Abstract
Acquiring accurate knowledge about the viscosity of carbon dioxide, nitrogen, and their mixtures as an extremely fundamental thermo-physical property for a broad range of temperatures and pressures is crucial not only for carbon capture and utilization (CCU) or carbon capture and storage (CCS) operations but also in chemical and petroleum industries and engineering design process. The proposed study aims at developing a model to predict the viscosity of carbon dioxide and nitrogen mixtures utilizing the Boosted Regression Tree (BRT) model optimized with the Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO) algorithms, the Cascade Feed-Forward Neural Networks (CFNN) and Multilayer Perception (MLP), General Regression Neural Network (GRNN), and the Genetic Programming (GP) techniques. To this end, an extensive dataset consisted of 3036 data points was gathered from the open-source literature in a broad range of pressures (0.001-453.2 MPa) and temperatures (66.5-973.15 K). The consistency of the employed paradigms was assessed based on graphical and statistical error analyses. The results indicated that the developed models provide a high degree of consistency with experimental values compared to the literature correlations. Among the established intelligent models, BRT-ABC model with a correlation coefficient (R2) of 0.9993 and root mean square error (RMSE) of 1.80 μPa s achieved the most accurate and reliable predictions of the gaseous mixture viscosity. Meanwhile, the GP technique was used to develop two easy-to-use correlations with regard to gas composition, temperature, and pressure with R2 values of 0.9883 and 0.9900 at temperatures lower and higher than 300 K, respectively.
NSTL