1Department of Chemical Engineering, Annamalai University, India.
2Department of Civil Engineering, Indian Institute of Technology Madras, India.
3Department of Chemical Engineering, Indian Institute of Technology Madras, India.
*Corresponding Author Email: lect_ravi@yahoo.com
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Dichloromethane (DCM) is one of the most important hazardous air pollutants implicated in both atmospheric pollution and health effects. With its large scale use as a solvent and as a process chemical, its effective removal has become mandatory. Biofiltration is a versatile waste gas and odor treatment technology that has gained much acceptance in the recent years to treat Volatile Organic Compounds (VOCs).
This paper presents the results of performance evaluation study using a compost based biofilter unit inoculated with a mixed microbial population to treat dichloromethane vapors from a synthetic gas stream. The biofilter was operated continuously for a period of 14 months at different flow rates (0.024 to 0.06 m3hr–1) corresponding to residence time of 2.4 to 1 min with dichloromethane concentrations ranging from 0.15 to 1.1 g m– 3.
Though batch acclimated mixed culture was used as an inoculum for the biofilter, it took nearly 135 days for acclimation. There was visible biofilm formation and a shift in the removal mechanism from adsorption to biodegradation. During the operational phase, the removal efficiency (RE) remained reasonably high (60 – 80%) for inlet DCM loading rates up to 30 g/m3h but at higher loads it dropped drastically.to 25%. The elimination capacity (EC) increased linearly to reach a maximum of 20.1 at g/m3h an inlet loading of 30 g/m3h. It remained constant till an inlet loading of 40 g/m3h but decreased at higher loadings. The macro kinetics of pollutant removal can be explained in terms of the different rate limiting steps involved as the inlet load is increased. The maximum pressure drop in the biofilter was 3.5 cm H2O.
The relationships between the operational parameters (inlet concentration and flow rate) and response parameters of the biofilter namely removal efficiency and elimination capacity were identified to be complex, which envisioned us to make an attempt to predict the performance using statistically significant data driven models. Predictions based on multiple regression analysis models using part of the data for model fitting and the rest for model evaluation were performed and compared using correlation co-efficient R2 as goodness of fit.. A very high level of R2 (0.983) showed the good predictability of the model.
Biofiltration, Compost, dichloromethane, Loading rate, Elimination Capacity, regression analysis
