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.