Detection Method for Energy Efficiency Data in Shell-and-Tube Heat Exchangers Using Multi-Pipeline Segmentation Algorithm


  • Haoyu Wang School of Mechanical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan City, Shandong Province, 250353,China
  • Lili Zhang School of Mechanical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan City, Shandong Province, 250353, China
  • Zizhen Zhao School of Mechanical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan City, Shandong Province, 250353, China
  • Yepeng Du Division, Shandong Sinocera Functional Materials Co., Ltd., Dongying City, Shandong Province, 257000, China
  • Zixu Wang School of Mechanical Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan City, Shandong Province, 250353, China



Energy, Heat Transfer, Shell-and-Tube Heat Exchangers, Detection Method, Multi-Pipeline Segmentation Algorithm, Data Analysis


Shell-and-tube heat exchangers are pivotal in thermal engineering, making the accuracy and quality of the heat transfer data obtained from them essential. Current data monitoring technologies face several challenges, such as increased complexity, noise, and inefficiency in handling the dynamic heat transfer process. This paper introduces a novel approach to enhancing the accuracy and precision of energy transfer data segmentation in shell-and-tube heat exchangers using a multi-pipeline segmentation algorithm. Our methodology integrates data collection with the algorithm's hands-on development, employing advanced techniques to segment and categorize energy transfer data based on real-time system parameters. This creates a robust definition of normal and anomalous operating conditions. Our approach was validated through extensive experiments and simulations, demonstrating superior data accuracy and noise detection compared to traditional methods. Moreover, this innovative segmentation algorithm has potential applications in maintenance forecasting and optimization strategies, ultimately improving energy efficiency. In the future, our algorithm could be extended to other types of heat exchangers or industrial systems, further enhancing their energy efficiency and operational lifespan.


Download data is not yet available.


A. Quartararo, P. A. Di Maio, I. Moscato, E. Vallone, and G. Guagliardo, “A Numerical Approach to Study ShellSide Fluid Flow in Shell-and-Tube Heat Exchangers,” J Phys Conf Ser, vol. 2177, no. 1, p. 012001, Apr. 2022, doi: 10.1088/1742-6596/2177/1/012001. DOI:

R. Prasad, A. Gupta, P. Kumar, and A. K. Mishra, “Mathematical and Computational Analysis of Shell and Tube Heat Exchanger on Varying Tube Patterns in Excel© and Ansys©,” 2022, pp. 235–246, doi: 10.1007/978-98116-8341-1_19. DOI:

R. Venkata Rao and M. Majethia, “Design optimization of shell-and-tube heat exchanger using Rao algorithms and their variants,” Thermal Science and Engineering Progress, vol. 36, p. 101520, Dec. 2022, doi: 10.1016/j.tsep.2022.101520. DOI:

R. Gugulothu, N. Sanke, and A. V. S. S. K. S. Gupta, “Numerical Study of Heat Transfer Characteristics in Shell-and-Tube Heat Exchanger,” 2019, pp. 375–383, doi: 10.1007/978-981-13-1903-7_43. DOI:

F. He and A. Makeev, “Overview of Research on Heat Transfer Technology for Reinforcement of Shell and Tube Heat Exchanger,” Bulletin of Science and Practice, vol. 6, no. 6, pp. 157–166, Jun. 2020, doi: 10.33619/24142948/55/20. DOI:

W. H. Saldanha and P. A. A. M. Junior, “General Pattern Search Applied to the Optimization of the Shell and Tube Heat Exchanger,” International Journal of Advanced Engineering Research and Science, vol. 4, no. 11, pp. 157– 159, 2017, doi: 10.22161/ijaers.4.11.23. DOI:

B. Jayachandraiah and C. Dinesh Kumar Patel, “Design of Shell-and-Tube Heat Exchanger with CFD Analysis,” 2021, pp. 393–400, doi: 10.1007/978-981-15-4488-0_34. DOI:

Š. Gužela, F. Dzianik, M. Juriga, and J. Kabát, “Shell and Tube Heat Exchanger – the Heat Transfer Area

Design Process,” Strojnícky casopis – Journal of Mechanical Engineering, vol. 67, no. 2, pp. 13–24, Nov. 2017, doi: 10.1515/scjme-2017-0014. DOI:

A. S. Pugachuk, E. O. Kalashnikova, N. K. Fominykh, and M. V. Sinkevitch, “Experimental study of heat transfer characteristics of additive shell-and-tube heat exchangers,” 2020, p. 030033, doi: 10.1063/5.0026960. DOI:

S. A. Marzouk, M. M. Abou Al-Sood, E. M. S. El-Said, M. M. Younes, and M. K. El-Fakharany, “A comprehensive review of methods of heat transfer enhancement in shell and tube heat exchangers,” J Therm Anal Calorim, vol. 148, no. 15, pp. 7539–7578, Aug. 2023, doi: DOI:




