Automatic Generation of a Simulation-Based Digital Twin of an Industrial Process Plant

Abstract

The author wrote the manuscript in collaboration with Mr. Miettinen, Mr. Aikala and Mr. Savolainen. The author implemented and tested the proposed method on the Aalto University laboratory process under the guidance of Dr. Karhela and Prof. Vyatkin. Publication IV: "Sliding Mode SISO Control of Model Parameters for Implicit Dynamic Feedback Estimation of Industrial Tracking Simulation Systems" The author wrote the manuscript in collaboration with Mr. Ruusu. Mr. Ruusu developed the conceptual designed of the proposed method and implemented the parameter controller. The author implemented and tested the proposed method on the Aalto University laboratory process under the guidance of Dr. Karhela and Prof. Vyatkin.

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

“…The closest state-of-the-art works and their differences are analyzed as follows. A dynamic digital twin, as defined in Section 1, has been generated from 3D CAD information for the purpose of using process state values from the twin as soft sensors [11]; the approach is not applicable to brownfield plants for which 3D CAD models are generally not available. A qualitative digital twin of the plant has been generated for co-simulating control software against the plant in order to detect logic errors in the virtual commissioning phase [12,48].…”

“…There are different available sources of information at process plants for the automatic generation of a digital display [11], such as datasheets, Process Flow Diagram (PFD) and Piping & Instrumentation Diagram (P&ID) diagrams, IO lists, 3D plant models and logic diagrams. The required information for simulation model creation can be extracted from these documents.…”

“…Essential source information for generating a high-fidelity dynamic simulation model includes pipeline routing details, which are used to determine pressure head losses [10]. If such information is available, for example from 3D CAD models, a dynamic simulation model can be automatically generated [10], and it can be extended to an experimentable digital twin with tracking simulation [11].…”

Integrating 2D and 3D Digital Plant Information Towards Automatic Generation of Digital Twins

“…1). The functionality of the process is not important for the aims of this article, but interested readers will find more details in [4], [9], [10], [37].…”

The Digitization of Quality Control Operations with Cloud Platform Computing Technologies

“…This digital replica of physical assets is called a digital twin, which continuously learns and updates itself from multiple sources. Case studies on digital twins are described in articles about digital twin ergonomic optimization (Caputo et al 2019), digital twin commentary (Tomko and Winter 2019), learning experiences by digital twins (David et al 2018), automatic generation of simulation-based digital twins (Martinez et al 2018), digital twins for legacy systems (Khan et al 2018), possibilities of digital twins technology (Shubenkova et al 2018), and rapid qualification of product by digital twins (Mukherjee and DebRoy 2019).…”