Equipment selection knowledge base system for industrial styrene process

doi:10.1016/j.cjche.2017.10.009

Chinese Journal of Chemical Engineering ›› 2018, Vol. 26 ›› Issue (8): 1707-1712.DOI: 10.1016/j.cjche.2017.10.009

• Selected Papers from the Chinese Process Systems Engineering Annual Meeting 2017 • 上一篇下一篇

Equipment selection knowledge base system for industrial styrene process

Weimin Zhong, Shuming Liu, Feng Wan, Zhi Li

Key Laboratory of Advanced Control and Optimization for Chemical Processed of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

收稿日期:2017-10-16 出版日期:2018-08-28 发布日期:2018-09-21
通讯作者: Weimin Zhong,E-mail address:wmzhong@ecust.edu.cn
基金资助:
Supported by the National Science Foundation China (61422303), National Key Technology R&D Program (2015BAF22B02), and the Development Fund for Shanghai Talents.

Equipment selection knowledge base system for industrial styrene process

Weimin Zhong, Shuming Liu, Feng Wan, Zhi Li

Key Laboratory of Advanced Control and Optimization for Chemical Processed of Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Received:2017-10-16 Online:2018-08-28 Published:2018-09-21
Contact: Weimin Zhong,E-mail address:wmzhong@ecust.edu.cn
Supported by:
Supported by the National Science Foundation China (61422303), National Key Technology R&D Program (2015BAF22B02), and the Development Fund for Shanghai Talents.

摘要/Abstract

摘要： Equipment selection for industrial process usually requires the extensive participation of industrial experts and technologists, which causes a serious waste of resources. This work presents an equipment selection knowledge base system for industrial styrene process (S-ESKBS) based on the ontology technology. This structure includes a low-level knowledge base and a top-level interactive application. As the core part of the S-ESKBS, the low-level knowledge base consists of the equipment selection ontology library, equipment selection rule set and Pellet inference engine. The top-level interactive application is implemented using S-ESKBS, including the parsing storage layer, inference query layer and client application layer. Case studies for the industrial styrene process equipment selection of an analytical column and an alkylation reactor are demonstrated to show the characteristics and implementability of the S-ESKBS.

关键词: Equipment selection, Ontology technology, Knowledge base system, Styrene process

Abstract: Equipment selection for industrial process usually requires the extensive participation of industrial experts and technologists, which causes a serious waste of resources. This work presents an equipment selection knowledge base system for industrial styrene process (S-ESKBS) based on the ontology technology. This structure includes a low-level knowledge base and a top-level interactive application. As the core part of the S-ESKBS, the low-level knowledge base consists of the equipment selection ontology library, equipment selection rule set and Pellet inference engine. The top-level interactive application is implemented using S-ESKBS, including the parsing storage layer, inference query layer and client application layer. Case studies for the industrial styrene process equipment selection of an analytical column and an alkylation reactor are demonstrated to show the characteristics and implementability of the S-ESKBS.

Key words: Equipment selection, Ontology technology, Knowledge base system, Styrene process

Weimin Zhong, Shuming Liu, Feng Wan, Zhi Li. Equipment selection knowledge base system for industrial styrene process[J]. Chinese Journal of Chemical Engineering, 2018, 26(8): 1707-1712.

Weimin Zhong, Shuming Liu, Feng Wan, Zhi Li. Equipment selection knowledge base system for industrial styrene process[J]. Chin.J.Chem.Eng., 2018, 26(8): 1707-1712.

参考文献

[1] J.L.M. Lastra, M. Delamer, Semantic web services in factory automation:fundamental insights and research roadmap, IEEE Trans. Ind. Inf. 2(1) (2006) 1-11.

[2] D. Lucke, C. Constantinescu, E. Westkamper, Smart factory-a step towards the next generation of manufacturing, Manufacturing Systems and Technologies for the New Frontier, Springer, London 2008, pp. 115-118.

[3] E. Sirin, B. Parsia, B.C. Grau, et al., Pellet:a practical OWL-DL reasoner, Web Semant. Sci. Serv. Agents World Wide Web 5(2) (2007) 51-53.

