The
objective of this research work was to utilize machine learning
tools for predicting the yield and hydrogen contents of bio-oil (H-bio-oil)
based on biomass compositions of feedstock and pyrolysis conditions.
In this regard, multiple linear regression (MLR) and random forest
(RF) method was successfully applied and compared. The results verified
RF’s larger feasibility than MLR for predicting bio-oil yield
and H-bio-oil. Moreover, the profound information behind the model
was extracted. The compositions of feedstock exerted more influences
on both yield (60%) and H-bio-oil (77%). Besides, the proximate analysis
information was preferable to determine yield, which was inverse for
H-bio-oil. The modes of each variable affecting yield and H-bio-oil
were described by partial dependence analysis. This research provided
a reference for upgrading the bio-oil and extended the knowledge into
biomass pyrolysis process.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.