Modelling and Biosorption Competence of Zinc Oxide Nanoparticle

An Artificial Neural Network (ANN) model was urbanized to forecast the biosorption competence of zinc oxide nanoparticle ingrained on activated silica using Corriandrum sativum (ZNO-NPs-AS-Cs) for the amputation of whole As(III) from aqueous solution based on 95 data sets obtained in a laboratory batch study. Experimental parameters affecting the biosorption progression such as initial concentration, dosage, pH, contact time and agitation were premeditated. A contact time of 90 min was generally passable to bring about equilibrium. The maximum adsorption capacity of (ZNO-NPs-AS-Cs) in AS (III) removal was found to be 3.46 g/L. The sensitivity analysis confirmed that MSE values decreased as the number of variables used in the ANN model increased. The relative increase in the performance due to inclusion of V₂, adsorbent dosage; V₃, contact time; and V₅, agitation speed is larger than the contribution of other variables. The proposed ANN model provided realistic experimental data with a satisfactory correlation coefficient of 0.999 for five operating variables.