An indirect temperature measurement of oil pipelines based on embedded devices. (15th July 2020)

• Record Type: Journal Article
• Authors: Huan, Huiting; Liu, Lixian; Chen, Xiaolong; Peng, Cuiling; Zhan, Jinsong; Liu, Qiang; Yao, Yi
• Is Part Of: Measurement. Volume 159(2020)
• Publication Date: 2020-07-15
• Subjects: Embedded devices -- Temperature measurement -- Green's function -- Oil pipelines; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2020.107747
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Real-time temperature prediction of oil pipeline is possible with embedded devices. Measured temperature error was less than 0.15 K from multilayer model. Green's function abates algorithm complexity and enables fast calculation. Edge device determines temperature independently in "smart pipe" IoT framework. Abstract: High-density polymer composite pipelines are widely used in oil industry due to its high strength, endurance and corrosion resistance. There is a practical need for monitoring oil temperature noninvasively for pipeline maintenance. However, real-time temperature measurement of oil is difficult based on sensors installed outside the insulation pipe wall. Thermal field calculation is harder for a microprocessor-based embedded device. This article proposed two theoretical models of temperature field in composite pipes with the help of Green's function. Analytical series forms were derived and implemented on microprocessors to determine fluid temperature indirectly. Computational and modelling complexity were discussed with respect to the integration transform and eigenfunctions. We showed experimentally that both the rigorous multilayer theory and equivalent heat transfer model have good accuracy in predicting temperature inside the composite pipe. The multilayer model has a better performance with maximum error lowered up to 0.15 K calculated by a commercial microprocessor.