[4] R. Singh, K.V. Gernaey, R. Gani, An ontological knowledge-based system for the selection of process monitoring and analysis tools, Comput. Chem. Eng. 34(7) (2010) 1137-1154.

[5] M. Ashburner, C.A. Ball, J.A. Blake, et al., Gene ontology:tool for the unification of biology, Nat. Genet. 25(1) (2000) 25-29.

[6] F. Giunchiglia, F. Farazi, L. Tanca, The semantic web languages, semantic web information management, Springer, Berlin Heidelberg, 2010175-179.

[7] J. Ashraf, E. Chang, O.K. Hussain, F.K. Hussain, Ontology usage analysis in the ontology lifecycle:a state-of-the-art review, Knowl.-Based Syst. 80(2015) 34-47.

[8] R. Studer, Knowledge engineering:principles and methods, Data Knowl. Eng. 25(1-2) (1998) 161-197.

[9] N. Villanueva-Rosales, M. Dumontier, yOWL:an ontology-driven knowledge base for yeast biologists, J. Biomed. Inform. 41(5) (2008) 779-789.

[10] J. Ai, B. Smith, D.T. Wong, Saliva ontology:an ontology-based framework for a Salivaomics knowledge base, BMC Bioinf. 11(1) (2010) 302-309.

[11] Y. Guo, Z. Pan, J. Heflin, LUBM:a benchmark for OWL knowledge base systems, Web Semant. Sci. Serv. Agents World Wide Web 3(2) (2005) 158-182.

[12] H. Pan, Z. He, Q. Lin, et al., The effect of copper valence on catalytic combustion of styrene over the copper based catalysts in the absence and presence of water vapor, Chin. J. Chem. Eng. 24(4) (2016) 468-474.

[13] M.A. Sicilia, E. Garcia, S. Sanchez, et al., On integrating learning object metadata inside the OpenCyc knowledge base, Advanced Learning Technologies, 2004. Proceedings. IEEE International Conference on, IEEE 2004, pp. 900-901.

[14] C. Brewster, K. O'Hara, Knowledge representation with ontologies:the present and future, IEEE Intell. Syst. 19(1) (2004) 72-81.

[15] S. Bechhofer, OWL:web ontology language, Encyclopedia of Database Systems, Springer, US 2009, pp. 2008-2009.

[16] F. Zhang, Z.M. Ma, W.J. Li, Storing OWL ontologies in object-oriented databases, Knowl.-Based Syst. 76(2015) 240-255.

[17] R.C. Chen, Y.H. Huang, C.T. Bau, et al., A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection, Expert Syst. Appl. 39(4) (2012) 3995-4006.

[18] S. Liu, F. Wan, Y. Yang, W. Zhong, Research on model of chemical domain device ontology constructing, J. East China Univ. Sci. Technol. (Sci. Ed.) 43(3) (2017) 404-410.

[19] Y. Sure, M. Erdmann, J. Angele, et al., OntoEdit:collaborative ontology development for the semantic web, International Semantic Web Conference, Springer, Berlin Heidelberg 2002, pp. 221-235.

[20] M. O'connor, H. Knublauch, S. Tu, et al., Supporting rule system interoperability on the semantic web with SWRL, International Semantic Web Conference, Springer, Berlin Heidelberg 2005, pp. 974-986.

[21] B. McBride, Jena:a semantic web toolkit, IEEE Internet Comput. 6(6) (2002) 55-59.

[22] J. Perez, M. Arenas, C. Gutierrez, Semantics and complexity of SPARQL, International Semantic Web Conference, Springer, Berlin Heidelberg 2006, pp. 30-43.

[23] B. Quilitz, U. Leser, Querying distributed RDF data sources with SPARQL, European Semantic Web Conference, Springer, Berlin Heidelberg 2008, pp. 524-538.

[24] J. McGovern, S. Tyagi, M. Stevens, et al., Java Web Services Architecture, Morgan Kaufmann, 2003134-145.

Equipment selection knowledge base system for industrial styrene process

Equipment selection knowledge base system for industrial styrene process

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 0

编辑推荐

Metrics

本文评价