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Cross-heating-rate prediction of thermogravimetry of PVC and XLPE cable insulation material: a novel artificial neural network framework

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Abstract

The analysis of thermogravimetric data of material at multiple heating rates is very labor-intensive and time-consuming. To provide an accurate and effective prediction of the thermogravimetric (TG) curves at various heating rates, this work presents a novel artificial neural network (ANN) framework for cross-heating-rate prediction on the TG curves of commonly used cable insulation materials. The proposed ANN framework consists of data transformation and division techniques that differ from previous studies. By comparing the actual test results and predicted TG results of polyvinyl chloride (PVC), the effectiveness of the proposed ANN framework in the cross-heating-rate prediction of TG curves is validated. By which, the relationship between heating rates and conversion rates can be reliably captured, demonstrating the capability of the proposed ANN framework in interpreting cross-heating-rate TG data. In addition to PVC, the proposed ANN framework has been extended to analyze the TG curves of XLPE.

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References

  1. Chen RY, Lu SX, Zhang Y, Lo SM. Pyrolysis study of waste cable hose with thermogravimetry/Fourier transform infrared/mass spectrometry analysis. Energy Convers Manag. 2017;153:83–92. https://doi.org/10.1016/j.enconman.2017.09.071.

    Article  CAS  Google Scholar 

  2. Wang Z, Wang J. Comparative thermal decomposition characteristics and fire behaviors of commercial cables. J Therm Anal Calorim. 2021;144(4):1209–18. https://doi.org/10.1007/s10973-020-10051-z.

    Article  CAS  Google Scholar 

  3. Xie Q, Zhang H, Tong L. Experimental study on the fire protection properties of PVC sheath for old and new cables. J Hazard Mater. 2010;179(1–3):373–81. https://doi.org/10.1016/j.jhazmat.2010.03.015.

    Article  CAS  Google Scholar 

  4. Huang XY, Nakamura Y. A review of fundamental combustion phenomena in wire fires. Fire Technol. 2020;56(1):315–60. https://doi.org/10.1007/s10694-019-00918-5.

    Article  Google Scholar 

  5. He H, Zhang QX, Tu R, Zhao LY, Liu J, Zhang YM. Molten thermoplastic dripping behavior induced by flame spread over wire insulation under overload currents. J Hazard Mater. 2016;320:628–34. https://doi.org/10.1016/j.jhazmat.2016.07.070.

    Article  CAS  Google Scholar 

  6. Courty L, Garo JP. External heating of electrical cables and auto-ignition investigation. J Hazard Mater. 2017;321:528–36. https://doi.org/10.1016/j.jhazmat.2016.09.042.

    Article  CAS  Google Scholar 

  7. Meinier R, Sonnier R, Zavaleta P, Suard S, Ferry L. Fire behavior of halogen-free flame retardant electrical cables with the cone calorimeter. J Hazard Mater. 2018;342:306–16. https://doi.org/10.1016/j.jhazmat.2017.08.027.

    Article  CAS  Google Scholar 

  8. Parise G, Hesla E, Mardegan CS, Parise L, Capaccini EB. Measures to minimize series faults in electrical cords and extension cords. IEEE Trans Ind Appl. 2019;55(5):4551–6. https://doi.org/10.1109/tia.2019.2926240.

    Article  Google Scholar 

  9. Kerekes Z, Restas A, Lubloy E. The effects causing the burning of plastic coatings of fire-resistant cables and its consequences. J Therm Anal Calorim. 2020;139(2):775–87. https://doi.org/10.1007/s10973-019-08526-9.

    Article  CAS  Google Scholar 

  10. Mo SJ, Zhang J, Liang D, Chen HY. Study on Pyrolysis Characteristics of Cross-linked Polyethylene Material Cable. In: Yao HW, editor. 2012 International Conference on Performance-Based Fire and Fire Protection Engineering. Procedia Engineering, 2013. p. 588–92.

  11. Hanley TL, Burford RP, Fleming RJ, Barber KW. A general review of polymeric insulation for use in HVDC cables. IEEE Electr Insul Mag. 2003;19(1):13–24. https://doi.org/10.1109/mei.2003.1178104.

    Article  Google Scholar 

  12. Wang Z, Wei R, Wang X, He J, Wang J. Pyrolysis and combustion of polyvinyl chloride (PVC) sheath for new and aged cables via thermogravimetric analysis-fourier transform infrared (TG-FTIR) and calorimeter. Materials. 2018. https://doi.org/10.3390/ma11101997.

    Article  Google Scholar 

  13. Wang CJ, Liu HR, Zhang JQ, Yang SL, Zhang Z, Zhao WP. Thermal degradation of flame-retarded high-voltage cable sheath and insulation via TG-FTIR. J Anal Appl Pyrol. 2018;134:167–75. https://doi.org/10.1016/j.jaap.2018.06.005.

    Article  CAS  Google Scholar 

  14. Du Y, Jiang X, Lv G, Jin Y, Wang F, Chi Y, et al. TG-DSC and FTIR study on pyrolysis of irradiation cross-linked polyethylene. J Mater Cycles Waste Manag. 2017;19(4):1400–4. https://doi.org/10.1007/s10163-016-0530-z.

