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

• Published in: Clean technologies and environmental policy Vol. 26; no. 7; pp. 2137 - 2150
• Main Authors: Merouani, El Farouk Omar, Ferdowsi, Milad, Buelna, Gerardo, Jones, J. Peter, Malhautier, Luc, Heitz, Michèle, Benyoussef, El-Hadi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01-07-2024; Springer Nature B.V; Springer Verlag
• Subjects: Acclimation; Acclimatization; Artificial neural networks; Biofilters; Biofiltration; Decision support systems; Earth and Environmental Science; Environment; Environmental Economics; Environmental Engineering/Biotechnology; Environmental Sciences; Ethyl benzene; Ethylbenzene; Industrial and Production Engineering; Industrial Chemistry/Chemical Engineering; Methane; Modelling; Neural networks; Organic compounds; Original Paper; Packed beds; Sustainable Development; VOCs; Volatile organic compounds; Xylene; ANN; Dynamic; Ethylbenzene; Greenhouse gas; Xylene; VOC; Steady-state
• ISSN: 1618-954X; 1618-9558
• DOI: 10.1007/s10098-023-02706-w

This study examined the artificial neural network (ANN) modeling of simultaneous biofiltration of methane (CH
4
) 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 CH
4
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 CH
4
conversion at the pseudo-steadystate condition, while the ANN2 (4 (input layer)-18 (hidden layer)-2 (output layer)) predicted the simultaneous conversion of CH
4
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 (CH
4
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 CH
4
concentration was changed. This study developed a decision support and prediction tool to anticipate the performance of biofilters in treating residual gases containing CH
4
and VOCs, avoiding costs and delays associated with experimentation.
Graphical abstract