Development of an Intelligent System for Monitoring and Diagnosis of the Carbon Dioxide Capture Process

Abstract

The technology of amine-based carbon dioxide (CO2) capture has been widely adopted for reducing CO2 emissions and mitigating global warming. The primary research objective in the field of post-combustion CO2 capture process system is to improve effectiveness and efficiency of the process. Extensive literature review of the research showed that the dominant approach was to investigate the behaviors of the aqueous amine solvents for enchancing CO2 capture efficiency. As 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, automated monitoring and process control can be a fruitful approach to enhance efficiency of the CO2 capture process system. In this study, artificial intelligence techniques were applied for development of a knowledge-based expert system that effectively monitors and controls the CO2 capture process system so as to 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 process system because it supports automated diagnosis of the system should any abnormal conditions occur. In this way, costly downtime and maintenance are avoided.

Keywords: knowledge-based system, monitoring, control, diagnosis, carbon dioxide capture process system