An intelligent system for monitoring and diagnosis of the CO2 capture process

Amine-based carbon dioxide capture has been widely considered as a feasible ideal technology for reducing large-scale CO2 emissions and mitigating global warming. The operation of amine-based CO2 capture is a complicated task, which involves monitoring over 100 process parameters and careful manipulation of numerous valves and pumps. The current research in the field of CO2 capture has emphasized the need for improving CO2 capture efficiency and enhancing plant performance. In the present study, artificial intelligence techniques were applied for developing a knowledge-based expert system that aims at effectively monitoring and controlling the CO2 capture process and thereby enhancing CO2 capture efficiency. In developing the system, the inferential modeling technique (IMT) was applied to analyze the domain knowledge and problem-solving techniques, and a knowledge base was developed on DeltaV Simulate. pert system helps to enhance CO2 capture system performance and efficiency by reducing the time required for diagnosis and problem solving if abnormal conditions occur. The expert system can be used as a decision-support tool that helps inexperienced operators control the plant; it can be used also for training novice operators.