Modeling of phenol degradation system using artificial neural networks

Abstract

Pseudomonas pictorum (NICM-2077) an effective strain used in the biodegradation of phenol was grown on various nutrient compounds which protect the microbes while confronting shock loads of concentrated toxic pollutants during waste water treatment. In the present study the effect of glucose, yeast extract, (NH4)2SO4 and NaCl on phenol degradation has been investigated and a Artificial Neural Network (ANN) Model has been developed to predict degradation. Also the learning, recall and generalization characteristics of neural networks has been studied using phenol degradation system data. The network model was then compared with a Multiple Regression Analysis model (MRA) arrived from the same training data. Further, these two models were used to predict the percentage degradation of phenol for a blind test data. Though both the models perform equally well ANN is found to be better than MRA due to its slightly higher coefficient of correlation, lower RMS error value and lower average absolute error value during prediction.