Machine learning based estimation of density of binary blends of cyclohexanes in normal alkanes

Given the application of cycloalkanes in surrogate blends for aviation fuels, their determination of critical characteristics pertinent to fuel transportation and combustion becomes imperative. In this study, we aim to construct intelligent models based on machine learning methods of random forest (RF), adaptive boosting, decision tree (DT), ensemble learning, K-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP) artificial neural network and convolutional neural network (CNN) to predict the density of binary blends of ethylcyclohexane or methylcyclohexane with n-hexadecane/n-dodecane/n-tetradecane in terms of operational conditions (pressure and temperature) and cycloalkane mole fractions in n-alkanes, utilizing laboratory data extracted from existing scholarly publications. The reliability of the data used is affirmed using an outlier detection algorithm, and the relevancy factor concept is utilized to find the relative effects of the input parameters on the output parameter. The preciseness of the developed models is checked and compared comprehensively via statistical and graphical methods. The obtained results indicate that temperature has the most effect on density, with a relevancy value of - 0.9619, and pressure has the least effective value, with a relevancy value of 0.041, which explains the straight relationship between pressure and density. The modeling results illustrate that DT and RF algorithms have the best performance in calculating density with R² values of 0.9985 and 0.09982, respectively. The MLP and Adaboosting models exhibit the weakest performance in this field, with R² values of 0.9455 and 0.9477, respectively. The current paper indicates robust tools for the accurate prediction of the density of binary blends of ethyl cyclohexane or methylcyclohexane with n-hexadecane/n-dodecane/n-tetradecane, which are required for fuel transportation and combustion studies.