    Article  CAS  Google Scholar 

  15. Wang Z, Xie T, Ning X, Liu Y, Wang J. Thermal degradation kinetics study of polyvinyl chloride (PVC) sheath for new and aged cables. Waste Manag. 2019;99:146–53. https://doi.org/10.1016/j.wasman.2019.08.042.

    Article  CAS  Google Scholar 

  16. Benes M, Milanov N, Matuschek G, Kettrup A, Placek V, Balek V. Thermal degradation of PVC cable insulation studied by simultaneous TG-FTIR and TG-EGA methods. J Therm Anal Calorim. 2004;78(2):621–30. https://doi.org/10.1023/B:JTAN.0000046123.59857.ad.

    Article  CAS  Google Scholar 

  17. Marcilla A, Beltran M. Thermogravimetric kinetic-study of poly(vinyl chloride) pyrolysis. Polym Degrad Stab. 1995;48(2):219–29. https://doi.org/10.1016/0141-3910(95)00050-v.

    Article  CAS  Google Scholar 

  18. Ohrbach KH, Radhoff G, Kettrup A. Investigations of the thermal-decomposition of PVC cable material by means of simultaneous TG-DTG-DTA-MS analysis. Thermochim Acta. 1985;85:403–6.

    Article  CAS  Google Scholar 

  19. Vyazovkin S, Burnham AK, Criado JM, Perez-Maqueda LA, Popescu C, Sbirrazzuoli N. ICTAC kinetics committee recommendations for performing kinetic computations on thermal analysis data. Thermochim Acta. 2011;520(1–2):1–19. https://doi.org/10.1016/j.tca.2011.03.034.

    Article  CAS  Google Scholar 

  20. Friedman HL. Kinetics of thermal degradation of char-forming plastics from thermogravimetry. Application to a phenolic plastic. J Polym Sci Part C Polym Symp. 1964;6(1):183–95. https://doi.org/10.1002/polc.5070060121.

    Article  Google Scholar 

  21. Flynn JH, Wall LA. A quick, direct method for the determination of activation energy from thermogravimetric data. J Polym Sci Part C Polym Lett. 1966;4(5):323–8. https://doi.org/10.1002/pol.1966.110040504.

    Article  CAS  Google Scholar 

  22. Ozawa T. A new method of analyzing thermogravimetric data. Bull Chem Soc Jpn. 1965;38(11):1881. https://doi.org/10.1246/bcsj.38.1881.

    Article  CAS  Google Scholar 

  23. Akahira T, Sunose T. Method of determining activation deterioration constant of electrical insulating materials. Res Rep Chiba Inst Technol (Sci Technol). 1971;1971(16):22–31.

    Google Scholar 

  24. Kissinger HE. Reaction kinetics in differential thermal analysis. Anal Chem. 1957;29(11):1702–6. https://doi.org/10.1021/ac60131a045.

    Article  CAS  Google Scholar 

  25. Starink MJ. The determination of activation energy from linear heating rate experiments: a comparison of the accuracy of isoconversion methods. Thermochim Acta. 2003;404(1–2):163–76. https://doi.org/10.1016/s0040-6031(03)00144-8.

    Article  CAS  Google Scholar 

  26. Coats AW, Redfern JP. Kinetic parameters from thermogravimetric data. Nature. 1964;201(491):68–000. https://doi.org/10.1038/201068a0.

    Article  CAS  Google Scholar 

  27. Sanchez-Jimenez PE, Perez-Maqueda LA, Perejon A, Criado JM. Generalized master plots as a straightforward approach for determining the kinetic model: the case of cellulose pyrolysis. Thermochim Acta. 2013;552:54–9. https://doi.org/10.1016/j.tca.2012.11.003.

    Article  CAS  Google Scholar 

  28. Muravyev NV, Luciano G, Ornaghi HL, Svoboda R, Vyazovkin S. Artificial neural networks for pyrolysis, thermal analysis, and thermokinetic studies: the status quo. Molecules. 2021;26(12):3727.

    Article  CAS  Google Scholar 

  29. Bezerra EM, Bento MS, Rocco J, Iha K, Lourenco VL, Pardini LC. Artificial neural network (ANN) prediction of kinetic parameters of (CRFC) composites. Comput Mater Sci. 2008;44(2):656–63. https://doi.org/10.1016/j.commatsci.2008.05.002.

    Article  CAS  Google Scholar 

  30. Chen JC, Liu JY, He Y, Huang LM, Sun SY, Sun J, et al. Investigation of co-combustion characteristics of sewage sludge and coffee grounds mixtures using thermogravimetric analysis coupled to artificial neural networks modeling. Bioresour Technol. 2017;225:234–45. https://doi.org/10.1016/j.biortech.2016.11.069.

    Article  CAS  Google Scholar 

  31. Kuang DY, Xu B. Predicting kinetic triplets using a 1d convolutional neural network. Thermochim Acta. 2018;669:8–15. https://doi.org/10.1016/j.tca.2018.08.024.

