Real-time control of multiphase processes with learned operators

TL;DR

This paper introduces a surrogate-assisted model predictive control framework utilizing learned Fourier Neural Operators to efficiently regulate multiphase flows, demonstrated on a bubble column system, enabling real-time control despite complex dynamics.

Contribution

The work develops a novel MPC approach using learned operators for fast, accurate forecasting of multiphase flow evolution, reducing computational costs compared to traditional numerical models.

Findings

FNO-based forecasting is accurate and computationally efficient.

The framework successfully controls a bubble column to track liquid level setpoints.

The approach offers a practical route for real-time control of multiphase processes.

Abstract

Multiphase flows frequently occur naturally and in manufactured devices. Controlling such phenomena is extremely challenging due to the strongly non-linear dynamics, rapid phase transitions, and the limited spatial and temporal resolution of available sensors, which can lead to significant inaccuracies in predicting and managing these flows. In most cases, numerical models are the only way to access high spatial and temporal resolution data to an extent that allows for fine control. While embedding numerical models in control algorithms could enable fine control of multiphase processes, the significant computational burden currently limits their practical application. This work proposes a surrogate-assisted model predictive control (MPC) framework for regulating multiphase processes using learned operators. A Fourier Neural Operator (FNO) is trained to forecast the spatiotemporal evolution of a phase-indicator field (the volume fraction) over a finite horizon from a short history of recent states and a candidate actuation signal. The neural operator surrogate is then iteratively called during the optimisation process to identify the optimal control variable. To illustrate the approach, we solve an optimal control problem (OCP) on a two-phase Eulerian bubble column. Here, the controller tracks piecewise-constant liquid level setpoints by adjusting the gas flow rate introduced into the system. The results we obtained indicate that field-level forecasting with FNOs are well suited for closed-loop optimization since they have relatively low evaluation cost. The latter provide a practical route toward MPC for fast multiphase unit operations and a foundation for future extensions to partial observability and physics-informed operator learning.