Experimental studies and neural network modeling of the removal of trichloroethylene vapor in a biofilter

Abstract

In this study, bamboo carrier based lab scale compost biofilter was evaluated to treat synthetic waste air containing trichloroethylene (TCE) under continuous operation mode. The effect of inlet TCE concentration and gas flow rate and its removal was investigated. Maximum TCE removal efficiency was found to be 89% under optimum conditions of inlet 0.986 g/m³ TCE concentration corresponding to a loading rate of 43 g/m³ h and 0.042 m³/h gas flow rate at empty bed residence time (EBRT) of 2 min. For the first time, Artificial Neural Network (ANN) was applied to predict the performance of the compost biofilter in terms of TCE removal. The ANN model used a three layer feed forward based Levenberg-Marquardt algorithm, and its topology consisted of 3-25-1 as the optimum number for the three layers (input, hidden and output). An excellent match between the experimental and ANN predicted the value of TCE removal was obtained with a coefficient of determination (R²) value greater than 0.99 during the model training, validation, testing and overall. Furthermore, statistical analysis of the ANN model performance mediated its prediction accuracy of the bioreactor to treat TCE contaminated systems.

Keywords: Artificial neural network modeling; Biodegradation; Compost biofilter; Trichloroethylene; Waste gas treatment.