The integration of optimization techniques and deep learning
models,
which offer a promising avenue for improving the efficiency and sustainability
of biodiesel production processes from baobab seed oil (BSO), is rare.
This study utilized a multi-input-multioutput (MIMO) deep learning
technique and the most recent central composite design (CCD) optimization
tool to model and optimize the yield and properties of biodiesel produced
from BSO. First, the baobab seed oil was extracted using a solvent
extraction method. BSO was subsequently analyzed and converted to
biodiesel by reacting CH3OH catalyzed by waste banana bunch
stalk biochar activated by KOH. Multiobjective optimization and prediction
of the biodiesel yield (Y) and several key fuel properties,
including the cetane number (CN), kinematic viscosity (VS), and purity
(P), were achieved. With better correlation coefficients
of 0.9709, 0.9464, and 0.9714 for response training, response testing,
and response validation, respectively, and a root-mean-square error
of 0.00755, the MIMO model on the logsig transfer function accurately
predicted the biodiesel yield and properties more than did the MISO
and response surface methodology models. The optimum Y (96 wt %), CN (48), VS (3.3 mm2/s), and P (98.3%) were concurrently accomplished at a reaction temperature
of 56 °C, a reaction time of 115 min, a CH3OH/BSO
molar ratio of 15:1, a catalyst dosage of 6 wt %, and a stirring speed
of 400 rpm with 98% optimal validation accuracy. CCD sensitivity analysis
revealed that the CH3OH/BSO ratio was the most sensitive
(50.9%) input predictor among the other input variables studied.