Objective The production process of natural gas purification is characterized by flammability, explosiveness, continuity, and complexity, all of which pose substantial safety risks. Fault attribution and tracing are crucial for operators to identify potential hazards, prevent accidents, and guarantee safe production. Furthermore, they provide invaluable guidance for engineering personnel in their operations. Knowledge graphs can efficiently store and manage the vast amount of process technical regulations and fault handling logs in chemical production, offering data support for downstream tasks such as fault tracing and enhancing the efficiency of maintenance personnel. However, most existing production and maintenance data are recorded and stored in unstructured text data, curtailing the potential for directly constructing a knowledge graph. To address this challenge, this study proposes a knowledge extraction method that combines a bidirectional long short-term memory network (BiLSTM) with conditional random field (CRF) and rule matching.

Method Firstly, production or operation and maintenance data pertaining to relevant industrial processes are gathered as original data and subjected to preprocessing. Secondly, the method integrating BiLSTM-CRF and rule matching was employed for knowledge extraction. Finally, the extracted data was stored in the graph database.

Result In this study, the flash tank device in a natural gas purification plant was taken as an example. The constructed knowledge graph was basically consistent with the theoretical graph structure constructed by expert experience.

Conclusion The proposed model can effectively extract knowledge about the device's production operation and maintenance data. The constructed knowledge graph enhances the data readability and facilitates ease of query and learning for operation and maintenance personnel.