Simulation and Optimization of Artificial Neural Network Modeling for Prediction of Sorption Efficiency of Nanocellulose Fibers for Removal of Cd (II) Ions from Aqueous System

Abstract

Simulation and optimization of an Artificial Neural Network (ANN) for modeling biosorption studies of cadmium removal using nanocellulose fibers (NCFs) was carried out. Experimental studies led to the standardization of the optimum conditions for the removal of cadmium ions i.e. biomass dosage (0.5 g), test volume (200 ml), metal concentration (25 mg/l), pH (6.5) and contact time (40 min). A Single layer ANN model was developed to simulate the process and to predict the sorption efficiency of Cd (II) ions using NCFs. Different NN architectures were tested by varying network topology, resulting in excellent agreement between experiment outputs and ANN outputs. The findings indicated that ANN provided reasonable predictive performance for training, cross validation and testing data sets (R2 = 0.998, 0.995, 0.992). A sensitivity analysis was carried out to assess the influence of different independent parameters on the biosorption efficiency, and pH > biomass dosage > metal concentration > contact time > test volume were found to be the most significant factors. Simulations based on the developed ANN model can estimate the behavior of the biosorption phenomenon process under different experimental conditions.doi:10.14456/WJST.2014.4