Abstract:
Co-gasification is one of the most promising thermochemical conversion technologies for
biomass derived from agricultural residue, and the co-gasification treatment of agricultural
residue can effectively enhance waste management systems and green energy production. In
the present study, the co-gasification of corncob, coconut coir, and coconut shell biomass in a
fixed-bed gasifier is simulated using the Aspen Plus software, and the model is validated using
the previously published literature. The present model investigates the effects of temperature
(500-1200℃), pressure (1-20 bar), steam-to-biomass ratio (SBR) (0.2-1), and Equivalence ratio
(ER) (0.1-0.6) on syngas composition, cold gas efficiency (CGE), and lower heating value
(LHV) of syngas. H2 and CO production increases with the increase in temperature and
decreases with pressure and ER, whereas CO2 shows the opposite trend. A higher steam-to
biomass ratio enhances the production of H2 and CO2. However, the amount of CO reduced
sharply with higher SBR. The LHV of syngas decreases with the increase in temperature, SBR,
and ER. However, an increase in pressure increases the LHV. In this study, Two Machine
learning models were developed to optimize the operating conditions of the co-gasification
process. The random forest (RF) and the gradient boosting regression (GBR) model are
considered. The result shows that the GBR model has better accuracy(R2 ≥0.99 and RMSE
≤2.66) for the optimization of the operating conditions. The model showed that maximum H2
(54.13%), CO (29.52%), CGE (90.30%), and LHV (9.57 MJ/m3), and minimum CO2 (14.97%)
produced at the operating conditions of 850℃, 1 bar pressure, SBR of 0.2, and blend ratio of
corncob, coconut coir, and coconut shell of (1:1:2). This research investigated the potential of
biomass in Bangladesh and the energy content in biomass, which encourages the commercial
use of co-gasification technology and helps rural communities manage their waste sustainably
and efficiently.
