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Engineering >> 2019, Volume 5, Issue 6 doi: 10.1016/j.eng.2019.09.004

A Knowledge Base System for Operation Optimization: Design and Implementation Practice for the Polyethylene Process

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

Received:2018-12-17 Revised:2019-02-22 Accepted: 2019-04-09 Available online:2019-10-01

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Setting up a knowledge base is a helpful way to optimize the operation of the polyethylene process by improving the performance and the efficiency of reuse of information and knowledge—two critical elements in polyethylene smart manufacturing. In this paper, we propose an overall structure for a knowledge base based on practical customer demand and the mechanism of the polyethylene process. First, an ontology of the polyethylene process constructed using the seven-step method is introduced as a carrier for knowledge representation and sharing. Next, a prediction method is presented for the molecular weight distribution (MWD) based on a back propagation (BP) neural network model, by analyzing the relationships between the operating conditions and the parameters of the MWD. Based on this network, a differential evolution algorithm is introduced to optimize the operating conditions by tuning the MWD. Finally, utilizing a MySQL database and the Java programming language, a knowledge base system for the operation optimization of the polyethylene process based on a browser/server framework is realized.


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[1]  Zhou J. Intelligent manufacturing—main direction of ‘‘Made in China 2025”. China Mech Eng 2015;26(17):2273–84. Chinese. link1

[2]  Pan AH. ‘‘Made in China 2025” points out the direction of the development of China’s petrochemical industry. Process 2015;25(19):14–8. Chinese. link1

[3]  Popescu M, Ungureanu-Anghel D, Filip I. Designing complex Petri nets using submodels with application in flexible manufacturing systems. In: Proceedings of 2013 IEEE 8th International Symposium on Applied Computational Intelligence and Informatics; 2013 May 23–25; Timisoara, Romania. New York: IEEE; 2013. p. 257–62. link1

[4]  Zhuang H, Feng L, Wen C, Peng Q, Tang Q. High-speed railway train timetable conflict prediction based on fuzzy temporal knowledge reasoning. Engineering 2016;2(3):366–73. link1

[5]  Yu S, Wu L, Zhang X. Research on equipment knowledge representation based on visual analytics. In: Proceedings of the 13th International Conference on Semantics, Knowledge and Grids; 2017 Aug 14–15; Beijing, China. New York: IEEE; 2017. p. 208–12. link1

[6]  Subbotin S, Gladkova O, Parkhomenko A. Knowledge-based recommendation system for embedded systems platform-oriented design. In: Proceedings of 2018 IEEE 13th International Scientific and Technical Conference on Computer Sciences and Information Technologies; 2018 Sep 11–14; Lviv, Ukraine. New York: IEEE; 2018. p. 368–73. link1

[7]  Zhang D, Hu D, Xu Y. A framework for ontology-based product design knowledge management. In: Proceedings of the 7th International Conference on Fuzzy Systems and Knowledge Discovery; 2010 Aug 10–12; Yantai, China. New York: IEEE; 2010. p. 1751–5. link1

[8]  Gao X. Research of knowledge base system based on ontology for drilling accident emergency decision. In: Proceedings of 2012 International Conference on Computer Science and Electronics Engineering; 2012 Mar 23–25; Hangzhou, China. New York: IEEE; 2012. p. 230–4. link1

[9]  Zhong W, Liu S, Wan F, Li Z. Equipment selection knowledge base system for industrial styrene process. Chin J Chem Eng 2018;26(8):1707–12. link1

[10]  Zhang R, Guo L. Knowledge management based on ontology modeling in collaborative learning environment. In: Proceedings of 2008 International Conference on Computer Science and Software Engineering; 2008 Dec 12–14; Wuhan, China. New York: IEEE; 2008. p. 337–40. link1

[11]  Noy NF, McGuinness DL. Ontology development 101: a guide to creating your first ontology. Palo Alto: Knowledge Systems Laboratory, Stanford University; 2001. Report No.: KSL-01-05. link1

[12]  Fernández-López M. Overview of methodologies for building ontologies. In: Proceedings of the IJCAI99 Workshop on Ontologies and Problem-Sloving Methods; 1999 Aug 2; Stokolm, Sweden; 1999. link1

[13]  Grüninger M, Fox MS. Methodology for the design and evaluation of ontologies. In: Proceedings of the IJCAI95 Workshop on Basic Ontological Issues in Knowledge Sharing; 1995 Aug 19–20; Montreal, Canada; 1995. link1

[14]  Fernández M, Gómez-Péres A, Juristo N. METHONTOLOGY: from ontological art towards ontological engineering. In: Proceedings of the Spring Symposium Series on Ontological Engineering; 1991 Mar 24–26; Palo Alto CA., USA; 1997. p. 33–40. link1

[15]  Bernaras A, Laresgoiti I, Corera J. Building and reusing ontologies for electrical network applications. In: Proceedings of the 12th European Conference on Artificial Intelligence; 1996 Aug 11–16; Budapest, Hungary. Chichester: John Wiley and Sons; 1996. p. 298–302. link1

[16]  Uschold M, Gruninger M. Ontologies: principles, methods and applications. Knowl Eng Rev 1996;11(2):93–136. link1

[17]  Yu Y. Research on knowledge base of typical chemical equipment based on ontology [dissertation]. Shanghai: East China University of Science and Technology; 2016. Chinese. link1

[18]  Soares JBP, Kim JD, Rempel GL. Analysis and control of the molecular weight and chemical composition distributions of polyolefins made with metallocene and Ziegler–Natta catalysts. Ind Eng Chem Res 1997;36(4):1144–50. link1

[19]  Soares JBP. The use of instantaneous distributions in polymerization reaction engineering. Macromol React Eng 2014;8(4):235–59. link1

[20]  Zhao W, Zhang H. Discuss of polymerization degree distribution function formula of different polymerization mechanism. Polym Mater Sci Eng 2013;29 (8):186–90. link1

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