Merging process optimization and advanced control: novel algorithms and performance monitoring strategies for sustained economic efficiency

This Thesis presents possible solutions to best obtain and maintain economic performances in industrial processes. It is generally known that sustained performance is seen from industrial practitioners as one of the main research goals. This PhD activity aims to achieve this goal from different perspectives and intermediate objectives. It is shown how current advanced control and optimization implementations are indeed far from being economically optimal, and how this gap can be reduced. In particular, the problem of designing an Economic Model Predictive Control (EMPC) algorithm is addressed. Different methods are proposed in order to achieve optimal performance despite the presence of plant-model mismatch. The most recent works in literature regarding this problematic are analyzed. Different solutions are formulated merging together different techniques coming from the MPC field, Real-Time Optimization (RTO) field and system identification. We also formulate a performance monitoring technique for a sustained optimal behavior in order to maintain the economic efficiency of the process despite time-varying disturbances. This PhD activity has been also developed with the aim of using and producing open-source tools to be available for the research community. Hence, Python has been used to produce a multipurpose code for MPC simulations. This code has been the main tool used to develop results and algorithmic analysis. In addition, during these three years, it has been applied both in didactic support and other MPC related research. Scientific rigor has always been the primary key, hence the different results showed in this Thesis have been published in peer-reviewed journals or presented to international congresses.