Artificial neural network modelling of As(III) removal from water by novel hybrid material

The present study reported a method for removal of As(III) from water solution by a novel hybrid material (Ce-HAHCl). 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 g L^?1 of adsorbent dose for initial As(III) concentration of 10 mg L^?1. Using adsorbent dose of 10 g L^?1, the maximum removal percentage remains same with initial As(III) concentration of 25 mg L^?1 (or 50 mg L^?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.