Accurate prediction of gas compressibility factor using kernel ridge regression

Gas compressibility factor (z) is one of the critical parameters in the calculations that are used for the upstream and downstream zones of petroleum/chemical industries. The process of obtaining accurate value for physical and thermodynamical properties of hydrocarbons is getting more challenging in the case of multicomponent non ideal systems. The purpose of this study is to apply kernel ridge regression (KRR) in the form of the recently developed truncated regularized kernel ridge regression algorithm to estimate z-factor. Compared to the support vector machines (SVM), the KRR algorithm is as accurate as, and much faster than all of the other algorithms.