English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

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

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