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.

Artificial Intelligence Based Methods for Asphaltenes Adsorption by Nanocomposites: Application of Group Method of Data Handling, Least Squares Support Vector Machine, and Artificial Neural Networks.

Asphaltenes deposition is considered a serious production problem. The literature does not include enough comprehensive studies on adsorption phenomenon involved in asphaltenes deposition utilizing inhibitors. In addition, effective protocols on handling asphaltenes deposition are still lacking.