A Methodology for Generating a Digital Twin for Process Industry: A Case Study of a Fiber Processing Pilot Plant

Mohammad Azangoo, Lotta Sorsamäki, Seppo A. Sierla, Teemu Mätäsniemi, Miia Rantala, Kari Rainio, Valeriy Vyatkin

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)


Digital twins are now one of the top trends in Industry 4.0, and many companies are using them to increase their level of digitalization, and, as a result, their productivity and reliability. However, the development of digital twins is difficult, expensive, and time consuming. This article proposes a semi-automated methodology to generate digital twins for process plants by extracting process data from engineering documents using text and image processing techniques. The extracted information is used to build an intermediate graph model, which serves as a starting point for generating a model in a simulation software. The translation of a graph-based model into a simulation software environment necessitates the use of simulator-specific mapping rules. This paper describes an approach for generating a digital twin based on a steady state simulation model, using a Piping and Instrumentation Diagram (P&ID) as the main source of information. The steady state modeling paradigm is especially suitable for use cases involving retrofits for an operational process plant, also known as a brownfield plant. A methodology and toolchain is proposed, consisting of manual, semi-automated and fully automated steps. A pilot scale brownfield fiber processing plant was used as a case study to demonstrate our proposed methodology and toolchain, and to identify and address issues that may not occur in laboratory scale case studies. The article concludes with an evaluation of unresolved concerns and future research topics for the automated development of a digital twin for a brownfield process system.

Original languageEnglish
Pages (from-to)58787-58810
Number of pages24
JournalIEEE Access
Publication statusPublished - 2022
MoE publication typeA1 Journal article-refereed


  • Digital twin
  • Directed graph
  • Flowsheet population
  • Image recognition
  • Modeling
  • Piping and instrumentation diagram
  • Process industry
  • Steady state simulation
  • Text recognition


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