A monitoring and diagnostic expert system for carbon dioxide capture

Abstract

The research objective is to design and construct a knowledge-based decision support system for monitoring, control and diagnosis of the carbon dioxide capture process, which is a complicated task involving manipulation of sixteen components and their operating parameters. Since manipulation of critical parameter values directly impacts performance of the plant and carbon dioxide capture efficiency, it is important to effectively monitor, control and diagnose the process. This paper describes development of a knowledge-based decision support system for the carbon dioxide capture process. The knowledge acquisition process was conducted based on the Inferential Modeling Technique, and the knowledge-based system was implemented with G2. Since the reboiler heat duty is the most significant parameter influencing the carbon dioxide production rate, in the current version, the Carbon Dioxide Capture Monitoring and Diagnostic (CDCMD) system controls the heat duty to maintain the desired carbon dioxide production rate, thereby improving performance of the plant and enhancing efficiency of the carbon dioxide capture process. The CDCMD system provides decision support to the operator in monitoring the process; it can also function as a tutorial system for novice operators.