Artificial neural networks for predicting the generation of acetaldehyde in pet resin in the process of injection of plastic packages

Mauro Reis Nascimento; David Barbosa de  Alencar; Manoel Henrique Reis Nascimento; Carlos Alberto Monteiro

doi:10.31686/ijier.vol9.iss6.3150

Authors

Mauro Reis Nascimento

Institute of Technology and Education Galileo of the Amazon
David Barbosa de Alencar

Institute of Technology and Education Galileo of the Amazon
Manoel Henrique Reis Nascimento

Institute of Technology and Education Galileo of the Amazon – ITEGAM
Carlos Alberto Monteiro

Institute Center for Research and Development in Software Technology (ICTS)

DOI:

https://doi.org/10.31686/ijier.vol9.iss6.3150

Keywords:

Drying Temperature, Artificial Neural Networks, Computational Intelligence, PET, Acetaldehyde

Abstract

The industrial production of preforms for the manufacture of PET bottles, during the plastic injection process, is essential to regulate the drying temperature of the PET resin, to control the generation of Acetaldehyde (ACH), which alters the flavor of carbonated or non-carbonated drinks, giving the drink a citrus flavor and putting in doubt the quality of packaged products. In this work, an Artificial Neural Network (ANN) of the Backpropagation type (Cascadeforwardnet) is specified to support the decision-making process in controlling the ideal drying temperature of the PET resin, allowing specialists to make the necessary temperature regulation decisions for the best performance by decreasing ACH levels. The materials and methods were applied according to the manufacturer's characteristics on the moisture in the PET resin grain, which may contain between 50 ppm and 100 ppm of ACH. Data were collected for the method analysis, according to temperatures and residence times used in the blow injection process in the manufacture of the bottle preform, the generation of ACH from the PET bottle after solid post-condensation stage reached residual ACH levels below (3-4) ppm, according to the desired specification, reaching levels below 1 ppm. The results found through the Computational Intelligence (IC) techniques applied by the ANNs, where they allowed the prediction of the ACH levels generated in the plastic injection process of the bottle packaging preform, allowing an effective management of the parameters of production, assisting in strategic decision making regarding the use of temperature control during the drying process of PET resin.

Author Biographies

Mauro Reis Nascimento, Institute of Technology and Education Galileo of the Amazon

Academic, of the Postgraduate Program in Engineering, Process Management, Systems and Environmental (PPEMSE) of the ITEGAM
David Barbosa de Alencar, Institute of Technology and Education Galileo of the Amazon

Research Department
Manoel Henrique Reis Nascimento, Institute of Technology and Education Galileo of the Amazon – ITEGAM

Research Department
Carlos Alberto Monteiro, Institute Center for Research and Development in Software Technology (ICTS)

Research Department

References

R. Nisticò, "Polyethylene terephthalate (PET) in the packaging industry," Polymer Testing, vol. 90, p. 106707, 2020/10/01/ 2020. DOI: https://doi.org/10.1016/j.polymertesting.2020.106707

S. Pira, "Sustainability and lightweighting are key areas in the developing PET packaging market," https://www.smitherspira.com/resources/2017/april/key-areas-in-pet-packaging, 2017.

E. M. Akshaya, R. Palaniappan, C. F. Sowmya, N. Rasana, and K. Jayanarayanan, "Properties of Blends from Polypropylene and Recycled Polyethylene Terephthalate using a Compatibilizer," Materials Today: Proceedings, vol. 24, pp. 359-368, 2020/01/01/ 2020. DOI: https://doi.org/10.1016/j.matpr.2020.04.287

K. E. Özlem, "Acetaldehyde migration from polyethylene terephthalate bottles into carbonated beverages in Türkiye," International Journal of Food Science & Technology, vol. 43, pp. 333-338, 2008. DOI: https://doi.org/10.1111/j.1365-2621.2006.01443.x

G. Galo Silva, M. L. d. C. Valente, L. Bachmann, and A. C. dos Reis, "Use of polyethylene terephthalate as a prosthetic component in the prosthesis on an overdenture implant," Materials Science and Engineering: C, vol. 99, pp. 1341-1349, 2019/06/01/ 2019. DOI: https://doi.org/10.1016/j.msec.2019.01.136

