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Accelerating Multiphase Control: An AI Surrogate Framework

Real-time control of multiphase processes with learned operators

KVL Staff on Project

Didier Barradas Bautista
didier.barradasbautista@kaust.edu.sa
Building 1, Level 0, Office 0125

KAUST PI on Project

William Roberts
william.roberts@kaust.edu.sa

KAUST RS on Project

Paolo Guida
paolo.guida@kaust.edu.sa

Research Breakthrough

  • Bridging the Real-Time Gap: Developed an AI framework that replaces slow, traditional physics simulations with Fourier Neural Operators, enabling the first real-time control of complex multiphase flows.
  • Precision AI Decision-Making: Integrated Bayesian Optimization with neural surrogates to precisely track liquid levels in bubble columns, overcoming the mathematical “noise” that typically breaks standard control algorithms.
  • Accelerating Industrial Innovation: Reduced the computational cost of multiphase flow management by orders of magnitude, providing a scalable foundation for smarter, more efficient chemical reactors and energy systems.

🔬 Research Visualization The graphical abstract below illustrates the iterative filtering strategy used to enrich correct protein–protein docking models. The paper entitled “Increasing the Fraction of Correct Solutions in Ensembles of Protein-Protein Docking Models by an Iterative Consensus Algorithm” is available for download and review. here.

🎯 KVL's Contribution: KVL's visualization scientist Didier Barradas-Bautista contributed significantly to the implementation of algorithm through:

  • Updating the code to efficientiyl use the GPU memory
  • refactoring the code to output efficientyl the results
,