The new approach was found to outperform the previously proposed ones in term of computational time when increasing the number of included operators, predictor variable and experimental data. As examples, the developed method allowed to correctly identify the models underlying the rheological behaviour of Newtonian and non-Newtonian fluids, as well as simple kinetic laws, also in the case of sparse data sets, which is a common scenario in chemical process development.