Performance evaluation of fungal biotrickling filter for styrene destruction: Experimental and artificial neural networks modeling

Removal of styrene vapors was investigated using a fungi-cultured biotrickling filter (BTF) in the presence of rhamnolipid. Evaluations at empty bed residence times (EBRTs) of 90, 60, and 30 s and inlet loading rates (LRs) ranging from 25.5 to 186.1 g m −3 h −1 were conducted. The maximum elimination capacity (EC) of 173.7 g m −3 h −1 was obtained at an inlet LR of 186.1 g m −3 h −1 at 90 s EBRT. Reducing the EBRT to 60 and 30 s resulted in decline of removal efficiency (RE). The performance of BTF was modeled using artificial neural network (ANN) to predict styrene RE using measurable inputs, inlet LR, EBRT, and pressure drop. The model performance was assessed by mean square error and overall coefficient of correlation. The influence of different input parameters on the output was analyzed using casual index. Styrene removal was positively influenced by the increase in EBRTs, negatively impacted by its inlet LRs, while pressure drop had a negligible effect . The BTF was then exposed to intermittent loading phases by varying operational conditions during the non-loading periods. The results from this study confirmed that rhamnolipids could enhance the BTF performance for handling transient load variations at unsteady-state conditions.