Prediction of Cell Voltage and Chlorine Current Efficiency of Aqueous HCl Electrolysis Utilizing an Oxygen Reducing Cathode Based on Artificial Neural Network
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Published:2010-02-22
Issue:33
Volume:25
Page:27-42
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ISSN:1938-5862
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Container-title:ECS Transactions
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language:
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Short-container-title:ECS Trans.
Author:
Ashrafizadeh Seyed Nezamedin,Mohammadi Fereydoon,Sattari Abolfazl,Shojaikaveh Narjes
Abstract
The effects of different process parameters on the cell voltage and chlorine current efficiency (ChCE) of the HCl membrane electrolysis were thoroughly studied and artificial neural network (ANN) models were developed for the prediction of them. A Half-MEA oxygen reducing membrane electrolysis cell with a projected area of 10 cm2 employing a dimensionally stable anode (DSA®) was employed to study the process. The cell voltage was increased with current density while this relation occurred in an opposite direction for other process parameters meanwhile the ChCE was decreased with current density and increased with other process parameters. 40 data (75%) and 13 data (25%) of total 53 distinct experiments were used to train and test the networks respectively. The predicted cell voltages and ChCEs using ANN modeling were found to be very close to the measured values with an average deviation of about 3.45% & 7.18% for test validation data, respectively.
Publisher
The Electrochemical Society
Cited by
2 articles.
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