Both the conversion of lignocellulosic biomass to bio-oil
(BO)
and the upgrading of BO have been the targets of many studies. Due
to the large diversity and discontinuity seen in terms of reaction
conditions, catalysts, solvents, and feedstock properties that have
been used, a comparison across different publications is difficult.
In this study, machine learning modeling is used for the prediction
of final higher heating value (HHV) and ΔHHV for the conversion
of lignocellulosic feedstocks to BO, and BO upgrading. The models
achieved coefficient of determination (R
2) scores ranging from 0.77 to 0.86, and the SHapley Additive exPlanations
(SHAP) values were used to obtain model explainability, revealing
that only a few experimental parameters are largely responsible for
the outcome of the experiments. In particular, process temperature
and reaction time were overwhelmingly responsible for the majority
of the predictions, for both final HHV and ΔHHV. Elemental composition
of the starting feedstock or BO dictated the upper possible HHV value
obtained after the experiment, which is in line with what is known
from previous methodologies for calculating HHV for fuels. Solvent
used, initial moisture concentration in BO, and catalyst active phase
showed low predicting power, within the context of the data set used.
The results of this study highlight experimental conditions and variables
that could be candidates for the creation of minimum reporting guidelines
for future studies in such a way that machine learning can be fully
harnessed.