FMEnets: Flow, Material, and Energy networks for non-ideal plug flow reactor design

TL;DR

FMEnets is a physics-informed machine learning framework that models non-ideal plug flow reactors by integrating fundamental equations into a multi-scale network, enabling accurate forward and inverse analysis with noise robustness.

Contribution

The paper introduces FMEnets, a novel multi-scale neural network framework that combines physics-based equations with machine learning for reactor design and analysis, including inverse parameter inference.

Findings

FME-KANs are more noise-robust than FME-PINNs.

Achieves less than 2.5% error in kinetic parameter estimation.

Effective in three different reaction scenarios compared to finite element simulations.

Abstract

We propose FMEnets, a physics-informed machine learning framework for the design and analysis of non-ideal plug flow reactors. FMEnets integrates the fundamental governing equations (Navier-Stokes for fluid flow, material balance for reactive species transport, and energy balance for temperature distribution) into a unified multi-scale network model. The framework is composed of three interconnected sub-networks with independent optimizers that enable both forward and inverse problem-solving. In the forward mode, FMEnets predicts velocity, pressure, species concentrations, and temperature profiles using only inlet and outlet information. In the inverse mode, FMEnets utilizes sparse multi-residence-time measurements to simultaneously infer unknown kinetic parameters and states. FMEnets can be implemented either as FME-PINNs, which employ conventional multilayer perceptrons, or as FME-KANs, based on Kolmogorov-Arnold Networks. Comprehensive ablation studies highlight the critical role of the FMEnets architecture in achieving accurate predictions. Specifically, FME-KANs are more robust to noise than FME-PINNs, although both representations are comparable in accuracy and speed in noise-free conditions. The proposed framework is applied to three different sets of reaction scenarios and is compared with finite element simulations. FMEnets effectively captures the complex interactions, achieving relative errors less than 2.5% for the unknown kinetic parameters. The new network framework not only provides a computationally efficient alternative for reactor design and optimization, but also opens new avenues for integrating empirical correlations, limited and noisy experimental data, and fundamental physical equations to guide reactor design.