Online Feedback Optimization of Compressor Stations with Model Adaptation using Gaussian Process Regression

TL;DR

This paper introduces an online feedback optimization method with Gaussian process-based model adaptation to effectively handle plant-model mismatch in compressor stations, significantly reducing power consumption deviations.

Contribution

It presents a novel integration of Gaussian process regression for online model adaptation within feedback optimization, improving performance under model mismatch conditions.

Findings

Reduces power consumption increase from 5% to 0.8% due to model mismatch

Demonstrates effectiveness in a realistic compressor station scenario

Achieves near-perfect knowledge performance despite imperfect plant models

Abstract

Online Feedback Optimization is a method used to steer the operation of a process plant to its optimal operating point without explicitly solving a nonlinear constrained optimization problem. This is achieved by leveraging a linear plant model and feedback from measurements. However the presence of plant-model mismatch leads to suboptimal results when using this approach. Learning the plant-model mismatch enables Online Feedback Optimization to overcome this shortcoming. In this work we present a novel application of Online Feedback Optimization with online model adaptation using Gaussian Process regression. We demonstrate our approach with a realistic load sharing problem in a compressor station with parametric and structural plant-model mismatch. We assume imperfect knowledge of the compressor maps and design an Online Feedback Optimization controller that minimizes the compressor station power consumption. In the evaluated scenario, imperfect knowledge of the plant leads to a 5\% increase in power consumption compared to the case with perfect knowledge. We demonstrate that Online Feedback Optimization with model adaptation reduces this increase to only 0.8%, closely approximating the case of perfect knowledge of the plant, regardless of the type of mismatch.