Dynamic control and optimisationof plug-flow performanceusing machine learning*

Dynamic optimisation of transient performance in fluid systems is critical for various applications, such as heat exchange, and microfluidic mixing. However, traditional optimisation methods often rely on evaluating the entire performance profile over time, which can be computationally expensive and impractical. To address these limitations, we propose a novel approach that combines Bayesian Optimisation (BO) and Reinforcement Learning (RL) to efficiently optimise the transient performance of fluid systems based on early flow characteristics. Our key idea is to leverage the valuable information provided by Computational Fluid Dynamics (CFD) models in the BO framework, not as black-box functions, but as sources of local flow characteristics, such as cross-sectional velocity or mixing characteristics. By identifying the optimal early flow characteristics that strongly correlate with the desired performance, we can guide the optimisation process more efficiently than waiting for the entire performance profile to develop. The proposed BO-RL framework fills a critical gap in the current landscape of dynamic optimisation techniques for fluid systems and beyond, offering a computationally efficient and accurate solution for optimising transient performance based on early system characteristics. Furthermore, our approach is applicable to other domains facing similar dynamic optimisation challenges, such as energy grid management, where early characteristics like renewable energy generation and load profiles can predict and optimise system stability and efficiency.