Ontology guided multi-level knowledge graph construction and its applications in blast furnace ironmaking process

Abstract

Due to the widespread presence of knowledge in factories, integrating various types of knowledge to solve different tasks in industrial production processes, including prediction, diagnosis, and control tasks, is of great significance and challenging. Knowledge graph, as a method of knowledge representation, holds significant promise for addressing challenges within industrial contexts. However, current research on knowledge graph has a limitation in that task related knowledge graph focus on structure information, while ignoring semantic and logical information in knowledge. Additionally, the existing ontologies designed for industrial production lack adaptability to cater to the diverse needs of different industrial tasks. This paper proposes a multi-level knowledge graph defined by ontology to introduce semantics and further explore the methods for combining semantics to complete practical industrial tasks. To ensure the accurate sampling of heterogeneous nodes, four semantic templates are generated using if-then rule logic. Different kinds of neighbor nodes are defined through the if-then rule logic, leading to a accelerated generation of target subgraphs related to different tasks. In this way, the plant-wide distributed computing for fault diagnosis tasks can be easily realized. Furthermore, this paper introduces a framework for semantics extraction and graph embedding based on multi-information fusion. This framework integrates semantic information, structural information, and node attribute information within the graph to deliver a holistic feature representation for prediction and control tasks. We take blast furnace ironmaking process as an industrial case study and the experimental results demonstrate the crucial role of semantics in enhancing the knowledge expression capability of graphs. Based on the blast furnace simulation experiment platform, the proposed method achieves 92.76% accuracy in the blast furnace fault diagnosis task, and the diagnosis time is reduced by 58.44% compared with the traditional rule-based method. In the self-healing control task of the blast furnace, the proposed graph embedding method can achieve a complete control process in three types of blast furnace faults: blowing out, tuyere failure, and low stockline. The control effect can be comparable to manual operation.