G. Carrieri, M. V. De Bonis, and G. Ruocco, "Modeling and experimental validation of mass transfer from carbonated beverages in polyethylene terephthalate bottles," Journal of Food Engineering, vol. 108, pp. 570-578, 2012/02/01/ 2012. DOI: https://doi.org/10.1016/j.jfoodeng.2011.09.001

Y.-C. Jang, G. Lee, Y. Kwon, J.-h. Lim, and J.-h. Jeong, "Recycling and management practices of plastic packaging waste towards a circular economy in South Korea," Resources, Conservation and Recycling, vol. 158, p. 104798, 2020/07/01/ 2020. DOI: https://doi.org/10.1016/j.resconrec.2020.104798

A. Lontos and A. Gregoriou, "The effect of the deformation rate on the wall thickness of 1.5LT PET bottle during ISBM (Injection Stretch Blow Molding) process," Procedia CIRP, vol. 81, pp. 1307-1312, 2019/01/01/ 2019. DOI: https://doi.org/10.1016/j.procir.2019.04.018

M. G. Manual Técnico Resina PET, "M&G. Manual Técnico Resina PET," M&G Polímeros Brasil S.A., 2009.

M. T. Koschevic and P. R. S. Bittencourt, "Meio ambiente e materiais poliméricos: Breves considerações com ênfase ao Politereftalato de Etileno (PET) e processos de degradação," Revista Eletrõnica Científica Inovação e Tecnologia, vol. 2, p. 21, 2016.

C. A. R. ANJOS, "INFLUENCE OF THE PROCESS IN THE GENERATION OF ACETALDEHYDE AND RESIDUAL LEVELS IN POLY (ETHYLENE TEREPHTHALATE) (PET) PACKAGING AND DRINKS," Revista Brasileira de engenharia de Biossistemas, vol. 3, pp. 277 - 290, 2007. DOI: https://doi.org/10.18011/bioeng2007v1n3p277-290

W. Romão, M. A. Spinacé, and M.-A. D. Paoli, "Poly (ethylene terephthalate), PET: a review on the synthesis processes, degradation mechanisms and its recycling," Polímeros, vol. 19, pp. 121-132, 2009. DOI: https://doi.org/10.1590/S0104-14282009000200009

C. Bach, X. Dauchy, M.-C. Chagnon, and S. Etienne, "Chemical compounds and toxicological assessments of drinking water stored in polyethylene terephthalate (PET) bottles: A source of controversy reviewed," Water Research, vol. 46, pp. 571-583, 2012/03/01/ 2012. DOI: https://doi.org/10.1016/j.watres.2011.11.062

M. L. Chaves, J. J. Márquez, H. Pérez, L. Sánchez, and A. Vizan, "Intelligent Decision System Based on Fuzzy Logic Expert System to Improve Plastic Injection Molding Process," in International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding, H. Pérez García, J. Alfonso-Cendón, L. Sánchez González, H. Quintián, and E. Corchado, Eds., ed Cham: Springer International Publishing, 2018, pp. 57-67. DOI: https://doi.org/10.1007/978-3-319-67180-2_6

R. D. Labati, A. Genovese, E. Munoz, V. Piuri, F. Scotti, and G. Sforza, "Computational Intelligence for Industrial and Environmental Applications," IEEE 8th International Conference on Intelligent Systems, 2016.

F. L. Peixoto, C. H. Ahrens, and G. V. Salmoria, "APLICAÇÃO DO PROCESSO DE MOLDAGEM POR INJEÇÃO SOBRE INSERTOS DE TERMOPLÁSTICO (SOBREINJEÇÃO) EM MOLDES DE FABRICAÇÃO RÁPIDA," 2011.

L. C. Paganin and G. F. Barbosa, "A comparative experimental study of additive manufacturing feasibility faced to injection molding process for polymeric parts," The International Journal of Advanced Manufacturing Technology, vol. 109, pp. 2663-2677, 2020/08/01 2020. DOI: https://doi.org/10.1007/s00170-020-05849-y

Y. Xu, Q. Zhang, W. Zhang, and P. Zhang, "Optimization of injection molding process parameters to improve the mechanical performance of polymer product against impact," The International Journal of Advanced Manufacturing Technology, vol. 76, pp. 2199-2208, 2015/02/01 2015. DOI: https://doi.org/10.1007/s00170-014-6434-y

Y. C. L. X. Chena, D.Q. Lib, "Analysis of thermal residual stress in plastic injection molding," Journal of Materials Processing Technology, 2000.

