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Developing Intelligent Algorithm as a Machine Learning Overview over the Big Data Generated by Euler-Euler Method To

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Machine learning (ML) models, combining Euler-Euler and adaptive network-based fuzzy inference system (ANFIS) methods, accurately simulate bubble column reactors. This intelligent simulation approach offers a faster, less computationally expensive alternative to traditional computational fluid dynamics (CFD).

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Area of Science:

  • Chemical Engineering
  • Computational Science
  • Artificial Intelligence

Background:

  • Bubble column reactors are crucial in chemical processes but complex to model.
  • Traditional computational fluid dynamics (CFD) simulations are time-consuming and computationally expensive.
  • Machine learning (ML) offers potential for efficient modeling of complex multiphase flows.

Purpose of the Study:

  • To develop an understanding of machine learning (ML) techniques for simulating multiphase flow in bubble column reactors.
  • To create an intelligent bubble column model using artificial intelligence (AI) for predictive simulations.
  • To validate the ML model's accuracy against established computational fluid dynamics (CFD) methods.

Main Methods:

  • A hybrid simulation approach combining Euler-Euler and adaptive network-based fuzzy inference system (ANFIS) was employed.
  • The ANFIS model was trained to learn input-output connections for predicting reactor behavior.
  • Validation involved comparing ANFIS predictions with results from detailed CFD simulations.

Main Results:

  • The ANFIS model accurately predicted hydrodynamic characteristics and stress within the bubble column reactor.
  • Predictions from the intelligent bubble column model showed excellent agreement with CFD simulation results.
  • The trained ML model successfully created a simulation independent of CFD source data.

Conclusions:

  • Machine learning, specifically the ANFIS approach, provides a viable and efficient method for simulating bubble column reactors.
  • Intelligent simulations developed through ML can replicate CFD accuracy without the associated computational cost.
  • This ML-driven simulation technique offers significant advantages for process modeling and optimization, reducing time and resource demands.