A novel knowledge graph development for industry design: A case study on indirect coal liquefaction process

Hazard and operability analysis (HAZOP) is a remarkable representative in industrial safety engineering. However, a great storehouse of industrial safety knowledge (ISK) in HAZOP reports has not been thoroughly exploited. In order to reuse and unlock the value of ISK and optimize HAZOP, we have developed a novel knowledge graph for industrial safety (ISKG) with HAZOP as the carrier through bridging data science (DS) and engineering design (ED). Specifically, firstly, considering that the knowledge contained in HAZOP reports of different processes in industry is not the same, we have creatively developed a general ISK standardization framework (ISKSF), ISKSF provides a practical scheme for the standardization of HAZOP reports in various processes and the unified representation of different types of ISK, which realizes the integration and circulation of ISK. Secondly, we conceive a novel and reliable information extraction model (HAINEX) based on deep learning combined with DS. HAINEX can effectively mine ISK from HAZOP reports, which alleviates the obstacle of ISK extraction caused by the particularity of HAZOP text. Finally, we build ISK triples based on ISKSF and HAINEX and store them in the Neo4j graph database. We take indirect coal liquefaction process as a case study to develop ISKG, and its oriented applications can optimize HAZOP and mine the potential of ISK, which is of great significance to improve the security of the system and enhance prevention awareness for people. ISKG containing ISKSF and HAINEX sets an example of the interaction between DS and ED for industrial safety, which can enlighten other researchers committed to DS for ED and extend the perspectives of industrial safety.