Prediction of Vapor Liquid Equilibrium of Binary CO2-Contained Mixtures for Carbon Capture and Sequestration using Artificial Intelligence

Abstract

This research provides a comprehensive prediction using machine learning to predict vapor-liquid-equilibrium for CO2 - contained binary mixtures for carbon capture and sequestration projects. One of the best practices to lower the CO2 emissions in the atmosphere is Carbon Capture and Sequestration including capturing carbon dioxide from atmosphere and injecting it into the underground geological formations. One of the key elements in a successful project is to accurately model the phase equilibria which provides us on how the fluid or mixtures of the injected fluids will behave in certain pressures and temperatures underground. In this regard, different machine learning models have been implemented for the prediction. The data set consists experimental results of five different binary mixtures with CO2 presents in all of them. Then the results were compared to each other and the one with the highest accuracy was selected for each mixture. Peng Robinson equation of state was also used and compared with machine learning results. Finally, both machine learning and thermodynamic models were compared to experimental results to determine the accuracy. It was found out that thermodynamic model was unable to predict results for many data points while machine learning could predict results for most of the data points. Also, the accuracy of machine learning models was greatly better than thermodynamic model. In this research, a large data set including 748 data points is used on which machine learning models can be trained more accurate. Also, as a single machine learning model cannot predict accurate results for all mixtures, several models have been run on each mixture, and the one with the highest accuracy was selected for each CO2 -contained binary mixture which to our knowledge, has been never implemented.