ARTIFICIAL NEURAL NETWORK APPROACH FOR MODELING OF NI(II) ADSORPTION FROM AQUEOUS SOLUTION USING AEGEL MARMELOS FRUIT SHELL ADSORBENT

The rapid increase in population and growth of industrialization worldwide has resulted in deterioration in quality of water. Industrial processes have introduced substantial amounts of potentially toxic heavy metals into the environment. Which are non-biodegradable and harmful to the human health. Adsorption is found to be promising technique for removal of heavy metal ions from aqueous solution. Due to the complexity and nonlinearity of the behavior of the adsorption processes, the conventional mathematical models correlating the governing adsorption parameters In present work Artificial neural networks have been applied for modeling applications related to adsorption studies. The adsorption of Ni (II) ions from aqueous solution is 81-88% depending upon process condition. Two ANN models S-1 & S-2 are developed for the estimation of concentration of Ni (II) ions from aqueous solution based on the correlation between the optical densities of the solutions as determined by the digital colorimeter with the Ni (II) ion concentration present in the solution. Three ANN models NS, NM & NC are developed for adsorption studies. The present work could highlight the novel feature of the ANN model in estimation of concentration of Ni (II) ions present in aqueous solution. It is indicative & representative of several possible applications of artificial neural network modeling in the area of adsorption involving several possible combinations of adsorbents & adsorbents.