Hybrid Digital Twin for process industry using Apros simulation environment

Azangoo, Mohammad; Salmi, Joonas; Yrjola, Iivo; Bensky, Jonathan; Santillan, Gerardo; Papakonstantinou, Nikolaos; Sierla, Seppo; Vyatkin, Valeriy

doi:10.1109/etfa45728.2021.9613416

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…This strategy allows the use of an interpretable model (physics-based) to build a fast DT (machine learning) that will be connected to the PO to support real-time engineering decisions. In addition, ref [ 84 ] shows how to build a hybrid DT model of a heater in a water process system. The work details the steps for updating the physical model and process system using data-driven models of the process equipment.…”

Section: Digital Twin Modelingmentioning

confidence: 99%

Design, Modeling and Implementation of Digital Twins

Segovia

García-Alfaro

2022

Sensors

157

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A Digital Twin (DT) is a set of computer-generated models that map a physical object into a virtual space. Both physical and virtual elements exchange information to monitor, simulate, predict, diagnose and control the state and behavior of the physical object within the virtual space. DTs supply a system with information and operating status, providing capabilities to create new business models. In this paper, we focus on the construction of DTs. More specifically, we focus on determining (methodologically) how to design, create and connect physical objects with their virtual counterpart. We explore the problem into several phases: from functional requirement selection and architecture planning to integration and verification of the final (digital) models. We address as well how physical components exchange real-time information with DTs, as well as experimental platforms to build DTs (including protocols and standards). We conclude with a discussion and open challenges.

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Section: Digital Twin Modelingmentioning

confidence: 99%

Design, Modeling and Implementation of Digital Twins

Segovia

García-Alfaro

2022

Sensors

157

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“…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.…”

Section: Automatic Generation Of Digital Twinsmentioning

confidence: 99%

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

et al. 2022

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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 semiautomated 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.

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“…The DT represents the interconnection and convergence between a physical entity and its digital representation, and it can exchange information in real-time in both directions; in this way, the digital entity can control the physical entity and vice versa [10]. There are also hybrid DTs that use a combination of physics-based models, artificial intelligence, and data to create a more accurate tool, which could even be simpler to create if the dynamics are unknown [11]. One of the main reasons for using a DT is that manufacturers can observe in real time the manufacturing/logistics environment, allowing for optimization and cost reduction.…”

Section: Introductionmentioning

confidence: 99%

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

Caiza,

Sanz

2024

Applied Sciences

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The present research proposes the implementation of an architecture for industrial process monitoring and control for a manufacturing execution system (MES) using an immersive digital twin (DT). For the design of the proposal, cyber–physical systems (CPS), MES, robotics, the Internet of Things, augmented reality, virtual reality, and open platform communication-unified architecture (OPC UA) communication protocols were used to integrate these technologies and enhance the functionalities of the DT by providing greater performance. The proposed work is implemented in an Industry 4.0 laboratory that is composed of Festo Cyber–Physical Factory and CP-Lab stations. The implementation of the architecture is based on ISO 23247, where the following requirements were considered for the design of DTs: (1) observable attributes and 3D design and visualization of all physical production lines in all of their stages, (2) a communication entity through the OPC UA protocol for the collection of state changes of manufacturing elements, (3) a DT entity where digital models are modeled and updated based on the collected data, and (4) user entities through the use of AR and VR to make manufacturing more efficient. The experimental results showed that the architecture enables interoperability between different platforms and control subsystems. It allows for the detection and diagnosis of problems during the execution of the production line; in addition, the high-fidelity simulation and AR and VR environments provided by the DT with data obtained in real time can improve the accuracy and efficiency of manufacturing through a more detailed analysis of the process, providing advantages such as interactive creation for customized products and continuous innovation.

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Hybrid Digital Twin for process industry using Apros simulation environment

Abstract: joonas.salmi, iivo.yrjola, jonathan.bensky}(at)aalto.fi, gerardo.santillan(at)semantum.fi, nikolaos.papakonstantinou(at)vtt.fi, seppo.sierla(at)aalto.fi, valeriy.vyatkin(at)aalto.fi

Cited by 11 publications

References 12 publications

Design, Modeling and Implementation of Digital Twins

Design, Modeling and Implementation of Digital Twins

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

An Immersive Digital Twin Applied to a Manufacturing Execution System for the Monitoring and Control of Industry 4.0 Processes

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