Abdulkader, J.A.M. and Emmanuel, B. (2002) Neural network to evaluate rotating cylinder electrode: performance for metal power deposition. Transactions of the SAEST, 37 (03). pp. 145-148.

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The parameters that influence the performance of rotating cylinder electrodes (RCE), a well defined high mass transfer device, are studied using neural network. The electrochemical reaction chosen is the cathodic deposition of copper in powder from acidic solution. The input parameters to the neural network are cathode current density, copper concentration and temperature in the ranges of 389 to 833 A m2, 1 to 3 dm-3 & 35-45 C, respectively. The outputs of the network are current efficiency and space time yield (kg of product per hour, per unit volume of cell). The response curves generated shows the influence of the parameters. The prediction from the network for unknown inputs is compared with the experimental value

Item Type: Article
Uncontrolled Keywords: Rotating cylinder electrode; Neural networks; Copper powder
Subjects: Electrodeposition
Theoretical Electrochemistry
Depositing User: ttbdu cecri
Date Deposited: 05 Apr 2012 10:32
Last Modified: 05 Apr 2012 10:32
URI: http://cecri.csircentral.net/id/eprint/2455

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