CORRELATIONS,” in Proceeding of Transport Phenomena in Thermal Engineering. Volume 2, Connecticut: Begellhouse, 2023, pp. 916–921, doi: 10.1615/ISTP-VI.190. DOI:

B. Zheng et al., “An autonomous robot for shell and tube heat exchanger inspection,” J Field Robot, vol. 39, no. 8, pp. 1165–1177, Dec. 2022, doi: 10.1002/rob.22102. DOI:

Alif Gita Arumsari and Petrus Junake Ginting, “Analysis of Heat Transfer Coefficient of Shell and Tube on Heat Exchanger Using Heat Transfer Research Inch (HTRI) Software,” Formosa Journal of Sustainable Research, vol. 2, no. 5, pp. 1175–1184, May 2023, doi: DOI:


G. Ligus, M. Wasilewski, S. Kołodziej, and D. Zając, “CFD and PIV Investigation of a Liquid Flow Maldistribution across a Tube Bundle in the Shell-and-Tube Heat Exchanger with Segmental Baffles,” Energies (Basel), vol. 13, no. 19, p. 5150, Oct. 2020, doi: 10.3390/en13195150. DOI:

A. Yu. Vladova and Yu. R. Vladov, “Detection of oil pipelines’ heat loss via machine learning methods,” IFAC-PapersOnLine, vol. 55, no. 9, pp. 117–121, 2022, doi: 10.1016/j.ifacol.2022.07.021. DOI:

Y. Khetib, H. M. Abo-Dief, A. K. Alanazi, S. M. Sajadi, S. Bhattacharyya, and M. Sharifpur, “Optimization of heat transfer in shell-and-tube heat exchangers using MOGA algorithm: adding nanofluid and changing the tube arrangement,” Chemical Engineering Transactions, vol. 85, pp. 49–54, 2021, doi: 10.3303/CET2185009. DOI:

J. Q. Tan and J. S. Law, “Effect of Fouling on Heat Exchanger Effectiveness: a Review and Case Study,” Thermal Engineering, vol. 2, no. 4, pp. 300–307, 2020, doi: 10.1007/s42853-020-00042-5.

C. M. Jin, P. Wang, L. Y. Luo, X. G. Meng, and Y. S. Wu, “Integrating CFD into the design of shell-and-tube heat exchangers: Potential benefits and pitfalls,” Applied Thermal Engineering, vol. 179, pp. 115810, Jan. 2021, doi: 10.1016/j.applthermaleng.2020.115810. DOI:

V. Dobre and I. Pîrvu, “Experimental Investigation on the Performance of a Shell and Tube Heat Exchanger,” 2022, pp. 211–217, doi: 10.1007/978-3-030-31907-3_19.

L. Sharma and S. K. Sharma, “Energy Performance Assessment of Shell-and-Tube Heat Exchangers

Using Artificial Neural Networks,” Energy and

Buildings, vol. 105, pp. 307–313, Oct. 2015, doi:

1016/j.enbuild.2015.07.030. DOI:

M. Davarnejad, S. Jamshidi, M. R. Mehrnia, and M. B. Oskouie, “Predictive analysis for heat exchanger design using machine learning approaches,” Applied Thermal Engineering, vol. 140, pp. 588–597, 2018, doi: 10.1016/j.applthermaleng.2018.05.146.

A. Aguirre, S. Abella, A. B. Barrientos, J. Guzmán, and J. M. Herrero, “Advanced control systems for shell and tube heat exchangers: State of the art,” Industrial & Engineering Chemistry Research, vol. 59, no. 12, pp. 5441– 5454, Mar. 2020, doi: 10.1021/acs.iecr.9b06852.

D. Borhani, B. Raja, and R. G. Pradeep Kumar, “Thermal performance of shell-and-tube heat exchangers with helical baffles: A critical review,” Renewable and Sustainable Energy Reviews, vol. 91, pp. 1092–1104, Jul. 2018, doi: 10.1016/j.rser.2018.04.041. DOI:

M. M. Abdelgawad, J. H. Lee, “Modeling of heat transfer in shell and tube heat exchangers with experimental verification,” International Journal of Heat and Mass Transfer, vol. 108, pp. 1577–1586, 2017, doi: 10.1016/j.ijheatmasstransfer.2017.01.104. DOI:

M. Milani, F. D. Arrigo, and G. Passoni, “Simulating the complex dynamics in shell and tube heat exchangers: Techniques and predictions,” Computers & Fluids, vol. 182, pp. 103–115, May 2019, doi: 10.1016/j.compfluid.2019.02.006. DOI:




How to Cite

Wang H, Zhang L, Zhao Z, Du Y, Wang Z. Detection Method for Energy Efficiency Data in Shell-and-Tube Heat Exchangers Using Multi-Pipeline Segmentation Algorithm. EAI Endorsed Trans Energy Web [Internet]. 2024 May 30 [cited 2024 Jul. 13];11. Available from: