Compartment Modelling of Multiphase Reactors using Unsupervised Clustering

TL;DR

This paper introduces CLARA, a software toolbox that automates the creation of compartment models from CFD data using unsupervised clustering, enabling efficient simulation of multiphase reactors for control and design.

Contribution

The novel CLARA toolbox automates compartment modeling of multiphase reactors from CFD data, including interphase phenomena, with verified accuracy and reduced computational cost.

Findings

CLARA accurately reproduces reactor performance and species distributions.

The compartment models significantly reduce computational demand compared to CFD.

Verification with benchmarks confirms the model's reliability.

Abstract

Detailed Computational Fluid Dynamics (CFD) simulations are too computationally expensive for the real-time control and design optimization of multiphase flow reactors. To address these limitations, we introduce CLARA, a software toolbox that automates the generation of Compartment Models (CM) via the unsupervised clustering of CFD data. Unlike previous studies, our toolbox enables the modelling of multiphase phenomena and interphase mass transfer within each compartment. CLARA employs unsupervised clustering algorithms, graph reassignment, and optimization routines to ensure mass conservation and spatial connectivity across all compartments. Verification studies utilizing analytical benchmarks and reactive multiphase CFD simulations demonstrate that the CMs produced by CLARA accurately reproduce reactor performance and spatial species distributions. The significantly reduced computational demand of CMs compared to full CFD models enables the optimal control of multiphase reactors and facilitates their rational design and optimization.