Application of Gaussian Processes to online approximation of compressor maps for load-sharing in a compressor station

Ahmed, A.; Zagorowska, M.A.; Rio-Chanona, E. A. del; Mercangöz, Mehmet

Application of Gaussian Processes to online approximation of compressor maps for load-sharing in a compressor station

Conference paper (2022)

Authors

A. Ahmed

M.A. Zagorowska Imperial College London

E. A. del Rio-Chanona

Mehmet Mercangöz

Affiliation

External organisation

To reference this document use:

http://resolver.tudelft.nl/uuid:f16ef9c8-f06c-4d86-bae5-6f2879ada807

More Info

expand_more

Published Date

2022

Language

English

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Affiliation

External organisation

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.