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 This graph show the acceleration of the code . You can access the paperhere.
🎯 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