Experimental assessment and artificial neural network modeling of dynamic and steady-state methane biofiltration in the presence of volatile organic compounds.

This study examined the artificial neural network (ANN) modeling of simultaneous biofiltration of methane (CH₄) with two volatile organic compounds (VOCs): xylene and ethylbenzene, using an inorganic packed bed biofilter at an empty bed residence time (EBRT) of 4.5 min. Results showed that the removal efficiency (RE) of CH₄ was in the range of 50 to 60% for concentrations of 1000 to 10,000 ppmv (0.6 to 6.5 g m⁻³), while the VOCs-REs were between 70 and 90% for X and EB concentrations in the range of 200 to 500 ppmv (0.9 to 2.2 g m⁻³). Artificial neural networks were used to predict and simulate the performances of the biofilter, based on a database containing previous biofiltration works. The ANN1 (architecture of 3 (input layer)-18 (hidden layer)-1 (output layer)) accurately predicted CH₄ conversion at the pseudo-steadystate condition, while the ANN2 (4 (input layer)-18 (hidden layer)-2 (output layer)) predicted the simultaneous conversion of CH₄ and VOCs with slightly lower accuracy than ANN1. The ANN3 (4 (input layer)-30 (hidden layer)-1 (output layer)) successfully predicted the acclimation period and final phase (CH₄ concentration of 10,000 ppmv) of the biofilter but could not accurately predict the transient phases and showed differences (up to 20%) from experimental results once the CH₄ concentration was changed. This study developed a decision support and prediction tool to anticipate the performance of biofilters in treating residual gases containing CH₄ and VOCs, avoiding costs and delays associated with experimentation. [ABSTRACT FROM AUTHOR]