Abstract
Abstract The application of machine learning (ML) techniques for the control and development of digital twins for a fluidized bed reactors represents a significant advancement in process engineering. In this study, the integration of data-driven models trained using computational fluid dynamics (CFD) simulations, is explored for developing and optimizing the lab-scale fluidized bed reactor operations. By leveraging the collection of data generated from CFD simulations, data-driven algorithms, based on the Singular Value Decomposition (SVD) and Gaussian processes for regression, are trained to predict the gas-solid flow patterns under different operating condition. The data-driven models presented, serve as efficient reduced order model (ROM) surrogate for computationally expensive CFD simulations, enabling real-time predictions and control strategies for fluidized bed reactors, facilitating continuous monitoring, optimization, and predictive maintenance. Moreover, the ROM can effectively capture the complex relationships within the reactor system, with an overall error < 10% even without precise knowledge of the underlying physical phenomena. The synergistic combination of ML techniques and CFD simulations offers valuable insights into complex multiphase flow phenomena and reactor dynamics, leading to improved process control, energy efficiency, and overall performance of fluidized bed reactors. This approach holds great promise for accelerating innovation and sustainability in chemical and energy industries.
Published Version
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