Optimization of caustic current efficiency in a zero-gap advanced chlor-alkali cell with application of genetic algorithm assisted by artificial neural networks

The effects of various process parameters on caustic current efficiency (CCE) in a zero-gap oxygen-depolarized chlor-alkali cell employing a state-of-the-art silver plated nickel screen electrode (ESNS ®) were studied. For doing a thorough research, we selected the process parameters from both catho...

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Bibliographic Details
Published in:Chemical engineering journal (Lausanne, Switzerland : 1996) Switzerland : 1996), 2008-07, Vol.140 (1), p.157-164
Main Authors: Mirzazadeh, T., Mohammadi, F., Soltanieh, M., Joudaki, E.
Format: Article
Language:English
Subjects:
Advanced chlor-alkali (ACA)
Applied sciences
CCE
Chemical engineering
ESNS
Exact sciences and technology
Neural networks
Process parameters
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Summary:The effects of various process parameters on caustic current efficiency (CCE) in a zero-gap oxygen-depolarized chlor-alkali cell employing a state-of-the-art silver plated nickel screen electrode (ESNS ®) were studied. For doing a thorough research, we selected the process parameters from both cathodic and anodic compartments. Seven process parameters were studied including anolyte pH, temperature, flow rate and brine concentration from the anode side, oxygen temperature and flow rate from the cathode side and the applied current density. The effect of these parameters on CCE was determined quantitatively. A feed forward neural network model with the Levenberg–Marquardt (LM) back propagation training method was developed to predict CCE. Then genetic algorithm (GA) was implemented to neural network model. The highest CCE (98.53%) was found after 20 times running GA at the following conditions: brine concentration (287 g/L), anolyte temperature (80 °C), anolyte pH (2.7), anolyte flow rate (408 cm 3/min), oxygen flow rate (841 cm 3/min), oxygen temperature (79 °C), and current density (0.33 A/cm 2).
ISSN:1385-8947
1873-3212
DOI:10.1016/j.cej.2007.09.028