Thermal degradation model of used surgical masks based on machine learning methodology

Chaudhary, Abhishek S; Kiran, Bandaru; Sivagami, K.; Govindarajan, Dhivakar; Chakraborty, Samarshi

doi:10.1016/j.jtice.2023.104732

Cited by 6 publications

(2 citation statements)

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“…Machine learning especially artificial neural network (ANN), which is specialized in solving nonlinear and unsteady problems, has be applied in predicting the thermal degradation behaviors of some materials. [11][12][13] Dubdub et al 14 predicted the pyrolysis of LDPE using three model-free methods, two model fitting methods, and ANN. The results showed that the ANN possessed the highest prediction accuracy among the six methods, and the weight loss rate variations predicted by the ANN were highly consistent with the experimental results.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of entire thermal degradation process of polymethyl methacrylate infiltrated with kerosene by a modified artificial neural network

Wu,

Zhao,

Chen

et al. 2023

Vinyl Additive Technology

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Predicting the entire thermal degradation process of solid combustibles infiltrated with flammable liquids is a challenge at present. In the current study, a novel artificial neural network (ANN) framework containing data preprocessing, data normalization and data transformation is proposed to predict the entire thermal degradation process of polymethyl methacrylate infiltrated with kerosene at three scenarios: (1) fixed kerosene mass fraction with various heating rates, (2) fixed heating rate with various kerosene mass fractions, and (3) various kerosene mass fractions with various heating rates. The entire thermal degradation process of scenario (1) can be accurately predicted using the ANN with 2‐4‐2‐1 topology. Using the data transformation formula exp(x1(1 + x2) to generate a new input variable based on temperature and kerosene mass fraction, a new ANN with 3‐4‐2‐1 topology can accurately predict the entire thermal degradation process of scenario (2). Two new input variables are generated using the data transformation formula 1/(1 + log((1 + x1)(1 + x2))) based on two data sets: (1) kerosene mass fraction and temperature, and (2) heating rate and temperature. The new ANN with 5‐4‐2‐1 topology can accurately predict the entire thermal degradation process at all three scenarios. The new ANN with Levenberg–Marquardt training function and Tanh activation function possesses the best prediction performance.Highlights Data preprocessing can significantly improve the prediction accuracy of ANN. ANN with a suitable hidden layer structure has high prediction accuracy. The new ANN can predict the entire pyrolysis process in various scenarios. ANN with LM and Tanh has the highest prediction accuracy.

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