Work

From Block-Oriented Models to the Koopman Operator: A Comprehensive Review on Data-Driven Chemical Reactor Modeling

Year: 2025

Type: article

Abstract: Some chemical reactors exhibit coupled dynamics with multiple equilibrium points and strong nonlinearities. The accurate modeling of these dynamics is crucial to optimal control and increasing the reactor’s economic performance. While neural networks can effectively handle complex nonlinearities, they sacrifice interpretability. Alternatively, block-oriented Hammerstein–Wiener models and Koopman operator-based linear predictors combine nonlinear representation with linear dynamics, offering a gray-box identification approach. This paper comprehensively reviews recent advancements in both the Hammerstein–Wiener and Koopman operator methods and benchmarks their accuracy against neural network-based approaches to modeling a large-scale industrial Fluid Catalytic Cracking fractionator. Furthermore, Monte Carlo simulations are employed to validate performance under varying signal-to-noise ratios. The results demonstrate that the Koopman bilinear model significantly outperforms the other methods in terms of accuracy and robustness.

Source: Mathematics

DOI: 10.3390/math13152411

Authors: Mustapha Kamel Khaldi, Mujahed Al-Dhaifallah, Ibrahim Aljamaan, Fouad Mohammad Al-Sunni, Othman Taha, Abdullah Alharbi

Institutions: King Fahd University of Petroleum and Minerals, Imam Abdulrahman Bin Faisal University, Saudi Aramco (Saudi Arabia)

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: King Fahd University of Petroleum and Minerals