The waste acid generated in the production of expanded graphite by"chromium method"has the charac-teristics of high acid concentration and high chromium content.The Cr(Ⅲ)can be oxidized to Cr(Ⅵ)by electro-oxidation in membrane system to realize the regeneration of waste acid containing chromium.Since it was difficult to achieve real-time detection of Cr(Ⅵ)content in this highly acidic system,a study based on artificial neural network was conducted to accurately predict the electro-oxidation regeneration effect of chromium-containing waste acid.Based on the regeneration of chromium-containing waste acid experiments,the key characteristic parameters of hexavalent chromium regeneration including time,sulfuric acid concentration,and electrolyte volume were deter-mined by correlation analysis.Then,through hyperparameter optimization,the relatively optimal topology structure of the artificial neural network was obtained as follows:Neurons=35,Batch size=30,Layers=4.The coefficient of de-termination(R2)between predicted value and experimental value was greater than 0.97,and the root-mean-square er-ror(RMSE)was less than 0.04.Finally,the average relative error between predicted value and experimental value was 0.14%,which indicated that the model had good generalization ability.The artificial neural network model over-came the difficulty of predicting electrochemical processes due to multi-parameter,nonlinearity and time variability,and could realize the prediction of Cr(Ⅵ)regeneration under complex mapping conditions,which was of great signifi-cance for the optimization and control of electrochemical processes.
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