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

TL;DR

This paper introduces a Gaussian Process-based algorithm for online learning of compressor characteristics, enabling real-time optimization in compressor stations despite changing conditions.

Contribution

It presents a novel online learning method using Gaussian Processes for compressor map approximation, adaptable to real-time data without prior knowledge.

Findings

Accurately captures compressor characteristics in real-time

Adapts effectively to changing compressor data

Suitable for real-time optimization applications

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.