Prediction of removal percentage and adsorption capacity of activated red mud for removal of cyanide by artificial neural network. Issue 5 (3rd September 2018)

• Record Type: Journal Article
• Authors: Deihimi, Nazanin; Irannajad, Mehdi; Rezai, Bahram
• Is Part Of: Geosystem engineering. Volume 21:Issue 5(2018)
• Page Start: 273
• Page End: 281
• Publication Date: 2018-09-03
• Subjects: Activated red mud -- ferrocyanide -- ferricyanide -- artificial neural network -- prediction; Mining engineering -- Periodicals; Petroleum engineering -- Periodicals; Gas engineering -- Periodicals; Geology, Economic -- Periodicals; 620
• DOI: 10.1080/12269328.2018.1424042
• Languages: English
• ISSNs: 1226-9328

Abstract

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.