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

Abstract

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.

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

“…To have a comprehensive digital twin generator, it is also necessary to implement and connect generation phases such as simulation software interface implementation, initial condition consideration, and equipment parameterization. To make this procedure more automatic, Sierla et al [9] introduced several rules to convert an intermediate graph model into a format suitable for steady state simulation software. Azangoo et al [42] demonstrated how machine learning can extract process parameters for digital twins from recorded process history.…”

“…Brownfield process systems are functioning plants that may have been developed and built before modern digital systems and may thus lack design information in an Industry 4.0 format. A preliminary study on the semiautomatic generation of a digital twin for a laboratory scale water process plant [9] was recently published. However, it was limited to a laboratory scale process plant with limited types of process equipment.…”

The Digital Twin – Your Ingenious Companion for Process Engineering and Smart Production

“…The authors of [7] attribute the skepticism of many colleagues from the process industry about the assertiveness of a ''digital twin'' to the fact that ''it is often unclear to operators and contractors what added value can be generated from the digital twin and how business models can be designed in the digital world'' and point out that this has already been presented and published, e.g., in [8][9][10]. Nevertheless, many col-leagues from the process industry may indeed find it difficult in individual cases to deduce the economic benefit of a digitalization initiative, in particular as the majority of the publications are not the work of the process and operations engineers themselves, but of (service or academic) colleagues from the automation field, and in fact also describe what is possibleespecially in the area of predictive maintenance -rather than what is needed.…”

Hybrid Digital Twin for process industry using Apros simulation environment

“…There are several applications for them in the process systems like monitoring, control system design, optimization and fault detection. Several attempts have been made by researchers to extract the physics-based Digital Twin from available sources of data in brownfield process systems like Piping and Instrumentation Diagrams (P&IDs) and 3D-scanned or computer-aided design (CAD) models of the systems [4]- [6].…”