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.

AIS: A nonlinear activation function for industrial safety engineering

In the task of Chinese named entity recognition based on deep learning, activation function plays an irreplaceable role, it introduces nonlinear characteristics into neural network, so that the fitted model can be applied to various tasks. However, the information density of industrial safety analysis text is relatively high, and the correlation and similarity between the information are large, which is easy to cause the problem of high deviation and high standard deviation of the model, no specific activation function has been designed in previous studies, and the traditional activation function has the problems of gradient vanishing and negative region, which also lead to the recognition accuracy of the model can not be further improved. To solve these problems, a novel activation function AIS is proposed in this paper. AIS is an activation function applied in industrial safety engineering, which is composed of two piecewise nonlinear functions. In the positive region, the structure combining exponential function and quadratic function is used to alleviate the problem of deviation and standard deviation, and the linear function is added to modify it, which makes the whole activation function smoother and overcomes the problem of gradient vanishing. In the negative region, the cubic function structure is used to solve the negative region problem and accelerate the convergence of the model. Based on the deep learning model of BERT-BiLSTM-CRF, the performance of AIS is evaluated. The results show that, compared with other activation functions, AIS overcomes the problems of gradient vanishing and negative region, reduces the deviation of the model, speeds up the model fitting, and improves the extraction ability of the model for industrial entities.