Performance assessment of gas-phase toluene removal in one- and two-liquid phase biotrickling filters using artificial neural networks

摘要

The main aim of this work is to study gas-phase toluene removal in one- and two-liquid phase bio-trickling filters (O/TLP-BTF) and model the BTF performance using artificial neural networks (ANNs). The TLP-BTF was operated for 60 din the presence of silicone oil at empty bed residence times (EBRTs) of 120, 60, and 45 s, respectively, and toluene concentrations in the range of 0.9-3.1 gm(-3). A t-test analysis indicated that increasing the silicone oil volume ratio from 5 to 10% v/v, did not significantly improve the TLP-BTF performance (p-value = 0.65 > 0.05). The results from ANN modeling showed that toluene removal was more negatively affected by the inlet concentration (casual index, CI = 5.63) due to the kinetic limitation. The CI values for inlet concentration (+4.01) and liquid trickling rate (-2.45) indicated that the diffusion-limited regime controlled the removal process in the OLP-BTF. (C) 2019 Elsevier Ltd. All rights reserved.