It
is well-known that reservoir hydrocarbon fluids contain heavy paraffins
that may form solid phases of wax at low temperatures. Problems associated
with wax formation and deposition are a major concern in production
and transportation of hydrocarbon fluids. Thus, testing of wax disappearance
temperature (WDT) is essential in high-efficiency development of crude
oil. For the sake of reduction of time and improvement of accuracy,
four metaheuristic models called gray wolf optimizer-based support
vector machine (GWO-SVM), least-squares support vector machine, genetic
algorithm-based adaptive network-based fuzzy inference system, and
particle swarm optimization-based adaptive network-based fuzzy inference
system were used for the prediction of WDT in
binary, ternary, and multicomponent systems in the range of 0.1–100
MPa. The input parameters are molar mass and pressure, and the output
is the WDT at every point. The comparison between the four models
shows that the GWO-SVM gets the best accordance with experimental
data sets with the minimum average absolute relative deviation (AARD
= 0.7128%), maximum determination coefficient (R
2 = 0.9546), and minimum root-mean-squared error (RMSE = 2.4208)
in all 272 data points. And outliers detection using
the leverage approach to detect the doubt points, where only 6 data
points in all 272 data points.
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