Application of artificial neural network for prediction of fluoride removal efficiency using neutralized activated red mud from aqueous medium in a continuous fixed bed column

Abstract

The present research work approaches the removal of uoride from aqueous medium using neutralized activated red mud (NARM) in a continuous xed bed column. Arti cial neural network (ANN) technique was applied effectively for optimization of the model for the practicability of the removal process. The consequences of various experimental variables like bed length, adsorbate concentration, experimental time and adsorbate solution ow rate studied to know the breakthrough point and saturation times. The highest removal potentiality of NARM was considered to be 3.815 mg g − 1 of F − in the bed height of 15 cm, starting concentration 1ppm, susceptible time 120 minutes, adsorbate solution ow rate 0.5 mLmin − 1 , and constant room temperature, respectively. Bohart-Adams and Thomas models were considered to describe the xed bed column effect to the bed height and adsorbate concentrations. The experimental data were applied a back propagation (BP) learning algorithm programme with four-seven-one architecture model. The arti cial neural network model was considered to be functioning correctly as absolute relative percentage error, which is 0.671 throughout the learning period. Differentiation between the predicted out comes from ANN model and actual results from experimental analysis affords a high degree of correlation (R 2 = 0.998) stipulating that the model was able to predict the adsorption e ciency. Experimented adsorbent materials was characterized using different instrumental analysis that is SEM-EDS, FTIR and XRD.

Green clay materials applied in packed bed columns for adsorption of hexavalent chromium from aqueous solutions

“…Continuous adsorption using different materials is an emerging alternative method due to its cost-effectiveness, simplicity in design and operation, capability for removal at low concentrations, low sludge production, and environmental friendliness (Patel, 2019;Giri and, Mishra, 2022;Gholamifard et al, 2023;Subair et al, 2024).…”