Prediction of high-density polyethylene pyrolysis using kinetic parameters based on thermogravimetric and artificial neural networks

Highlights

● Reducting the sampling frequency can enhance the modelling process.

● The pyrolysis of HDPE was investigated at three different heating rates.

● The average E_a 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.

Abstract

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.

Graphical abstract

Keywords

HDPE / Pyrolysis / Kinetics / Thermogravimetric / ANOVA / Artificial neural network

References

[1] Aboulkas A , El Harfi K , El Bouadili A . (2010). Thermal degradation behaviors of polyethylene and polypropylene. Part I: Pyrolysis kinetics and mechanisms. Energy Conversion and Management, 51( 7): 1363– 1369

[2] Akahira T , Sunose T . (1971). Method of determining activation deterioration constant of electrical insulating materials. Research Report Chiba Institute of Technology, 16 : 22– 31

[3] Al-Salem S M . (2019). Thermal pyrolysis of high density polyethylene (HDPE) in a novel fixed bed reactor system for the production of high value gasoline range hydrocarbons (HC). Process Safety and Environmental Protection, 127 : 171– 179

[4] Al-Salem S M , Antelava A , Constantinou A , Manos G , Dutta A . (2017). A review on thermal and catalytic pyrolysis of plastic solid waste (PSW). Journal of Environmental Management, 197 : 177– 198

[5] Al-Salem S M, Lettieri P ( 2010). Kinetic study of high density polyethylene (HDPE) pyrolysis. Chemical Engineering Research & Design, 88( 12 12A): 1599– 1606

[6] Bong J T , Loy A C M , Chin B L F , Lam M K , Tang D K H , Lim H Y , Chai Y H , Yusup S . (2020). Artificial neural network approach for co-pyrolysis of Chlorella vulgaris and peanut shell binary mixtures using microalgae ash catalyst. Energy, 207 : 118289

[7] Chen G , He S , Cheng Z , Guan Y , Yan B , Ma W , Leung D Y C . (2017). Comparison of kinetic analysis methods in thermal decomposition of cattle manure by themogravimetric analysis. Bioresource Technology, 243 : 69– 77

[8] Coats A W , Redfern J P . (1964). Kinetic parameters from thermogravimetric data. Nature, 201( 4914): 68– 69

[9] Dorofki M, H.Elshafie A, Jaafar O, A.Karim O, Mastura S ( 2012). Comparison of Artificial Neural Network Transfer Functions Abilities to Simulate Extreme Runoff Data. Kuala Lumpur, Malaysia: International Conference on Solid-State and Integrated Circuit (IACSIT), 45– 50

[10] Doyle C D . (1961). Kinetic analysis of thermogravimetric data. Journal of Applied Polymer Science, 5( 15): 285– 292

[11] Flynn J H . (1997). The ‘Temperature Integral’ — Its use and abuse. Thermochimica Acta, 300( 1–2): 83– 92

[12] Flynn J H , Wall L A . (1966a). General treatment of the thermogravimetry of polymers. Journal of Research of the National Bureau of Standards. Section A. Physics and Chemistry, 70A( 6): 487– 523

[13] Flynn J H , Wall L A . (1966b). A quick, direct method for the determination of activation energy from thermogravimetric data. Journal of Polymer Science Part B: Polymer Letters, 4( 5): 323– 328

[14] Friedman H L . (1964). Kinetics of thermal degradation of char-forming plastics from thermogravimetry: application to a phenolic plastic. Journal of Polymer Science Part C: Polymer Symposia, 6( 1): 183– 195

[15] He T , Tong C , Chen L , Zhou Y , Jin B , Zhang B . (2021). Pyrolytic kinetics, products and reaction mechanisms of invasive plant and high-density polyethylene: TG, Py-GC/MS and DFT analysis. Fuel, 303 : 121231

[16] Khedri S , Elyasi S . (2016). Kinetic analysis for thermal cracking of HDPE: a new isoconversional approach. Polymer Degradation & Stability, 129 : 306– 318

[17] Kissinger H E . (1957). Reaction kinetics in differential thermal analysis. Analytical Chemistry, 29( 11): 1702– 1706

[18] Liu C , Liu J , Evrendilek F , Xie W , Kuo J , Buyukada M . (2020). Bioenergy and emission characterizations of catalytic combustion and pyrolysis of litchi peels via TG-FTIR-MS and Py-GC/MS. Renewable Energy, 148 : 1074– 1093

