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Application of Adaptive Neuro-Fuzzy Inference System in Flammability Parameter Prediction
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Material Science.
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
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2020 (English)In: Polymers, ISSN 2073-4360, E-ISSN 2073-4360, Vol. 12, no 1, article id 122Article in journal (Refereed) Published
Abstract [en]

The fire behavior of materials is usually modeled on the basis of fire physics and material composition. However, significant strides have been made recently in applying soft computing methods such as artificial intelligence in flammability studies. In this paper, multiple linear regression (MLR) was employed to test the degree of non-linearities in flammability parameter modeling by assessing the linear relationship between sample mass, heating rate, heat release capacity (HRC) and total heat release (THR). Adaptive neuro-fuzzy inference system (ANFIS) was then adopted to predict the HRC and THR of the extruded polystyrene measured from microscale combustion calorimetry experiments. The ANFIS models presented excellent predictions, showing very low mean training and testing errors as well as reasonable agreements between experimental and predicted datasets. Hence, it can be inferred that ANFIS can handle the non-linearities in flammability modeling, making it apt as a modeling technique for accurate and effective flammability assessments.

Place, publisher, year, edition, pages
MDPI, 2020. Vol. 12, no 1, article id 122
Keywords [en]
flammability, heat release rate, microscale combustion calorimetry, multiple linear regression, adaptive neuro-fuzzy inference system
National Category
Bio Materials
Research subject
Wood and Bionanocomposites
Identifiers
URN: urn:nbn:se:ltu:diva-78461DOI: 10.3390/polym12010122ISI: 000519848300122PubMedID: 31948059Scopus ID: 2-s2.0-85078480829OAI: oai:DiVA.org:ltu-78461DiVA, id: diva2:1423114
Note

Validerad;2020;Nivå 2;2020-04-14 (alebob)

Available from: 2020-04-14 Created: 2020-04-14 Last updated: 2020-04-20Bibliographically approved

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Das, Oisik

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