The aim of this document is to present an efficient and systematic method of model-based predictive control synthesis. Model predictive control requires using a model of a dynamical system, that can be linear, time-varying, non-linear or identified from data. Finding a model that is both precise and simulatable at low computational cost can be challenging and time consuming due to requiring extensive knowledge of the system and physics as well as a large volume of data with relevant scenarios and sometimes a complicated identification work (filtering noises and bias, data formatting, etc.). The proposed methodology begins with fine-scale multi-physics modelling, which is possible thanks to open model libraries (see Modelica). The obtained model is then simulated by considering ad hoc scenarios to generate data, which are then used to identify a neural network, that will support the predictive control syntheses. The systematic methodology is detailed and applied to the widely used control benchmark known as the quadruple tanks process. Results show that the methodology is accurately applied to optimize hyperparameters in finding a neural network model and to control the quadruple tanks process with the predictive controller.