[19] Mazloum S , Aboumsallem Y , Awad S , Allam N , Loubar K . (2021). Modelling pyrolysis process for PP and HDPE inside thermogravimetric analyzer coupled with differential scanning calorimeter. International Journal of Heat and Mass Transfer, 176 : 121468

[20] Murray P , White J . (1949). Kinetics of thermal dehydration of clays. Transactions of the British Ceramic Society, 48 : 187– 200

[21] Naqvi S R, Hameed Z, Tariq R, Taqvi S A, Ali I, Niazi M B K, Noor T, Hussain A, Iqbal N, Shahbaz M ( 2019). Synergistic effect on co-pyrolysis of rice husk and sewage sludge by thermal behavior, kinetics, thermodynamic parameters and artificial neural network. Waste Management (New York, N.Y.), 85: 131– 140

[22] Naqvi S R , Tariq R , Hameed Z , Ali I , Taqvi S A , Naqvi M , Niazi M B K , Noor T , Farooq W . (2018). Pyrolysis of high-ash sewage sludge: Thermo-kinetic study using TGA and artificial neural networks. Fuel, 233 : 529– 538

[23] Ng Q H , Chin B L F , Yusup S , Loy A C M , Chong K Y Y . (2018). Modeling of the co-pyrolysis of rubber residual and HDPE waste using the distributed activation energy model (DAEM). Applied Thermal Engineering, 138 : 336– 345

[24] Rizzarelli P , Rapisarda M , Perna S , Mirabella E F , La Carta S , Puglisi C , Valenti G . (2016). Determination of polyethylene in biodegradable polymer blends and in compostable carrier bags by Py-GC/MS and TGA. Journal of Analytical and Applied Pyrolysis, 117 : 72– 81

[25] Sunphorka S , Chalermsinsuwan B , Piumsomboon P . (2017). Artificial neural network model for the prediction of kinetic parameters of biomass pyrolysis from its constituents. Fuel, 193 : 142– 158

[26] Sharuddin S D A , Abnisa F , Daud W , Aroua M K . (2016). A review on pyrolysis of plastic wastes. Energy Conversion and Management, 115 : 308– 326

[27] Tao L , Ma X , Ye L , Jia J , Wang L , Ma P , Liu J . (2021). Interactions of lignin and LDPE during catalytic co-pyrolysis: thermal behavior and kinetics study by TG-FTIR. Journal of Analytical and Applied Pyrolysis, 158 : 105267

[28] Wang C , Bi H , Lin Q , Jiang X , Jiang C . (2020). Co-pyrolysis of sewage sludge and rice husk by TG–FTIR–MS: pyrolysis behavior, kinetics, and condensable/non-condensable gases characteristics. Renewable Energy, 160 : 1048– 1066

[29] Wong S L , Ngadi N , Abdullah T A T , Inuwa I M . (2015). Current state and future prospects of plastic waste as source of fuel: a review. Renewable and Sustainable Energy Reviews, 50 : 1167– 1180

[30] Yin L J , Chen D Z , Wang H , Ma X B , Zhou G M . (2014). Simulation of an innovative reactor for waste plastics pyrolysis. Chemical Engineering Journal, 237 : 229– 235

[31] Zhang F , Zeng M , Yappert R D , Sun J , Lee Y H , LaPointe A M , Peters B , Abu-Omar M M , Scott S L . (2020). Polyethylene upcycling to long-chain alkylaromatics by tandem hydrogenolysis/aromatization. Science, 370( 6515): 437– 441

[32] Zhou Z , Liu C , Chen X , Ma H , Zhou C , Wang Y , Qi F . (2019). On-line photoionization mass spectrometric study of lignin and lignite co-pyrolysis: insight into the synergetic effect. Journal of Analytical and Applied Pyrolysis, 137 : 285– 292

[33] Zhu H , Liu N A . (2020). Kinetic analysis based on the kinetic compensation effect and optimization calculation. Thermochimica Acta, 690 : 178686

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 52176197, 52100156, and 52100157).

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11783-023-1606-3 and is accessible for authorized users.

Abbreviations

HDPE	High-density polyethylene
ANOVA	Analysis of variance
RMF	Reaction mechanism function
KAS	Kissinger-Akahira-Sunose
DTG	Derivative thermogravimetry
MSE	Mean squared error
TGA	Thermogravimetric analysis
FR	Friedman
TG	Thermogravimetric
Eq	Equation
ANN	Artificial neural network
CR	Coats Redfern
FWO	Flynn-Wall-Ozawa

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