Association Hierarchical Representation Learning for Plant-Wide Process Monitoring by Using Multilevel Knowledge Graph

Abstract

In order to satisfy safety requirements of plant-wide processes, distributed process monitoring methods are often used. However, few of them consider the problem on building multi-level knowledge blocks and the associations between different blocks, all of which are beneficial for plant-wide process monitoring in avoiding information conflict and even to improve monitoring accuracy. To handle these issues, a plant-wide process monitoring method is proposed, which is based on hierarchical graph representation learning with differentiable pooling by using multi-level knowledge graph (MLKG). Specifically, MLKG consists of devices level, subprocess level, process level, etc. Each level has numerous blocks (nodes) which is firstly constructed by priori-knowledge on monitoring variables to calculate the status of key components, such as the Hotelling's statistics. And then normalized mutual information (NMI) is used to obtain the associations between monitoring variables, and the status of each block on each level can be updated. Based on this method, MLKG can be completely constructed. In order to consider the association information of hierarchical representations of MLKG, the hierarchical graph representation learning is used to achieve plant-wide process monitoring. Results of case study on practical cobalt and nickel removal from zinc solution demonstrate the effectiveness and applicability of this method.