Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks |
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| Authors: | Xiaoxiao Yin Junyu Tao Guanyi Chen Xilei Yao Pengpeng Luan Zhanjun Cheng Ning Li Zhongyue Zhou Beibei Yan |
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| Affiliation: | 1. School of Mechanical Engineering, Tianjin University of Commerce, Tianjin 300134, China2. School of Environmental Science and Engineering, Tianjin University, Tianjin 300350, China3. Key Laboratory for Power Machinery and Engineering of Ministry of Education, Shanghai Jiao Tong University, Shanghai 200240, China |
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| Abstract: | ● Reducting the sampling frequency can enhance the modelling process. ● The pyrolysis of HDPE was investigated at three different heating rates. ● The average Ea and k0 were calculated by Friedman, KAS, FWO, and CR methods. ● ANN was employed to predict the HDPE weight loss with the optimal MSE and R2. Pyrolysis is considered an attractive option and a promising way to dispose waste plastics. The thermogravimetric experiments of high-density polyethylene (HDPE) were conducted from 105 °C to 900 °C at different heating rates (10 °C/min, 20 °C/min, and 30 °C/min) to investigate their thermal pyrolysis behavior. We investigated four methods including three model-free methods and one model-fitting method to estimate dynamic parameters. Additionally, an artificial neural network model was developed by providing the heating rates and temperatures to predict the weight loss (wt.%) of HDPE, and optimized via assessing mean squared error and determination coefficient on the test set. The optimal MSE (2.6297 × 10−2) and R2 value (R2 > 0.999) were obtained. Activation energy and pre-exponential factor obtained from four different models achieves the acceptable value between experimental and predicted results. The relative error of the model increased from 2.4 % to 6.8 % when the sampling frequency changed from 50 s to 60 s, but showed no significant difference when the sampling frequency was below 50 s. This result provides a promising approach to simplify the further modelling work and to reduce the required data storage space. This study revealed the possibility of simulating the HDPE pyrolysis process via machine learning with no significant accuracy loss of the kinetic parameters. It is hoped that this work could potentially benefit to the development of pyrolysis process modelling of HDPE and the other plastics. |
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| Keywords: | HDPE Pyrolysis Kinetics Thermogravimetric ANOVA Artificial neural network |
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