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

Yalong Wang; Ning Kang; Jin Lin; Shouxiang Lu; Kim Meow Liew

doi:10.1007/s10973-022-11635-7

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

Yalong Wang, Ning Kang, Jin Lin^*, Shouxiang Lu^*, Kim Meow Liew

^*Corresponding author for this work

Department of Architecture and Civil Engineering

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

8 Citations (Scopus)

97 Downloads (CityUHK Scholars)

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.

Original language	English
Pages (from-to)	14467–14478
Number of pages	12
Journal	Journal of Thermal Analysis and Calorimetry
Volume	147
Issue number	24
Online published	5 Oct 2022
DOIs	https://doi.org/10.1007/s10973-022-11635-7
Publication status	Published - Dec 2022

Funding

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).

Research Keywords

PVC
XLPE
Thermogravimetry
Artificial neural network
Cross-heating-rate
THERMAL-DEGRADATION
KINETIC-PARAMETERS
ELECTRICAL CABLES
ACTIVATION-ENERGY
SEWAGE-SLUDGE
TG-FTIR
PYROLYSIS
SHEATH
DECOMPOSITION

Publisher's Copyright Statement

COPYRIGHT TERMS OF DEPOSITED POSTPRINT FILE: This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10973-022-11635-7.

RGC Funding Information

RGC-funded

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10.1007/s10973-022-11635-7

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Cite this

@article{05dc70b10dd544e6bde11182cf6f0ed3,

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

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.",

keywords = "PVC, XLPE, Thermogravimetry, Artificial neural network, Cross-heating-rate, THERMAL-DEGRADATION, KINETIC-PARAMETERS, ELECTRICAL CABLES, ACTIVATION-ENERGY, SEWAGE-SLUDGE, TG-FTIR, PYROLYSIS, SHEATH, DECOMPOSITION",

author = "Yalong Wang and Ning Kang and Jin Lin and Shouxiang Lu and Liew, {Kim Meow}",

year = "2022",

month = dec,

doi = "10.1007/s10973-022-11635-7",

language = "English",

volume = "147",

pages = "14467–14478",

journal = "Journal of Thermal Analysis and Calorimetry",

issn = "1388-6150",

publisher = "Springer Science and Business Media B.V.",

number = "24",

}

Cross-heating-rate prediction of thermogravimetry of PVC and XLPE cable insulation material: a novel artificial neural network framework. / Wang, Yalong; Kang, Ning; Lin, Jin et al.
In: Journal of Thermal Analysis and Calorimetry, Vol. 147, No. 24, 12.2022, p. 14467–14478.

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

TY - JOUR

T1 - Cross-heating-rate prediction of thermogravimetry of PVC and XLPE cable insulation material

T2 - a novel artificial neural network framework

AU - Wang, Yalong

AU - Kang, Ning

AU - Lin, Jin

AU - Lu, Shouxiang

AU - Liew, Kim Meow

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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.

KW - PVC

KW - XLPE

KW - Thermogravimetry

KW - Artificial neural network

KW - Cross-heating-rate

KW - THERMAL-DEGRADATION

KW - KINETIC-PARAMETERS

KW - ELECTRICAL CABLES

KW - ACTIVATION-ENERGY

KW - SEWAGE-SLUDGE

KW - TG-FTIR

KW - PYROLYSIS

KW - SHEATH

KW - DECOMPOSITION

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DO - 10.1007/s10973-022-11635-7

M3 - RGC 21 - Publication in refereed journal

SN - 1388-6150

VL - 147

SP - 14467

EP - 14478

JO - Journal of Thermal Analysis and Calorimetry

JF - Journal of Thermal Analysis and Calorimetry

IS - 24

ER -

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

Abstract

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GRF: Fire Resistance and Mechanical Performance of Laminated Glass Facades Subject to A Down-Flowing Water Film

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

Abstract

Funding

Research Keywords

Publisher's Copyright Statement

RGC Funding Information

Access Output

Other Access Options

Permanent Link

Other Files and Links

Fingerprint

Projects

GRF: Fire Resistance and Mechanical Performance of Laminated Glass Facades Subject to A Down-Flowing Water Film

Cite this