Research On Ontology-driven Based Modeling For DEVS Simulation Of Refinery

Petrochemical industry is a pillar enterprise in national economy.How to assist decision-makers for intelligent manufacturing by efficient,intuitive and rapid integration of information resources remains an important topic.There are many challenges in integration of intelligent manufacturing due to its inherent complexity.The main drawback of traditional modeling software lies in that the modeling individual perceive the knowledge differently and cannot model the knowledge uniformly,which leads to knowledge reuse and share problem.As a result,a waste of human resources and time is inevitable in system integration and model updating.How to handle knowledge modeling and management more effectively is a key to the information system of intelligent manufacturing.In this thesis,the traditional computer integrated process system(CIPS)of petrochemical enterprises is analyzed and an ontology-driven based modeling framework for DEVS simulation is proposed by applying the ontology-driven modeling theory according to the specific simulation requirements and characteristics of discrete event.Firstly,this thesis introduces the multi-level structure(ERP/MES/PCS)and simulation requirements of CIPS in petrochemical enterprises.The problems of modeling for petrochemical enterprises are discussed and ontology technology can be adopted to solve the knowledge reuse and sharing problem in modeling and simulation(M&S)field.Ontology-driven theory and its advantages in modeling are described in detail.Next,modeling specification of the domain ontology and OntoCAPE ontology are discussed respectively.The extension of OntoCAPE will reduce workload of ontology construction.Besides,the DEVS modeling paradigm is proposed based on the discrete characteristics of petrochemical enterprises and ontology mapping technology is developed to map the domain ontology to the DEVS model ontology,which further reuses knowledge of domain ontology.Considering that the relational database contains a large amount of model data in the existing system,knowledge extraction instead of artificial construction is designed to build ontology instances,which enhances the efficiency of modeling.Combined with the ontology mapping and ontology translation,the ontology modeling framework can generate model code by data.SWRL semantic rules are considered in the ontology to check for model consistency,which will decrease model debugging.Then,implementations of knowledge extraction,ontology mapping and ontology translation in ontology modeling framework are described.This thesis also presented the low coupling integration for generated plant model and existing logistics simulation platform by Web.Moreover,the ontology mapping and translation tools are developed to illustrate the generation process of simulation model.Finally,the relevant logistics simulation scenes based on the proposed ontology-driven based modeling framework for DEVS simulation of refinery are designed.The feasibility and correctness of the framework are verified from model code and numerical simulation results.