Thermal degradation kinetics, mechanism, thermodynamics, shape memory properties and artificial neural network application study of polycaprolactone (PCL)/polyvinyl chloride (PVC) blends

Demir, Pınar

doi:10.1007/s00289-022-04522-6

Cited by 3 publications

(1 citation statement)

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“…Demir (2022) [ 8 ] used different compositions of polycaprolactone (PCL)/polyvinyl chloride (PVC) blends in order to study the thermal degradation kinetics with a four-heating rate using ANN analysis. Further, the investigated results of the decomposition temperature, heating rate, and percentage of PVC in blends as input data for the ANN model, and the percentage of weight remaining during degradation as output data for the ANN model were reported by [ 8 ]. He concluded the 3-10-10-1 network with Logsig–Tansig transfer function with feed-forward backpropagation was the best network for this system.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Study on the Pyrolysis of Polypropylene with a Sensitivity Analysis

Dubdub

2023

Polymers

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Among machine learning (ML) studies, artificial neural network (ANN) analysis is the most widely used technique in pyrolysis research. In this work, the pyrolysis of polypropylene (PP) polymers was established using a thermogravimetric analyzer (TGA) with five sets of heating rates (5–40 K min−1). TGA data was used to exploit an ANN network by achieving a feed-forward backpropagation optimization technique in order to predict the weight-left percentage. Two important ANN model input variables were identified as the heating rate (K min−1) and temperature (K). For the range of TGA values, a 2-10-10-1 network with two hidden layers (Logsig-Tansig) was concluded to be the best structure for predicting the weight-left percentage. The ANN demonstrated a good agreement between the experimental and calculated values, with a high correlation coefficient (R) of greater than 0.9999. The final network was then simulated with the new input data set for effective performance. In addition, a sensitivity analysis was performed to identify the uncertainties associated with the relationship between the output and input parameters. Temperature was found to be a more sensitive input parameter than the heating rate on the weight-left percentage calculation.

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Section: Introductionmentioning

confidence: 99%