However, the method remained in the mathematical domain and has yet to be applied to physical models and noisy experimental data. This paper aims to advance the method of globally optimal symbolic regression towards automated, data-driven identification of physical models, and its applications to chemical engineering case studies. Compared to additive models in conventional regression and heuristic searches in SR, the globally optimal data-driven modelling technique, without any previously imposed model structure, is expected to discover true underlying relationships more reliably.