Residence time distribution data analysis and model prediction using machine learning for industrial scale pulp digester

Key Points

• Predictive analytics using machine learning enhances understanding of residence time distribution.
• Key findings suggest improved model accuracy by 25% through advanced data analysis techniques.
• Approach involved comprehensive data analysis focusing on industrial-scale pulp digesters' operations.
• Findings support potential for optimized digester performance and efficiency gains in production processes.