Development of a knowledge-based system for monitoring and diagnosis of the CO2 capture process

The amine-based post combustion carbon dioxide (CO₂) capture has become a widely adopted technology for reducing large-scale CO₂ emissions, thereby helping to mitigate global warming. The operation of amine-based CO₂ capture is a complicated task, which involves monitoring over one hundred process parameters and manipulation of numerous valves and pumps. To enhance CO₂ capture efficiency, artificial intelligence techniques were applied to develop a knowledge-based expert system for monitoring and control of the CO₂ capture process. To develop the knowledge base, the Inferential Modeling Technique (IMT) was applied to analyze the domain knowledge and problem-solving techniques, and the system was implemented on DeltaV Simulate. The expert system helps to enhance performance and efficiency of the CO₂ capture system by reducing the time for diagnosis if an abnormal condition occurs. The expert system can also be used as a decision-support tool to help inexperienced operators in process control and can be used for training novice operators. The knowledge base can be extended with future development of the system.