Application of a dual foundation approach for construction of an intelligent system

Abstract

This paper presents the development of a decision support system for monitoring and diagnosis of the amine-based post-combustion carbon dioxide (CO2) capture process system at the International Test Centre for CO2 Capture (ITC) at the University of Regina. The amine-based CO2 absorption capture process system consists of dozens of components and generates more than a hundred different types of data. The vast amount of raw data produced by the system are measurements of the many reaction components, valves and pumps. The system operators often find it difficult to quickly detect, diagnose and correct any abnormal conditions that may arise during operation. Therefore, developing a decision support system for monitoring and diagnosis of the CO2 capture process system which aids the operator in monitoring and diagnosis of the system is a desirable objective. This paper describes development of the system based on the dual foundation of a domain ontology and an intelligent system framework in the domain of carbon dioxide capture process. The developed ontology provides the semantic knowledge foundation; it was implemented in the Knowledge Modeling System (KMS), and the knowledge was stored in the XML format. The intelligent system framework consists of system functions which can use the XML schema provided by the ontology and support the development process. A decision support expert system for process monitoring is a sample system developed on the dual foundation.