Carbon dioxide compressibility factor determination using a robust intelligent method

Owing to the demanding applications and wide uses of supercritical carbon dioxide in oil, gas and chemical industries, fast and precise estimation of carbon dioxide compressibility factor is of a vital significance in order to be imported into the relevant industrial simulators. In this study, a data bank covering wide range of temperature and pressure was gathered from open literature. Afterwards, a rigorous novel approach, namely least square support vector machine (LSSVM) optimized with coupled simulated annealing (CSA) was proposed to develop a reliable and robust model for the prediction of compressibility factor of carbon dioxide. Reduced temperature and pressure are the inputs of the model. 80% of the dataset was used for training the model and the remaining 20% was used to evaluate its accuracy and reliability. Statistical and graphical error analyses have been conducted to investigate the performance of the model and the obtained results from the proposed model have been compared with those of six equations of state, REFPROP package and two correlations. It was demonstrated that the proposed CSA–LSSVM model is more efficient and reliable than all of the studied empirical correlations, equations of state and the software package, hence it can be utilized confidently for the prediction of carbon dioxide compressibility factor.