AI Article Synopsis
- The study investigated how effectively a biotrickling filter (BTF) removes hydrogen sulfide (H), methanol (M), and α-pinene (P) using artificial neural networks (ANNs) to model the process.
- The optimal ANN configuration for the model was found to be 5-8-3, with the removal efficiency (RE) of M being significantly influenced by its concentration, while the removal of P and H depended more on the operational time of the BTF.
- Short-term shock-loads from individual pollutants did not greatly impact their removal rates, but α-pinene removal was reduced by 50% when exposed to shock-loads, showcasing the system's adaptability to sudden changes.
Article Abstract
The removal efficiency (RE) of gas-phase hydrogen sulfide (H), methanol (M) and α-pinene (P) in a biotrickling filter (BTF) was modeled using artificial neural networks (ANNs). The inlet concentrations of H, M, P, unit flow and operation time were used as the model inputs, while the outputs were the RE of H, M and P, respectively. After testing and validating the results, an optimal network topology of 5-8-3 was obtained. The model predictions were analyzed using Casual index (CI) values. M removal in the BTF was influenced positively by the inlet concentration of M in mixture (CI=3.79), while the removal of P and H were influenced more by the time of BTF operation (CI=25.36, 15.62). The BTF was subjected to different types of short-term shock-loads: 5-h shock-load of HMP mixture simultaneously, and 2.5-h shock-load of either H, M, or P, individually. It was observed that, short-term shock-loads of individual pollutants (M or H) did not significantly affect their own removal, but the removal of P was affected by 50%. The results from this study also show the sensitiveness of the well-acclimated BTF to handle sudden load variations and also revival capability of the BTF when pre-shock conditions were restored.
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| http://dx.doi.org/10.1016/j.jhazmat.2013.11.023 | DOI Listing |
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