Application of Gaussian Processes to online approximation of compressor maps for load-sharing in a compressor station
Abstract
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.
Keywords
Cite
@article{arxiv.2111.11890,
title = {Application of Gaussian Processes to online approximation of compressor maps for load-sharing in a compressor station},
author = {Akhil Ahmed and Marta Zagorowska and Ehecatl Antonio del Rio-Chanona and Mehmet Mercangöz},
journal= {arXiv preprint arXiv:2111.11890},
year = {2021}
}