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

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

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2022 European Control Conference (ECC)
Pages205-212
Number of pages8
ISBN (Electronic)9783907144077
DOIs
Publication statusPublished - 2022
Externally publishedYes

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