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• W2790118452 abstract "In this study, the activated red mud was used as a new and appropriate adsorbent for the removal of ferrocyanide and ferricyanide from aqueous solution. Predicting the removal percentage and adsorption capacity of ferro-ferricyanide by activated red mud during the adsorption process is necessary which has been done by modeling and simulation. The artificial neural network (ANN) was used to develop new models for the predictions. A back propagation algorithm model was trained to develop a predictive model. The effective variables including pH, absorbent amount, absorbent type, ionic strength, stirring rate, time, adsorbate type, and adsorbate dosage were considered as inputs of the models. The correlation coefficient value (R 2) and root mean square error (RMSE) values of the testing data for the removal percentage and adsorption capacity using ANN models were 0.8560, 12.5667, 0.9329, and 10.8117, respectively. The results showed that the proposed ANN models can be used to predict the removal percentage and adsorption capacity of activated red mud for the removal of ferrocyanide and ferricyanide with reasonable error." @default.
• W2790118452 created "2018-03-29" @default.
• W2790118452 creator A5044671263 @default.
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• W2790118452 creator A5080765237 @default.
• W2790118452 date "2018-02-08" @default.
• W2790118452 modified "2023-09-26" @default.
• W2790118452 title "Prediction of removal percentage and adsorption capacity of activated red mud for removal of cyanide by artificial neural network" @default.
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• W2790118452 doi "https://doi.org/10.1080/12269328.2018.1424042" @default.
• W2790118452 hasPublicationYear "2018" @default.
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