2 research outputs found
Molecular-Based Bayesian Regression Model of Petroleum Fractions
Molecular
reconstruction of petroleum fractions is for determining
the detailed molecular compositions in the mixture from a few measurable
bulk properties, e.g., density, Reid vapor pressure (RVP), molecular
weight, and ASTM boiling point curves etc., which is a great challenge
because the number of hydrocarbon compounds is much larger than that
of the bulk properties. In this paper, a novel molecular reconstruction
method is developed which includes two Bayesian regression models
for bulk properties’ prediction and molecular reconstruction.
By defining a characteristic function of bulk property and then establishing
its general mixing rule with respect to compositions, the bulk property
is predicted from a linear regression model with sigmoidal basis functions
whose parameters can be estimated by maximizing a posterior distribution
from a well-determined database containing bulk properties and molecular
information on petroleum fraction samples. Furthermore, by developing
a prior distribution of the molecular information with an assumption
that the compounds in the hydrocarbon mixture have an independently
and identically distributed (iid) gamma distribution and combining
the likelihood function used in bulk properties’ prediction,
the molecular information is thus reconstructed by maximizing a new
posterior distribution. Case studies of naphtha fractions demonstrate
the effectiveness of the proposed method
MOESM3 of Tumor-infiltrating CD4+ T cells in patients with gastric cancer
Additional file 3: Figure S3. Effects of PD-1+ and Tim-3+ inhibition on IFN-ĂŽĹ‚ induction. (A) Flow cytometry results; (B) IFN-ĂŽĹ‚ induction on CD4+ cells