Automatic generation method of process knowledge based on P-graph

被引：0
|
作者：
Cao J. [1 ,2 ]
Mu P. [1 ,2 ]
Gu X. [1 ,3 ]
Zhu Q. [1 ,2 ]
机构：
[1] College of Information Science and Technology, Beijing University of Chemical Technology, Beijing
[2] Engineering Research Center of Intelligent PSE, Ministry of Education, Beijing
[3] Sinopec Engineering (Group) Co., Ltd., Beijing
来源：
Huagong Xuebao/CIESC Journal | 2019年 / 70卷 / 02期
关键词：
Algorithm; Integration; Ontology; Optimization; P-graph; Superstructure;
D O I：
10.11949/j.issn.0438-1157.20181353
中图分类号：
学科分类号：
摘要：
Knowledge generation is the basis of industrial knowledge automation. Process industry knowledge is usually generated by directed tasks, such as optimization scheduling, optimization operation, fault diagnosis, etc. The solution generation requires not only understanding the operation mechanism and production data, but also relying on domain expert experience. Such forms of knowledge representation are difficult to unify, poorly ported, and inconvenient to share and reuse. Aiming at the problem of resource scheduling optimization of ethylene cracking furnace group, the P-graph method is used to construct the superstructure model of the solution, which is designed to represent the knowledge of P-graph ontology and database mapping into knowledge rules, and automatically generate RDF (resource description framework) represents the solution knowledge and builds a knowledge repository. Finally, the actual production data of the ethylene production plant was used to verify the feasibility and practicability of the proposed method. © All Right Reserved.
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页码：467 / 474
页数：7
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