A consistent set of thermophysical properties of methane curated with machine learning

Accurately predicting thermophysical properties across different physical states is essential for industrial and scientific applications. However, experimental data measurements often exhibit variability and noise, requiring robust modeling approaches. In this work, we employ machine learning (ML) techniques to predict methane’s thermophysical properties in liquid, vapor, and supercritical phases, including isobaric and isochoric heat capacities, density, volume, Joule-Thomson coefficients, enthalpies, sound speed, and viscosities applying an approach recently developed (ACS Eng. Au, DOI: 10.1021/acsengineeringau.5c00001). We explored different ML algorithms and approaches, including Adaptive Boosting, Bagging, Decision Trees, Extra Trees, Gradient Boosting, Histogram-based Gradient Boosting Regression Tree, K-Nearest Neighbors, Light Gradient Boosting Machine, Nu-Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Artificial Neural Networks. ML models produced predictions that aligned more closely with the statistically treated National Institute of Standards and Technology (NIST) data than with the raw experimental data used to train these models. These results highlight ML’s potential to identify and generalize complex patterns, smooth inherent noise, and manage the variability of different thermophysical properties. They indicate that ML models, particularly Extra Trees and Gradient Boosting, can offer a scalable alternative for thermophysical property predictions, offering consistency and efficiency over traditional methods. Although our approach does not eliminate preprocessing, it demonstrates that ML can effectively manage noisy data independently, offering a more efficient and cost-effective alternative to conventional pre- and post-processing techniques.