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When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
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Gray wolf optimizer with bubble-net predation for modeling fluidized catalytic cracking unit main fractionator.

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A new Gray Wolf Optimizer with Bubble-Net Predation (GWO_BP) improves modeling accuracy for complex systems like Fluidized Catalytic Cracking Units (FCCU). This enhanced algorithm offers faster convergence and better optimization for dynamic characteristic estimation.

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Area of Science:

  • Chemical Engineering
  • Computational Intelligence
  • Process Systems Engineering

Background:

  • Modeling complex industrial systems like Fluidized Catalytic Cracking Unit (FCCU) main fractionators presents significant challenges due to their multivariable, nonlinear, and uncertain nature.
  • Traditional modeling methods often struggle to accurately capture the dynamic characteristics of such intricate systems.

Purpose of the Study:

  • To propose an advanced optimization algorithm, the Gray Wolf Optimizer with Bubble-Net Predation (GWO_BP), for accurate parameter estimation in FCCU main fractionator modeling.
  • To enhance the global search capability and convergence speed of optimization algorithms for complex system modeling.

Main Methods:

  • Development and application of the Gray Wolf Optimizer with Bubble-Net Predation (GWO_BP) algorithm.
  • Integration of whale's bubble-net predation strategy and Lévy flight into the GWO framework to improve search efficiency and avoid local optima.
  • Comparative analysis against basic GWO and Particle Swarm Optimization (PSO) using 12 standard test functions.
  • Application of GWO_BP to the parameter estimation of an FCCU main fractionator model.

Main Results:

  • GWO_BP demonstrated superior optimization accuracy compared to basic GWO and PSO on 12 test functions.
  • The GWO_BP algorithm effectively balanced exploration and exploitation, leading to faster convergence and higher accuracy.
  • Simulations confirmed that the FCCU main fractionator model developed using GWO_BP accurately represents the real-world dynamic characteristics.

Conclusions:

  • The GWO_BP algorithm is a highly effective tool for complex optimization problems, particularly in the accurate modeling of industrial processes.
  • The proposed method significantly improves the estimation of dynamic characteristics for FCCU main fractionators, offering a more reliable modeling approach.