Application of an Intelligent System Framework and the Semantic Web for the CO2 Capture Process

  • Chuansan Luo
  • Qing Zhou
  • Christine W. ChanEmail author
Conference paper


This chapter describes how an implemented domain ontology provides the semantic foundation of an intelligent system framework in the domain of carbon dioxide capture process. The developed ontology has been implemented in the Knowledge Modeling System (KMS), which stores knowledge in the XML format. The intelligent system can process this XML schema to support construction of different solution application for the domain. As a sample application, an expert system for monitoring and control of the CO2 capture process operating at the International Test Centre for carbon Dioxide Capture (ITC) located on the campus of University of Regina in Saskatchewan, Canada is presented.


Semantic Knowledge Capture Process Knowledge Element Task Knowledge Solution Application 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aditya KalyanpurDaniel Jimenez Pastor, Steve Battle, and Julian Padget.: Automatic mapping of OWL ontologies into Java. In 16th International Conference on Software Engineering and Knowledge Engineering (SEKE), Banff, Canada (2004)Google Scholar
  2. 2.
    Chan, C.W.: A knowledge modeling system. IEEE Canadian Conference on Electrical and Computer Engineering (CCECE 04). Niagara Falls Ontario (2004)Google Scholar
  3. 3.
    Chan, C.W.: A Knowledge Modeling Technique for Construction of Knowledge and Databases, invited book chapter, Chapter 34 in Volume 4 of Leondes, C., Ed., Knowledge-Based Systems Techniques and Applications, 4 volumes, Academic Press, USA, p. 1109-1140, (2000)CrossRefGoogle Scholar
  4. 4.
    Chan, C.W.: Cognitive Modeling and Representation of Knowledge in Ontological Engineering. Brain and Mind, Vol. 4. 1389-1987, (2003)CrossRefGoogle Scholar
  5. 5.
    Chan, C.W.: Towards Ontology Construction for An Industrial Domain. Proceedings of the Third IEEE International Conference on Cognitive Informatics. 158 167, (2004)Google Scholar
  6. 6.
    Chen, L.L., Chan, C.W.: Ontology Construction from Knowledge Acquisition. Proceedings of Pacific Knowledge Acquisition Workshop (2005)Google Scholar
  7. 7.
    Maedche, A., Saab, S.: Ontology Learning for the Semantic Web. IEEE Intelligent Systems, Vol. 16.72-79 (2007)Google Scholar
  8. 8.
    Michel Vanden Bossche, Peter Ross, Ian MacLarty, Bert Van Nuffelen, Nikolay Pelov, On tology Driven Software Engineering for Real Life Applications, 3rd International Workshop on Semantic Web Enabled Software Engineering (SWESE 2007) Innsbruck, Austria (2007)Google Scholar
  9. 9.
    Neil M. Goldman. Ontology-oriented programming: Static typing for the inconsistent programmer. In 2nd International Semantic Web Conference (ISWC 2003), Sanibel Island, FL (2003)Google Scholar
  10. 10.
    Uschold, M.: Where are the semantics in the semantics web. AI Magazine, Vol. 24(3). 25-36 (2003)Google Scholar
  11. 11.
    Ouksel, A., Sheth, A.: A Brief Introduction to the Research Area and the Special Section. Special Section on Semantic Interoperability in Global Information Systems, SIGMOD Record. Vol 28(1). (1999)Google Scholar
  12. 12.
    Zhou, Q.: A Monitoring and diagnostic Expert System for Carbon Dioxide Capture. Expert System with Applications, Vol. 36. (2009)zbMATHGoogle Scholar

Copyright information

© Springer US 2010

Authors and Affiliations

  1. 1.Energy Informatics LaboratoryFaculty of Engineering University of ReginaReginaCanada

Personalised recommendations