  Devising optimal operating strategies for a compressor station relies on the
knowledge of compressor characteristics. As the compressor characteristics
change with time and use, it is necessary to provide accurate models of the
characteristics that can be used in optimization of the operating strategy.
This paper proposes a new algorithm for online learning of the characteristics
of the compressors using Gaussian Processes. The performance of the new
approximation is shown in a case study with three compressors. The case study
shows that Gaussian Processes accurately capture the characteristics of
compressors even if no knowledge about the characteristics is initially
available. The results show that the flexible nature of Gaussian Processes
allows them to adapt to the data online making them amenable for use in
real-time optimization problems.
