Gas–liquid separator modelling and simulation with Gaussian-process models

The Gaussian-process (GP) model is an example of a probabilistic, nonparametric model with uncertainty predictions. It can be used for the modelling of complex nonlinear systems and also for dynamic systems identification. The output of the GP model is a normal distribution, expressed in terms of the mean and variance. The modelling case study of a gas–liquid separator is presented in this paper. It describes the comparison of three methods for dynamic GP model simulation in the phase of model validation. The level of the computational burden associated with each approach increases with the complexity of the computation necessary for an approximation of the uncertainty propagation.