FCC Regenerator Afterburn Prediction Using Machine Learning

The patent replaces traditional mechanical monitoring and manual analysis systems with a machine learning-based prediction system. The condition estimator uses algorithms to automatically analyze operational data and predict afterburn occurrence, substituting complex human judgment and multiple monitoring devices with an intelligent software system that simplifies the overall architecture while improving reliability.

The patent creates a virtual model (condition estimator) that copies and simulates the complex combustion processes and afterburn phenomena in the regeneration tower. This software model replicates the behavior of the physical system, allowing prediction and analysis without requiring direct intervention in the physical process, thereby improving reliability while maintaining manageable system complexity.

The patent implements preliminary action by pre-training the condition estimator model using historical operational data before actual prediction tasks. This pre-processing phase allows the system to learn patterns and relationships in advance, so that during real-time operation, the model can quickly and accurately predict afterburn occurrence without requiring extensive data processing at that moment, thus improving measurement precision while minimizing time loss.

The condition estimator system performs self-service by automatically collecting, processing, and analyzing operational data without requiring external intervention. The machine learning model autonomously identifies patterns and makes predictions, eliminating the need for manual data processing steps and reducing the time required while maintaining high measurement precision through continuous automated monitoring.