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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.