Integration, navigation and exploration of plant topology networks using the property-graph model

Abstract

Abstract-Understanding the process topology is essential for many process systems engineering activities. Previous works in this area have explored the extraction of connectivity and causality from different sources by using structural and process data. A few works, however, have focused on how to integrate, store, and visualise connectivity models, which are becoming available in the literature. This paper proposes a method for integration, navigation and exploration of topology models in an efficient way, which can be applied to the study of connectivity and causality in chemical processes. I. INTRODUCTIONDisturbances in continuous processes commonly travel along the product stream through different propagation paths affecting the process performance. Abnormalities that propagate plantwide include oscillations caused by sticking valves, disturbances produced by interacting controllers, instabilities in the systems such as slugging flows, and vibrations caused by interacting units (for example in a turbine-generator train).Since large-scale chemical facilities are highly integrated systems containing recycle flows and utility systems, units are not independent anymore. Connectivity and causality have been concepts proposed for describing dependencies between units and variables [1]. Several authors have been applying new methods that make it possible to extract process topology describing causality relationships from process knowledge and process data. The knowledge from process topology has many applications, for instance in the field of plantwide analysis, where the use of process connectivity can enhance the isolation and diagnosis of the root-cause of plantwide disturbances [2], [3]. There is also evidence of use of topology in other areas such as alarm rationalization [4], risk assessment [5] and control structure design [6]. It seems now that mining the data and knowledge about topology (connectivity and causality) is a largely solved problem. What becomes challenging now is to integrate all these insights into a single topology model and make it possible for engineers to use it for exploration, navigation and more effective visualisation.This work is supported Marie Curie FP7 project ENERGY-SMARTOPS, Contract No: PITN-GA-2010-264940.David Dorantes Romero is with ABB Technology and Innovation, Norway, Ole Deviks vei 10, david.romero@no.abb.com. He is also part-time PhD student at the Department of Chemical Engineering at Imperial College.Nina F. Thornhill is with the Centre for Process Systems engineering, Department of Chemical Engineering, Imperial College London, London SW7 2AZ, U.K.The main contribution of this paper is the proposal of a method to integrate process connectivity and causality information into a plant topology network, making use of interaction and techniques to facilitate its navigation and exploration. Examples will also be given based on a novel prototype called Topoviz™, currently under development. II. STATE OF THE ARTInformation about the topology can be captured from modelbased m...