My main research activities are within the area of process modeling, control, and economic optimization. A First contribution is the study of economic-MPC algorithms that combine MPC and RTO into a single dynamic optimization and control module. I aimed at studying recent proposals, as well as defining new algorithms, that included disturbance estimation and the novel technique carried by the Dynamic-RTO of modifier adaptation, to improve robustness and applicability in process control problems. A central contribution is the study of the data-driven techniques for MPC. Nonlinear data-driven models will be deployed and updated on-line to achieve optimality of the control action despite the inaccuracy of the available non-linear model and to guarantee the safety of operation. The subsequent contribution will be the application of the proposed methodologies into a chosen process industry. First-year: Analysis of scientific literature in the subjects of real-time optimization, model predictive control, optimization algorithms, machine learning, and cyber-physical system. Identification of interest in existing solutions. Second-year: Theoretical development and validation of the proposed algorithm. Third-year: Software implementation in a “candidate” industry.