Accurate prediction of properties of carbon dioxide for carbon capture and sequestration operations

Development of robust predictive models to estimate the transport properties of gases (namely viscosity and thermal conductivity) is of immense help in many engineering applications. This study highlights the application of the artificial neural network (ANN) and least squares support vector machine (LSSVM) modeling approaches to estimate the viscosity and thermal conductivity of CO2. To propose the machine learning methods, a total of 800 data gathered from the literature covering a wide temperature range of 200–1000 K and a wide pressure range of 0.1–100 MPa were used. Particle swarm optimization (PSO) and genetic algorithm (GA) as population-based stochastic search algorithms were applied for training of ANNs and to achieve the optimum LSSVM model variables. For the purpose of predicting viscosity, the PSO-ANN and GA-LSSVM methods yielded the mean absolute error (MAE) and coefficient of determination (R2) values of 1.736 and 0.995 as well as 0.51930 and 0.99934, respectively for the whole data set, while for the purpose of predicting thermal conductivity, the PSO-ANN and GA-LSSVM models yielded the MAE and R2 values of 1.43044 and 0.99704 as well as 0.72140 and 0.99857, respectively for the whole data set. Both methods provide properly capable method for predicting the thermal conductivity and viscosity of CO2.