Real-time capable deep learning framework for stable long-term forecasting of solids holdup in fluidized beds

Key Points

• Solids holdup in fluidized beds can be forecasted accurately using a deep learning framework, enhancing process efficiency.
• The model achieved notable performance metrics indicating high accuracy, with results likely applicable for industrial settings.
• Assessment using a real-time capable deep learning framework enables stable long-term forecasting, transforming operational strategies.
• Implications of this framework suggest improved process control in fluid dynamics, paving the way for more optimized operations.