Modelling and Biosorption Competence of Zinc Oxide Nanoparticle

摘要

An Artificial Neural Network (ANN) model was urbanized to forecast the biosorption competence of zincoxide nanoparticle ingrained on activated silica using Corriandrum sativum (ZNO-NPs-AS-Cs) for theamputation of whole As(III) from aqueous solution based on 95 data sets obtained in a laboratory batchstudy. 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 bringabout equilibrium. The maximum adsorption capacity of (ZNO-NPs-AS-Cs) in AS (III) removal was found tobe 3.46 g/L. The sensitivity analysis confirmed that MSE values decreased as the number of variables usedin the ANN model increased. The relative increase in the performance due to inclusion of V2, adsorbentdosage; V3, contact time; and V5, agitation speed is larger than the contribution of other variables. Theproposed ANN model provided realistic experimental data with a satisfactory correlation coefficient of 0.999for five operating variables..