An intelligent modeling approach for prediction of thermal conductivity of CO2

Abstract

In the design of a carbon dioxide capture and storage (CCS) process, the thermal conductivity of carbon dioxide is of special concern. Hence, it is quite important to search for a quick and accurate determination of thermal conductivity of CO2 for precise modeling and evaluation of such a process. To achieve this aim, a robust computing methodology, entitled least square support vector machine (LSSVM) modeling, which is coupled with an optimization approach, was used to model this transport property. The model was constructed and evaluated employing a comprehensive data bank (more than 550 data series) covering wide ranges of pressures and temperatures. Before constructing the model, outlier detection was performed on the whole data bank to diagnose and delete erroneous measurements and doubtful data from the experimental dataset. It was found that the proposed LSSVM model had a very accurate prediction of thermal conductivity of CO2 with an average absolute relative error of 0.79% and a coefficient of determination of 0.999. In addition, more than 90% of the experimental data points were estimated with an absolute relative error smaller than 2% by the developed model.