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Prediction of gas velocity in two-phase flow using developed fuzzy logic system with differential evolution
Meisam Babanezhad1,2,3, Samyar Zabihi4, Iman Behroyan5,6
1Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam.
Scientific Reports
|January 28, 2021
Summary
This study optimized a fuzzy inference system (FIS) using the differential evolution (DE) algorithm. The enhanced model accurately predicted gas velocity in a two-phase reactor simulation.
Area of Science:
- Chemical Engineering
- Computational Fluid Dynamics
- Artificial Intelligence
Background:
- Accurate modeling of two-phase reactors is crucial for process optimization.
- Traditional methods for simulating multiphase flow can be computationally intensive.
- Fuzzy inference systems (FIS) offer a data-driven approach to modeling complex systems.
Purpose of the Study:
- To integrate the differential evolution (DE) algorithm for training a fuzzy inference system (FIS).
- To optimize FIS parameters for enhanced predictive capacity in multiphase reactor simulations.
- To predict gas phase velocity in a 2D two-phase reactor model.
Main Methods:
- Utilized a 2D simulation of a two-phase reactor with gas sparging.
- Employed the differential evolution (DE) algorithm to train the fuzzy inference system (FIS).
- Tuned DE and FIS parameters to maximize the system's predictive performance.
Main Results:
- Achieved the highest FIS capacity through optimized DE and FIS parameters.
- Successfully predicted the gas phase velocity in the x-direction within the reactor.
- Validated the model's effectiveness at a specific injected gas velocity (0.08 m/s).
Conclusions:
- The combined DE-FIS approach provides an effective method for modeling and predicting complex phenomena in two-phase reactors.
- Optimized FIS models demonstrate high predictive accuracy for gas velocity.
- This hybrid intelligent system offers a computationally efficient alternative for reactor simulation and analysis.
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