Research On Standardization Method Of Safety Evaluation Information For Chemical Process

The petrochemical industry is one of the pillar industries in China.In recent years,with the development of the economy,the petrochemical industry has developed rapidly,the scale has become larger and larger,the process has become more and more complicated,and people have paid more and more attention to process safety.In order to ensure that the factory can carry out safe production,more and more researchers have done a lot of research on process safety.The most effective method is to conduct hazard analysis on the reaction equipment.Process hazard analysis is very important to proactively identify potential safety issues and suggest feasible mitigation measures.Among the existing process hazard analysis technologies,hazard and operability analysis is the most widely used safety analysis methods in crude oil and natural gas processing.The traditional methods of hazard and operability analysis are developed in the form of brainstorming,and the analysis results are stored in the form of documents,so that a large number of analysis cases cannot be shared and reused,and the analysis results based on computer software can only be analyzed by the corresponding analysis software.Recognition also has the problem of being difficult to reuse and share,resulting in a large amount of data that cannot be fully utilized.Therefore,the research on the sharing and reuse of existing chemical process safety analysis data is of great significance.Based on the data of hazard and operability analysis report of oil synthesis equipment of coal seam indirect liquefaction project,the methods were proposed to solve the problem of data sharing and reuse in petrochemical industry in this paper.The first method is to use the characteristics of knowledge ontology to share and reuse,and then using natural language technology to extract the data in the report to construct the domain knowledge ontology,and store it in the OWL file,then using Protege software to visualize the knowledge ontology to facilitate knowledge management and knowledge verification,the final results show that the method can well express the project's hazard and operability analysis text data,and can realize the sharing and reuse of the petrochemical industry's hazard and operability analysis data;The second method is to build a knowledge graph of hazard and operability analysis in the petrochemical industry,which can also achieve the purpose of data sharing and reuse.Named entity recognition is an important part of the process of building a knowledge graph.The main work of this paper is to use deep learning technology to identify named entities for hazard and operability analysis data,and lay the foundation for the construction of the knowledge graph.Finally,through experimental comparison,it can be seen that the named entity recognition model proposed in this paper has a better named entity recognition effect than the existing models in the field.