Gas-phase trichloroethylene removal by Rhodococcus opacus using an airlift bioreactor and its modeling by artificial neural network.

This study evaluated the biological removal of trichloroethylene (TCE) by Rhodococcus opacus using airlift bioreactor under continuous operation mode. The effect of inlet TCE concentration in the range 0.12-2.34 g m-3 on TCE removal has studied for 55 days. During the continuous bioreactor operation, a maximum of 96% TCE removal was obtained for low inlet TCE concentration, whereas the highest elimination capacity was 151.2 g m-3 h-1 for the TCE loading rate of 175.0 g m-3 h-1. The carbon dioxide (CO2) concentration profile from the airlift bioreactor revealed that the degraded TCE has primarily converted to CO2 with a fraction of organic carbon utilized for bacterial growth. The artificial neural network (ANN) based model was able to successfully predict the performance of the bioreactor system using the Levenberg-Marquardt (LM) back propagation algorithm, and optimized biological topology is 3:12:1. The prediction accuracy of the model was high as the experimental data were in good agreement (R2 = 0.9923) with the ANN predicted data. Overall, from the bioreactor experiments and its ANN modeling, the potential strength of R. opacus in TCE biodegradation is proved.

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

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.

Biodegradation of gaseous toluene with mixed microbial consortium in a biofilter: steady state and transient operation.

Petroleum oil refineries are massive emitters of risky volatile organic compounds (VOCs). Among the VOCs, toluene is taken into account as a significant pollutant. In the present study, a compost biofilter is used to treat the toluene vapor. However, an elimination capacity and removal efficiency of the biofilter was investigated for a wide range of toluene concentrations (0.29-3.8 g m-3) and operated for 54 days effectively. Elimination capacity of 93 g m-3 h-1 was recorded as maximum value at a toluene inlet concentration of 114 g m-3 h-1. An elimination capacity was perpetually better at the lower section of the biofilter, and therefore, the value was around 40-60 g m-3 h-1. The high removal efficiency of 97% was obtained at inlet toluene load of 60.55 g m-3 h-1. Hence, the biofilm was quite sensitive to handling transient loading conditions. The pressure drop had no vital impact on the biofilter performance. An Ottengraf model was applied to all phase of biofilter operation in each of the diffusion limiting region and reaction limiting region. The parameters of the model K 1 (75.95 g1/2 m-3/2 h-1) and K 0 (90.51 g m-3 h-1) were obtained from diffusion and reaction limiting region severally. However, K 1 was used to calculate the theoretical elimination capacities, and therefore, K 0 was used to discover the biofilm thickness. By the way, the average biofilm thickness was found to be 0.98 mm from reaction limiting region.