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- Title
Artificial neural network modelling of As(III) removal from water by novel hybrid material.
- Authors
Mandal, S.; Mahapatra, S. S.; Sahu, M. K.; Patel, R. K.
- Abstract
The present study reported a method for removal of As(III) from water solution by a novel hybrid material (Ce-HAHC1). The hybrid material was synthesized by sol-gel method and was characterized by XRD, FTIR, SEM-EDS and TGA-DTA. Batch adsorption experiments were conducted as a function of different variables like adsorbent dose, pH, contact time, agitation speed, initial concentration and temperature. The experimental studies revealed that maximum removal percentage is 98.85 at optimum condition: pH = 5.0, agitation speed = 180 rpm, temperature = 60° C and contact time = 80 min using 9 gL-1 of adsorbent dose for initial As(III) concentration of 10 mg L-1. Using adsorbent dose of 10 gL-1, the maximum removal percentage remains same with initial As(III) concentration of 25 mgL-1 (or 50 mgL-1). The maximum adsorption capacity of the material is found to be 182.6 mg g-1. Subsequently, the experimental results are used for developing a valid model based on back propagation (BP) learning algorithm with artificial neural networking (BP-ANN) for prediction of removal efficiency. The adequacy of the model (BP-ANN) is checked by value of the absolute relative percentage error (0.293) and correlation coefficient (R² = 0.975). Comparison of experimental and predictive model results show that the model can predict the adsorption efficiency with acceptable accuracy.
- Subjects
ARSENIC &; the environment; SOL-gel processes; SOL-gel materials; ADSORPTION (Chemistry); ARTIFICIAL neural networks
- Publication
Process Safety & Environmental Protection: Transactions of the Institution of Chemical Engineers Part B, 2015, Vol 93, Issue Part B, p249
- ISSN
0957-5820
- Publication type
Article
- DOI
10.1016/j.psep.2014.02.016