Abstract
Hazard and operability (HAZOP) is an important safety analysis method, which is widely used in the safety evaluation of petrochemical industry. The HAZOP analysis report contains a large amount of expert knowledge and experience. In order to realize the effective expression and reuse of knowledge, the knowledge ontology is constructed to store the risk propagation path and realize the standardization of knowledge expression. On this basis, a comprehensive algorithm of ontology semantic similarity based on the ant clony optimization generalized neural network (ACO-GRNN) model is proposed to improve the accuracy of semantic comparison. This method combines the concept name, semantic distance, and improved attribute coincidence calculation method, and ACO-GRNN is used to train the weights of each part, avoiding the influence of manual weighting. The results show that the Pearson coefficient of this method reaches 0.9819, which is 45.83% higher than the traditional method. It could solve the problems of semantic comparison and matching, and lays a good foundation for subsequent knowledge retrieval and reuse.
Highlights
When one analyzes the possible causes, consequences, safeguard procedures and safety tips of a deviation, one obtains its propagation path, which constitutes the Hazard and operability (HAZOP) analysis results. This method considers the inherent danger of the equipment, and considers the dynamic danger when the state parameters deviate in the process flow, comprehensively predicts the danger of the process, and has been widely used in safety analysis
This paper proposes a comprehensive calculation method of ontology semantic similarity based on ACO-GRNN
Pearson coefficient of prothe posed algorithm is improved by compared with the linear weighting algorithm, proposed algorithm is improved by 45.83% compared with the linear weighting algo15.89%15.89%
Summary
HAZOP analysis studies the hazards and operability of the system by exploring the impact of the deviation which means that process factors off normal process design conditions (including equipment failure, human misoperation, etc.) [1]. When one analyzes the possible causes, consequences, safeguard procedures and safety tips of a deviation, one obtains its propagation path, which constitutes the HAZOP analysis results. This method considers the inherent danger of the equipment, and considers the dynamic danger when the state parameters deviate in the process flow, comprehensively predicts the danger of the process, and has been widely used in safety analysis. The complexity of industrial application scenarios requires efficient, low-cost, and reusable
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