  Any industrial system goes along with objectives to be met (e.g. economic
performance), disturbances to handle (e.g. market fluctuations, catalyst decay,
unexpected variations in uncontrolled flow rates and compositions,...), and
uncertainties about its behavior. In response to these, decisions must be taken
and instructions be sent to the operators to drive and maintain the plant at
satisfactory, yet potentially changing operating conditions.
  Over the past thirty years many methods have been created and developed to
answer these questions. In particular, the field of Real-Time Optimization
(RTO) has emerged that, among others, encompasses methods that allow the
systematic improvement of the performances of the industrial system, using
plant measurements and a potentially inaccurate tool to predict its behaviour,
generally in the form of a model. Even though the definition of RTO can differ
between authors, inside and outside the process systems engineering community,
there is currently no RTO method, which is deemed capable of fully automating
the aforementioned decision-making process. This thesis consists of a series of
contributions in this direction, which brings RTO closer to being capable of a
full plant automation.
  Keywords: Real-time optimization, Decision-making, Optimization,
Reduced-order-model optimization, Autopilot for steady-state processes,
Operational research.
