Next-generation 5G fusion-based intelligent health-monitoring platform for ethylene cracking furnace tube.

Journal: Mathematical biosciences and engineering : MBE
Published Date: Jun 22, 2022

Abstract

This study aimed to develop a 5G + "mixed computing" + deep learning-based next-generation intelligent health-monitoring platform for an ethylene cracking furnace tube based on 5G communication technology, with the goal of improving the health management level of the key component of ethylene production, that is, the cracking furnace tube, and focusing on the key common technical difficulties of ethylene production of tube outer-surface temperature sensing and tube slagging diagnosis. It also integrated the edge-fog-cloud "mixed computing" technology and deep learning technology in artificial intelligence, which had a higher degree in the research and development of automation and intelligence, and was more versatile in an industrial environment. The platform included a 5G-based tube intelligent temperature-measuring device, a 5G-based intelligent peep door gearing, a 5G-based edge-fog-cloud collaboration mechanism, and a mixed deep learning-related application. The platform enhanced the automation and intelligence of the enterprise, which could not only promote the quality and efficiency of the enterprise but also protect the safe operation of the cracking furnace device and lead the technological progress and transformation and upgrading of the industry through the application.

Authors

  • Delong Cui
    College of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming 525000, China.
  • Hong Huang
    Department of Ecology and Evolution, University of Lausanne, 1015 Lausanne, Switzerland, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland, Department of Microbiology and Immunology and Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore MD, USA, SIB Swiss Institute of Bioinformatics, 1 Rue Michel Servet, 1211 Geneva, Switzerland, Department of Medicine and Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore MD, USA, Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA 94158, USA, School of Information, University of South Florida, Tampa, FL, 33647, USA, Genomics Division, Lawrence Berkeley National Lab, 1 Cyclotron Rd., Berkeley, 94720 CA USA, European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK, Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, Geneva, Switzerland, ETH Zurich, Department of Computer Science, Universitätstr. 19, 8092 Zürich, Switzerland, SIB Swiss Institute of Bioinformatics, Universitätstr. 6, 8092 Zürich, Switzerland and University College London, Gower St, London WC1E 6BT, UK.
  • Zhiping Peng
    School of Automation, Guangdong University of Petrochemical Technology, Maoming 525000, PR China. Electronic address: pengzp@gdupt.edu.cn.
  • Qirui Li
    College of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming 525000, China.
  • Jieguang He
    College of Computer and Electronic Information, Guangdong University of Petrochemical Technology, Maoming 525000, China.
  • Jinbo Qiu
    College of Electronic Information Engineer, Guangdong University of Petrochemical Technology, Maoming, China.
  • Xinlong Luo
    College of Electronic Information Engineer, Guangdong University of Petrochemical Technology, Maoming, China.
  • Jiangtao Ou
    AI Sensing Technology, Chancheng District, Foshan, China.
  • Chengyuan Fan
    AI Sensing Technology, Chancheng District, Foshan, China.

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