Gurusamy Annadurai

Biosorption of Zn(II) on the different Ca-alginate beads from aqueous solution

The performance of a new biosorbent system, consisting of a fungal biomass immobilized within an orange peel cellulose absorbent matrix, for the removal of Zn(2+) heavy metal ions from an aqueous solution was tested. The amount of Zn(II) ion sorption by the beads was as follows; orange peel cellulose with Phanerochaete chrysosporium immobilized Ca-alginate beads (OPCFCA) (168.61 mg/g) > orange peel cellulose immobilized Ca-alginate beads (OPCCA) (147.06 mg/g) > P. chrysosporium (F) (125.0 mg/g) > orange peel cellulose (OPC) (108.70 mg/g) > plain Ca-alginate bead (PCA) (98.26 mg/g). The Zn(2+) concentration was 100 to 1000 mg/L. The widely used Langmuir and Freundlich isotherm models were utilized to describe the biosorption equilibrium process. The isotherm parameters were estimated using linear and non-linear regression analysis. The Box-Behnken model was found to be in close agreement with the experimental values, as indicated by the correlation coefficient value of 0.9999.

Application of artificial neural network model for the development of optimized complex medium for phenol degradation using Pseudomonas pictorum (NICM 2074)

Biodegradation of phenol using Pseudomonas pictorum (NICM 2074) a potential biodegradant of phenol was investigated for its degrading potential under different operating conditions. The neural network input parameter set consisted of the same set of four levels of maltose (0.025, 0.05, 0.075xa0g/l), phosphate (3, 12.5, 22xa0g/l), pH (7, 8, 9) and temperature (30°C, 32°C, 34°C) on phenol degradation was investigated and a Artificial Neural Network (ANN) model was developed to predict the extent of degradation. The learning, recall and generalization characteristic of neural networks was studied using phenol degradation system data. The efficiency of the model generated by the ANN, was tested and compared with the results obtained from an established second order polynomial multiple regression analysis (MRA). Further, the two models (ANN and MRA) were used to predict the percentage of degradation of phenol for blind test data. Performance of both the models were validated in the cases of training and test data, ANN was recommended based on the following higher coefficient of determination R2; lower standard error of residuals and lower mean absolute percentage deviation.