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Area of Science:

  • Chemical Engineering
  • Computational Science
  • Artificial Intelligence

Background:

  • Bubble column reactors are crucial in the chemical industry for two-phase flow processes.
  • Computational Fluid Dynamics (CFD) is robust for simulating these reactors but computationally expensive.
  • Artificial Intelligence (AI) algorithms, like ANFIS, can optimize CFD by learning simulation patterns.

Purpose of the Study:

  • To reduce the computational expense of CFD simulations for two-phase flows in chemical reactors.
  • To introduce and evaluate a hybrid approach coupling CFD data with a differential evolution-based fuzzy inference system (DEFIS).
  • To compare the predictive performance and learning efficiency of DEFIS against ANFIS.

Main Methods:

  • Coupling CFD simulation data with an AI algorithm (DEFIS).
  • Using air velocity as output, with coordinates, water velocity, and time step as inputs for the AI model.
  • Investigating the impact of differential evolution parameters and input numbers on AI model performance.
  • Comparing the prediction accuracy and computational time of DEFIS and ANFIS.

Main Results:

  • Both ANFIS and DEFIS accurately predicted the CFD flow patterns.
  • The prediction times for ANFIS and DEFIS were comparable.
  • DEFIS demonstrated a fourfold increase in learning time compared to ANFIS.
  • DEFIS successfully correlated air velocity with input parameters, offering a simpler alternative to CFD.

Conclusions:

  • Hybrid AI-CFD models, particularly DEFIS, can effectively replace expensive CFD computations for two-phase flow simulations.
  • DEFIS offers a viable alternative for process simulation and optimization in chemical reactors.
  • While both ANFIS and DEFIS show promise, DEFIS requires further optimization to reduce its learning time.