Rigorous process simulation has become a tool that academic and industrial environments are exploiting, mainly to extract information useful for maximizing pro t. As a matter of fact, the detailed thermodynamic models contained in commercial or open-source software are able to represent the behavior of a chemical process far better than a linearized model. On the other hand, designing customized model predictive controllers (MPC) has proven to enhance process performance over traditional control architectures. Therefore, in this paper, we present the interaction of an easy-to-use MPC algorithm developed in Python with the rigorous simulator UniSim Design®. The communication exploits the UniSim Design® spreadsheets as the variables database to be read/written by Python, by stopping or not the simulation before every control action. The software communication has been properly developed so to maintain the exibility of the original MPC code and to exploit different controller designs. Two different test cases are presented to show the effectiveness of the proposed methodology: a simple two-phase separator and a more complex debutanizer column. System identi cation is used to build the controller's linear models, various MPC designs differing in considering disturbances as measurable have been analyzed and satisfactory results are obtained.