A novel knowledge graph development for industry design: A case study on indirect coal liquefaction process
Zhenhua Wang, Beike Zhang, Dong Gao

TL;DR
This paper introduces a novel knowledge graph for industrial safety (ISKG) based on HAZOP reports, using a standardization framework and deep learning for information extraction, demonstrated through a case study on indirect coal liquefaction.
Contribution
It develops a general ISK standardization framework and a deep learning-based information extraction model for HAZOP reports, enabling effective knowledge reuse and safety analysis.
Findings
Successfully built ISKG for indirect coal liquefaction process
Enhanced safety analysis and hazard prevention through the knowledge graph
Provides a practical framework for integrating diverse industrial safety knowledge
Abstract
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 and engineering design. Specifically, firstly, considering that the knowledge contained in HAZOP reports of different processes in industry is not the same, we creatively develope a general ISK standardization framework, it provides a practical scheme for integrating HAZOP reports from various processes and uniformly representing the ISK with diverse expressions. Secondly, we conceive a novel and reliable information extraction model based on deep learning combined with data…
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Taxonomy
TopicsOccupational Health and Safety Research · Risk and Safety Analysis · Evaluation and Optimization Models
MethodsMemory Network
