Towards Semi-Automatic Generation of a Steady State Digital Twin of a Brownfield Process Plant

• Main Authors: Seppo Sierla, Lotta Sorsamäki, Mohammad Azangoo, Antti Villberg, Eemeli Hytönen, Valeriy Vyatkin
• Format: Article
• Language: English
• Published: MDPI AG 2020-10-01
• Series: Applied Sciences
• Subjects: digital twin; industrial process; steady state simulation; directed graph; piping and instrumentation diagram; Balas^®
• Online Access: https://www.mdpi.com/2076-3417/10/19/6959

Researchers have proposed various models for assessing design alternatives for process plant retrofits. Due to the considerable engineering effort involved, no such models exist for the great majority of brownfield process plants, which have been in operation for years or decades. This article proposes a semi-automatic methodology for generating a digital twin of a brownfield plant. The methodology consists of: (1) extracting information from piping and instrumentation diagrams, (2) converting the information to a graph format, (3) applying graph algorithms to preprocess the graph, (4) generating a simulation model from the graph, (5) performing manual expert editing of the generated model, (6) configuring the calculations done by simulation model elements and (7) parameterizing the simulation model according to recent process measurements in order to obtain a digital twin. Since previous work exists for steps (1–2), this article focuses on defining the methodology for (3–5) and demonstrating it on a laboratory process. A discussion is provided for (6–7). The result of the case study was that only few manual edits needed to be made to the automatically generated simulation model. The paper is concluded with an assessment of open issues and topics of further research for this 7-step methodology.