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

Author

Listed:
  • Dong-Han Kang

    (Graduate Institute of Ferrous and Eco Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea)

  • So-Won Choi

    (Graduate Institute of Ferrous and Eco Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea)

  • Eul-Bum Lee

    (Graduate Institute of Ferrous and Eco Materials Technology, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea
    Department of Industrial and Management Engineering, Pohang University of Science and Technology (POSTECH), Pohang 37673, Republic of Korea)

  • Sung-O Kang

    (DofTech Engineering, 83 Baikbum-ro 1 Gil, Mapo-Ku, Seoul 04104, Republic of Korea)

Abstract

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.

Suggested Citation

  • Dong-Han Kang & So-Won Choi & Eul-Bum Lee & Sung-O Kang, 2024. "Auto-Routing Systems (ARSs) with 3D Piping for Sustainable Plant Projects Based on Artificial Intelligence (AI) and Digitalization of 2D Drawings and Specifications," Sustainability, MDPI, vol. 16(7), pages 1-38, March.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:7:p:2770-:d:1364940
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    References listed on IDEAS

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    1. Sung-O Kang & Eul-Bum Lee & Hum-Kyung Baek, 2019. "A Digitization and Conversion Tool for Imaged Drawings to Intelligent Piping and Instrumentation Diagrams (P&ID)," Energies, MDPI, vol. 12(13), pages 1-26, July.
    2. Eun-Seop Yu & Jae-Min Cha & Taekyong Lee & Jinil Kim & Duhwan Mun, 2019. "Features Recognition from Piping and Instrumentation Diagrams in Image Format Using a Deep Learning Network," Energies, MDPI, vol. 12(23), pages 1-19, November.
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