural network based technique is appropriate for modeling the system. The generalization capability of the neural networks was also tested in realistic conditions where Chemical Oxygen Demand was predicted with errors around 5%. Therefore, the developed neural models can be used in industry to determine the required treatment period, to obtain the discharge limits in batch electrolysis processes, and it is a first step in the development of process control strategies. The ten step methodology was applied to the neural network based process modeling.