Comprehensive Modeling in Predicting Liquid Density of the Refrigerant Systems Using Least-Squares Support Vector Machine Approach

Abstract

A robust machine learning algorithm known as the least-squares support vector machine (LSSVM) model was used to predict the liquid densities of 48 different refrigerant systems. Hence, a massive dataset was gathered using the reports published previously. The proposed model was evaluated via various analyses. Based on the statistical analysis results, the actual values predicted by this model have high accuracy, and the calculated values of RMSE, MRE, STD, and R2 were 0.0116, 0.158, 0.1070, and 0.999, respectively. Moreover, sensitivity analysis was done on the efficient input parameters, and it was found that CF2H2 has the most positive effect on the output parameter (with a relevancy factor of +50.19). Furthermore, for checking the real data accuracy, the technique of leverage was considered, the results of which revealed that most of the considered data are reliable. The power and accuracy of this simple model in predicting liquid densities of different refrigerant systems are high; therefore, it is an appropriate alternative for laboratory data.

Optimization Design of Drilling Fluid Chemical Formula Based on Artificial Intelligence

“…e most significant difference between it and the neural network is that it only needs to build a support vector machine model based on limited training samples by mining the corresponding relationship between the input and output data, to realize the prediction of unknown data. Support vector machines not only perform well in processing language, text, face recognition, etc., but also achieve good results in regression, such as using logging data to predict formation porosity and reservoir properties in the field of well logging [10][11][12][13]. Support vector machine is influencing various areas of machine learning through this new method of intelligent machine learning.…”

“…e introduction of the penalty factor c balances these two terms. After introducing the Lagrange multipliers, α,α * and Lagrange functions, (12) becomes:…”