Monitoring and Diagnosis of the Carbon Dioxide Capture Process Using an Intelligent System Approach

The technology of amine-based carbon dioxide (CO2) capture has been widely adopted for reducing CO2 emissions and mitigating global warming. The operation of an amine-based CO2 capture system is complicated and involves monitoring over one hundred process parameters and careful manipulation of numerous valves and pumps. The monitoring and control of critical parameters of the process is an important task because it directly impacts plant performance and capture efficiency of CO2. In this study, artificial intelligence techniques were applied to develop a knowledge-based expert system that aims to effectively monitor and control the CO2 capture process, and thereby enhance CO2 capture efficiency. The Knowledge-Based System for Carbon Dioxide Capture (KBSCDC) was implemented with DeltaV Simulate (trademark of Emerson Corp., USA). DeltaV Simulate provides control utilities and algorithms which support the configuration of control strategies in modular components. The KBSCDC can conduct real-time monitoring and diagnosis, as well as suggest remedies for any abnormality detected. Also, the control strategies applied to the control devices of the process are simulated in KBSCDC. The expert system enhances performance and efficiency of the CO2 capture system because it supports automated diagnosis of the system should any abnormal conditions occur. In developing the system, the Inferential Modeling Technique (IMT) was applied to analyze the domain knowledge and problem-solving techniques. The knowledge base of KBSCDC can be shared and reused, and can contribute to future study of the CO2 capture process.