Generating an industrial process graph from 3D pipe routing information

Sierla, Seppo; Azangoo, Mohammad; Vyatkin, Valeriy

doi:10.1109/etfa46521.2020.9212175

Cited by 7 publications

(3 citation statements)

References 21 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This technique, recognizing pipes and elbows through a center line and a connection method based on a CNN neural network, was noted to be sensitive to noise and occlusion. Sierla et al [49] extracted control loop information from P&ID and 3D CAD drawings and converted it into a graph. They developed a Data Exchange in the Process Industry (DEXPI) algorithm to obtain an integrated graph model.…”

Section: Three-dimensional Modeling Layout For Plant Projectmentioning

confidence: 99%

Auto-Routing Systems (ARSs) with 3D Piping for Sustainable Plant Projects Based on Artificial Intelligence (AI) and Digitalization of 2D Drawings and Specifications

Kang,

Choi,

Lee

et al. 2024

Sustainability

View full text Add to dashboard Cite

The engineering sector is undergoing digital transformation (DT) alongside shifts in labor patterns. This study concentrates on piping design within plant engineering, aiming to develop a system for optimal piping route design using artificial intelligence (AI) technology. The objective is to overcome limitations related to time and costs in traditional manual piping design processes. The ultimate aim is to contribute to the digitalization of engineering processes and improve project performance. Initially, digital image processing was utilized to digitize piping and instrument diagram (P&ID) data and establish a line topology set (LTS). Subsequently, three-dimensional (3D) modeling digital tools were employed to create a user-friendly system environment that visually represents piping information. Dijkstra’s algorithm was implemented to determine the optimal piping route, considering various priorities during the design process. Finally, an interference avoidance algorithm was used to prevent clashes among piping, equipment, and structures. Hence, an auto-routing system (ARS), equipped with a logical algorithm and 3D environment for optimal piping design, was developed. To evaluate the effectiveness of the proposed model, a comparison was made between the bill of materials (BoM) from Company D’s chemical plant project and the BoM extracted from the ARS. The performance evaluation revealed that the accuracy in matching pipe weight and length was 105.7% and 84.9%, respectively. Additionally, the accuracy in matching the weight and quantity of fittings was found to be 99.7% and 83.9%, respectively. These findings indicate that current digitalized design technology does not ensure 100% accurate designs. Nevertheless, the results can still serve as a valuable reference for attaining optimal piping design. This study’s outcomes are anticipated to enhance work efficiency through DT in the engineering piping design sector and contribute to the sustainable growth of companies.

show abstract

Section: Three-dimensional Modeling Layout For Plant Projectmentioning

confidence: 99%

Auto-Routing Systems (ARSs) with 3D Piping for Sustainable Plant Projects Based on Artificial Intelligence (AI) and Digitalization of 2D Drawings and Specifications

Kang,

Choi,

Lee

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…However, the extracted graph models were at varying levels of abstraction, making it difficult to compare them for validation purposes. Several algorithms have been proposed for converting a 3D CAD generated graph to make it at the same level of abstraction as a P&ID generated graph [41]. Siemens and Bentley Systems [39] collaborated to create an as-built digital twin of a brownfield process plant using a variety of data sources, including 1D (datasheets, lists, records), 2D (drawings, logical connectivity), and 3D (physical layout, sizes).…”

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…The automatic generation of Digital Twins is an emerging research field and can save time and reduce costs. It can also respond to high demands from industries that want to digitize their production lines and systems [3]. For the generation of Digital Twins we may create a simulation model and specify its connection to the real system.…”

Section: Introductionmentioning

confidence: 99%

Digital Twin-Assisted Controlling of AGVs in Flexible Manufacturing Environments

Azangoo

Taherkordi

Blech

et al. 2021

2021 IEEE 30th International Symposium on Industrial Electronics (ISIE)

Self Cite

View full text Add to dashboard Cite

Digital Twins are increasingly being introduced for smart manufacturing systems to improve the efficiency of the main disciplines of such systems. Formal techniques, such as graphs, are a common way of describing Digital Twin models, allowing broad types of tools to provide Digital Twin based services such as fault detection in production lines. Obtaining correct and complete formal Digital Twins of physical systems can be a complicated and time consuming process, particularly for manufacturing systems with plenty of physical objects and the associated manufacturing processes. Automatic generation of Digital Twins is an emerging research field and can reduce time and costs. In this paper, we focus on the generation of Digital Twins for flexible manufacturing systems with Automated Guided Vehicles (AGVs) on the factory floor. In particular, we propose an architectural framework and the associated design choices and software development tools that facilitate automatic generation of Digital Twins for AGVs. Specifically, the scope of the generated digital twins is controlling AGVs in the factory floor. To this end, we focus on different control levels of AGVs and utilize graph theory to generate the graph-based Digital Twin of the factory floor.

show abstract

Generating an industrial process graph from 3D pipe routing information

Cited by 7 publications

References 21 publications

Auto-Routing Systems (ARSs) with 3D Piping for Sustainable Plant Projects Based on Artificial Intelligence (AI) and Digitalization of 2D Drawings and Specifications

Auto-Routing Systems (ARSs) with 3D Piping for Sustainable Plant Projects Based on Artificial Intelligence (AI) and Digitalization of 2D Drawings and Specifications

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

Digital Twin-Assisted Controlling of AGVs in Flexible Manufacturing Environments

Contact Info

Product

Resources

About