An ontology for distributed process supervision of large-scale chemical plants

Abstract

As modern chemical processes evolve into extremely large and complex systems involving multiple third-party technologies, instruments, and software, the information necessary for process supervision is distributed across disparate sources. Supervision methods rely on models that are themselves based on these process descriptors. Therefore, any changes during operations have to be reflected to the supervision methods so that they can adapt suitably. In this paper, we propose an ontology, called OntoSafe, that provides the semantics for abnormal situation management. It enables the integration of all the information necessary for forming a cohesive judgment of the condition and state of the process. It also captures the underlying linkages so that changes in the process descriptors are propagated consistently. We illustrate the benefits of these using the ENCORE distributed intelligence system. The application to an offshore oil and gas production case study is also reported. © 2012 Elsevier Ltd.