کاربرد یک رویکرد پایه ای دوگانه برای ساخت و ساز سیستم های هوشمند

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.

Research on post-combustion CO2 capture has been ongoing over the past two decades, and the amine-based CO2 capture process has become one of the dominant post-combustion CO2 capture technologies because of its efficiency and low cost; this process has been implemented at the International Test Centre for CO2 capture (ITC) at the University of Regina in Saskatchewan, Canada. Operation of the ITC system involves manipulation of 16 primary components and a multitude of valves and pumps, which generate a huge set of data on the process parameters. Currently the CO2 capture process system is supervised by the DeltaV (a trademark of Emerson Corp., USA) process control system, which is based on the Object Linking and Embedding for Process Control (OPC) industrial protocol.1 The DeltaV system is able to provide automated industrial process control and can support automated monitoring and control functions. However, it neither supports data filtering and analysis nor diagnosis of the process systems. As a result, the process engineers and researchers at ITC often need to manually retrieve and analyze the monitored data collected by DeltaV. Therefore, developing a decision support system (DSS) for operation support of the CO2 capture process system would enhance the efficiency of analysis and monitoring processes. A DSS would (1) provide reliable support to the operators in performing monitoring and diagnosis of the facilities during daily system operations, and (2) help researchers who need easy access to the process data. In order to provide infrastructure for building this and other such DSS's, a web-based intelligent system framework was built which can support development of different intelligent system modules for diverse functions. In addition, the experts′ knowledge and problem-solving methods adopted during operation of the CO2 capture process system were captured and represented in an ontology model of the CO2 capture process. The ontology model can serve as the knowledge foundation, which provides semantic guidance in diagnosis and operational decision-making of the CO2 capture process system. This paper presents development of the IT infrastructure developed for the CO2 capture process system which consists of both the intelligent system framework and the ontology model. This paper is organized as follows. Section 2 introduces the CO2 capture process and background literature about ontology, semantic knowledge and operational support system. Section 3 describes knowledge modeling and ontology construction. Section 4 presents the design of the architectural framework for developing intelligent systems. Section 5 illustrates how the architectural framework and the ontology model can serve as dual foundation for building intelligent systems. Section 6 provides some discussions and conclusions, and discusses possible directions for future work.

This paper presents the interaction between the intelligent system framework of CO2AFIS and the implemented ontology developed for the CO2 capture process system. An expert system module for monitoring and troubleshooting operation of the CO2 capture process system has been developed based on the dual foundation provided by the framework and the ontology. The framework supports construction of the expert decision support system, and enables retrieving and manipulating the semantic knowledge in the ontology. The current version of CO2AFIS uses the implemented ontology produced by the Knowledge Modeling System (KMS), which represented the domain knowledge formally, but left the task knowledge in the non-formalized representation of pseudo code. To facilitate task information retrieval from the knowledge base, a parser was written to interpret the pseudo code. Therefore, an item on the future research agenda is to formalize the task knowledge in the rule base so that it is machine-processable. To facilitate knowledge sharing with multiple intelligent system applications within the framework, the data and rules stored in the XML format need to be converted to the more standard representation of RDF or OWL.