Discrete particle simulations predicting mixing behavior of solid substrate particles in a rotating drum fermenter

Area of Science:

• Biochemical Engineering
• Particle Technology
• Computational Modeling

Background:

• Solid-state fermentation (SSF) relies on efficient mixing of solid substrates.
• Understanding particle dynamics is crucial for optimizing SSF processes.
• Discrete particle modeling offers a potential method for simulating granular material flow.

Purpose of the Study:

• To simulate and validate the mixing behavior of solid substrate particles in a slow rotating drum using a soft-sphere discrete particle model.
• To investigate the influence of drum geometry and baffles on mixing efficiency.
• To assess the potential of discrete particle models for SSF equipment design.

Main Methods:

• Utilized a soft-sphere discrete particle model (DPM) to predict particle forces and motion.
• Conducted 2D simulations of particle mixing in a rotating drum.
• Validated simulation results against experimental mixing data obtained via video and image analysis.

Main Results:

• The DPM successfully predicted mixing progress based on drum filling degree and size.
• Large, straight baffles (67% of drum radius) were found to significantly improve mixing performance.
• Simulation accuracy was confirmed through qualitative and quantitative comparison with experimental data.

Conclusions:

• The soft-sphere DPM is a valuable tool for studying solid substrate particle mixing in SSF.
• The model can aid in the design and scale-up of mixed solid-state fermenters.
• Baffle design is a critical factor in optimizing mixing efficiency within rotating drums.