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Abstract
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tThe present work has focused on the modeling and simulation of a recycled ozone generator system via electrochem-ical oxidation of water. To produce ozone, a Pyrex glass electrochemical reactor, comprised of two separate half-cellby Nafion 117 membrane was applied. The applied anode and cathode electrodes were Ti/Sn-Sb-Ni and platinizedtitanium, respectively. The modeling and simulation of the reactor operation were done via artificial neural network(ANN) technique. In this regard, four important operational parameters (i.e. electrolyte concentration, applied voltage,flow rate and electrolysis time) and the generated ozone concentration were considered as the independent inputsand the network output, respectively. To find out the best model, six numbers of three-layered ANNs with differentfunctions were constructed and optimized. Best simulation was related to a model, consist of Levenberg–MarquardtBack propagation learning algorithm (trainlm) and tangent sigmoid (tansig) as transfer function in the both hiddenand output layers. Also, application of 10 hidden neurons and 80 iterations for the network calibration caused tosatisfy the network training while overfitting was prevented. The K-fold cross-validation method, employed for themodel evaluation, showed high correlation coefficient (0.9936) and low mean square error (3.58 × 10−4) for the testingdata. Sensitivity analysis indicated order of relative importance the operational parameters on the ozone productionas: time > [electrolyte] > voltage > flow rate.
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