PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 4, 20261 citationsOpen Access

Intelligent Monitoring and Early Warning Diagnosis Technology for Ethylene Cracking Furnace Tubes: A Review of Current Status and Future Prospects

View Full Paper
JRJia-Kuan RenXXXiu-Qing XuZLZhi-Hong Li

Key Points

  • The review aims to explore and summarize the current technologies for monitoring ethylene cracking furnace tubes.
  • Systematic review of monitoring technologies for cracking furnace tubes
  • Analysis of traditional and emerging monitoring methods
  • Assessment of data-driven and expert integration models
  • Introduction of industrial application cases for monitoring systems
  • Discussion of current challenges and future trends in the field
  • Identified key thermal and coking effects impacting furnace tube operation
  • Reviewed various monitoring techniques such as infrared thermometry and acoustic emission
  • Discussed the evolution from white-box to gray-box diagnostic models
  • Highlighted challenges in data fusion and real-time performance
  • Outlined future trends including digital twins and edge intelligence systems

Abstract

As the “flagship” unit of the petrochemical industry, the operational status of ethylene cracking furnaces directly impacts the stability and efficiency of the entire production chain. During long-term operation under extreme temperatures and complex reaction environments, cracking furnace tubes face core bottlenecks primarily related to thermal and coking effects, such as coke deposition, tube metal overheating, and associated creep damage, which restrict the long-term, safe, and efficient operation of the unit. This paper systematically reviews the key technologies for condition monitoring of cracking furnace tubes, providing an in-depth analysis of various monitoring methods—from traditional infrared thermometry and acoustic emission to emerging optical fiber sensing—covering their working principles, application status, and inherent limitations. Furthermore, it elaborates on the evolution from mechanism-based “white-box” models to data-driven “black-box” models, and further to “gray-box” intelligent diagnostic models that integrate expert knowledge. Industrial application cases of integrated monitoring and diagnostic systems are also introduced. Finally, the paper critically addresses the current severe challenges in data fusion, model generalization, real-time performance, and cost-effectiveness, while outlining future development trends toward digital twins, cross-modal fusion, edge intelligence, and self-evolving systems. The aim is to provide valuable references for technological innovation and engineering applications in this field.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Ren et al. (2026) studied this question.

synapsesocial.com/papers/69a7cd9dd48f933b5eeda134https://doi.org/10.3390/pr14050811
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Failure analysis and prevention of cracking furnace tube in ethylene plant2025
  2. 2Analysis of Flow-induced Noise Characteristics of Ethylene Cracking Furnace Tubes before and after Coking2024
  3. 3Portable Eddy Current Inspection System with Real-Time SVM Classification of the Aging States of Steam Reforming Furnace Tubes2024 · 1 citations
  4. 4Data-Driven Cyclic Scheduling Optimization of Industrial Ethylene Furnace Systems for Lower-Carbon Production Considering Cracking and Decoking CO 2 Emissions2026
  5. 5Development of a Boiler Monitoring System for Predictive Maintenance of Boiler Tube Failures2026 · 1 citations