Response surface data for sensitivity study of industrial spray injected fluidized bed reactor

Area of Science:

• Chemical Engineering
• Process Engineering

Background:

• Industrial fluidized bed reactors are crucial for converting solid-laden streams into granular products.
• Optimizing these reactors requires understanding complex parameter interactions.

Purpose of the Study:

• To design and simulate an industrial fluidized bed reactor for aqueous solid-laden stream conversion.
• To investigate the impact of five key parameters on reactor performance using CFD simulations.

Main Methods:

• Computational Fluid Dynamics (CFD) simulations using the MFiX two-fluid model.
• Latin-hypercube sampling for exploring five model parameters: bed particle size, bed particle density, coal particle size, spray feed flow rate, and fluidizing gas flow rate.
• Gaussian Process (GP) response surface modeling via the Dakota toolkit to analyze simulation data.

Main Results:

• Response surfaces were generated for bed differential temperature, low solids velocity, and bed void fraction.
• The GP models achieved high fits: 0.7797 for temperature, 0.8664 for velocity, and 0.9440 for solids packing.
• The study provides a comprehensive dataset and fitted model coefficients for future optimization.

Conclusions:

• The developed Gaussian Process models accurately represent the complex relationships between input parameters and fluidized bed reactor performance.
• This work offers a robust methodology for optimizing industrial fluidized bed reactor design and operation for consistent granular product generation.