  Many industrial applications rely on real-time optimization to improve key
performance indicators. In the case of unknown process characteristics,
real-time optimization becomes challenging, particularly for the satisfaction
of safety constraints. In this paper, we demonstrate the application of an
adaptive and explorative real-time optimization framework to an industrial
refrigeration process, where we learn the process characteristics through
changes in process control targets and through exploration to satisfy safety
constraints. We quantify the uncertainty in unknown compressor characteristics
of the refrigeration plant by using Gaussian processes and incorporate this
uncertainty into the objective function of the real-time optimization problem
as a weighted cost term. We adaptively control the weight of this term to drive
exploration. The results of our simulation experiments indicate the proposed
approach can help to increase the energy efficiency of the considered
refrigeration process, closely approximating the performance of a solution that
has complete information about the compressor performance characteristics.