    Article  CAS  Google Scholar 

  32. Naqvi SR, Tariq R, Hameed Z, Ali I, Taqvi SA, Naqvi M, et al. Pyrolysis of high-ash sewage sludge: thermo-kinetic study using TGA and artificial neural networks. Fuel. 2018;233:529–38. https://doi.org/10.1016/j.fuel.2018.06.089.

    Article  CAS  Google Scholar 

  33. Buyukada M. Investigation of thermal conversion characteristics and performance evaluation of co-combustion of pine sawdust and lignite coal using TGA, artificial neural network modeling and likelihood method. Bioresour Technol. 2019;287:8. https://doi.org/10.1016/j.biortech.2019.121461.

    Article  CAS  Google Scholar 

  34. Zhang JH, Liu JY, Evrendilek F, Zhang XC, Buyukada M. TG-FTIR and Py-GC/MS analyses of pyrolysis behaviors and products of cattle manure in CO2 and N2 atmospheres: kinetic, thermodynamic, and machine-learning models. Energy Convers Manag. 2019;195:346–59. https://doi.org/10.1016/j.enconman.2019.05.019.

    Article  CAS  Google Scholar 

  35. Bi HB, Wang CX, Lin QZ, Jiang XD, Jiang CL, Bao L. Combustion behavior, kinetics, gas emission characteristics and artificial neural network modeling of coal gangue and biomass via TG-FTIR. Energy. 2020;213:13. https://doi.org/10.1016/j.energy.2020.118790.

    Article  CAS  Google Scholar 

  36. Dubdub I, Al-Yaari M. Pyrolysis of low density polyethylene: kinetic study using TGA data and ANN prediction. Polymers. 2020;12(4):14. https://doi.org/10.3390/polym12040891.

    Article  CAS  Google Scholar 

  37. Bi HB, Wang CX, Jiang XD, Jiang CL, Bao L, Lin QZ. Thermodynamics, kinetics, gas emissions and artificial neural network modeling of co-pyrolysis of sewage sludge and peanut shell. Fuel. 2021;284:12. https://doi.org/10.1016/j.fuel.2020.118988.

    Article  CAS  Google Scholar 

  38. Bi HB, Wang CX, Lin QZ, Jiang XD, Jiang CL, Bao L. Pyrolysis characteristics, artificial neural network modeling and environmental impact of coal gangue and biomass by TG-FTIR. Sci Total Environ. 2021;751:12. https://doi.org/10.1016/j.scitotenv.2020.142293.

    Article  CAS  Google Scholar 

  39. Chen ZY, Chen HS, Wu XY, Zhang JH, Evrendilek DE, Liu JY, et al. Temperature- and heating rate-dependent pyrolysis mechanisms and emissions of Chinese medicine residues and numerical reconstruction and optimization of their non-linear dynamics. Renew Energy. 2021;164:1408–23. https://doi.org/10.1016/j.renene.2020.10.095.

    Article  CAS  Google Scholar 

  40. Karlik B, Olgac AV. Performance analysis of various activation functions in generalized MLP architectures of neural networks. Int J Artif Intell Expert Syst. 2011;1(4):111–22.

    Google Scholar 

  41. Bowden GJ, Dandy GC, Maier HR. Data transformation for neural network models in water resources applications. J Hydroinf. 2003;5(4):245–58. https://doi.org/10.2166/hydro.2003.0021.

    Article  Google Scholar 

  42. Yin BB, Liew KM. Machine learning and materials informatics approaches for evaluating the interfacial properties of fiber-reinforced composites. Compos Struct. 2021;273:12. https://doi.org/10.1016/j.compstruct.2021.114328.

    Article  Google Scholar 

  43. Alaba PA, Popoola SI, Abnisal F, Lee CS, Ohunakin OS, Adetiba E, et al. Thermal decomposition of rice husk: a comprehensive artificial intelligence predictive model. J Therm Anal Calorim. 2020;140(4):1811–23. https://doi.org/10.1007/s10973-019-08915-0.

    Article  CAS  Google Scholar 

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Acknowledgements

This study was supported by “the Fundamental Research Funds for the Central Universities under Grant No. WK2320000050” and the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. 9043135, CityU 11202721).

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Authors and Affiliations

  1. State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei, 230027, China

    Yalong Wang, Ning Kang, Jin Lin & Shouxiang Lu

  2. Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong, China

    Yalong Wang & Kim Meow Liew

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  1. Yalong Wang
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Contributions

All authors contributed to the study conception and design. The first draft of the manuscript was written by Yalong Wang. The experiment was done by Yalong Wang and Ning Kang. Jin Lin, Shouxiang Lu, Kim Meow Liew commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jin Lin or Shouxiang Lu.

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Wang, Y., Kang, N., Lin, J. et al. Cross-heating-rate prediction of thermogravimetry of PVC and XLPE cable insulation material: a novel artificial neural network framework. J Therm Anal Calorim 147, 14467–14478 (2022). https://doi.org/10.1007/s10973-022-11635-7

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