C.-H. Sun, J.-H. Chen, and L.-J. Sheu, "Quality control of the injection molding process using an EWMA predictor and minimum–variance controller," The International Journal of Advanced Manufacturing Technology, vol. 48, pp. 63-70, 2010/04/01 2010. DOI: https://doi.org/10.1007/s00170-009-2278-2

A. L. M. NASSER, L. M. X. LOPES, and M. MONTEIRO, "Oligômeros em embalagem de PET para água mineral e suco de fruta. uma revisão," Alimentos e Nutrição Araraquara, vol. 16, pp. 183-194, 2009.

D. V. D. V. Rosato, Matthew V. , "Plastic Product Material and Process Selection Handbook," Ed. Elsieveier Science & Tecnology Books, 2004. DOI: https://doi.org/10.1016/B978-185617431-2/50005-0

S. A. Elsheikhi and K. Y. Benyounis, "Review of Recent Developments in Injection Molding Process for Polymeric Materials," in Reference Module in Materials Science and Materials Engineering, ed: Elsevier, 2016. DOI: https://doi.org/10.1016/B978-0-12-803581-8.04022-4

S. L. Belcher, "13 - Blow Molding," in Applied Plastics Engineering Handbook (Second Edition), M. Kutz, Ed., ed: William Andrew Publishing, 2017, pp. 265-289. DOI: https://doi.org/10.1016/B978-0-323-39040-8.00013-4

M. Han, "5 - Depolymerization of PET Bottle via Methanolysis and Hydrolysis," in Recycling of Polyethylene Terephthalate Bottles, S. Thomas, A. Rane, K. Kanny, A. V.K, and M. G. Thomas, Eds., ed: William Andrew Publishing, 2019, pp. 85-108. DOI: https://doi.org/10.1016/B978-0-12-811361-5.00005-5

N. Serinçay and M. F. Fellah, "Acetaldehyde adsorption and detection: A density functional theory study on Al-doped graphene," Vacuum, vol. 175, p. 109279, 2020/05/01/ 2020. DOI: https://doi.org/10.1016/j.vacuum.2020.109279

T. K. a. J. J. Ben Nijssen, "Acetaldehyde in Mineral Water Stored in Polyethylene Terephthalate (PET) Bottles: Odour Threshold and Quantification," PACKAGING TECHNOLOGY AND SCIENCE, 1996.

R. N. Serio and L. J. Gudas, "Modification of stem cell states by alcohol and acetaldehyde," Chemico-Biological Interactions, vol. 316, p. 108919, 2020/01/25/ 2020. DOI: https://doi.org/10.1016/j.cbi.2019.108919

C. Bach, X. Dauchy, I. Severin, J.-F. Munoz, S. Etienne, and M.-C. Chagnon, "Effect of temperature on the release of intentionally and non-intentionally added substances from polyethylene terephthalate (PET) bottles into water: Chemical analysis and potential toxicity," Food Chemistry, vol. 139, pp. 672-680, 2013/08/15/ 2013. DOI: https://doi.org/10.1016/j.foodchem.2013.01.046

J. Ewender and W. Frank, "Determination of the Migration of Acetaldehyde from PET Bottles into Noncarbonated and Carbonated Mineral Water," Fraunhofer Institute for Process Engineering and Packaging (IVV), Giggenhauser Straße 35, 85354 Freising, Germany,, 2008.

F. Haddadi, S. Khanchi, M. Shetabi, and V. Derhami, "Intrusion Detection and Attack Classification Using Feed-Forward Neural Network," in 2010 Second International Conference on Computer and Network Technology, 2010, pp. 262-266. DOI: https://doi.org/10.1109/ICCNT.2010.28

A. Yadav, K. Yadav, and S. Anirbid, "Feedforward Neural Network for Joint Inversion of Geophysical data to Identify Geothermal Sweet Spots in Gandhar, Gujarat, India," Energy Geoscience, 2021/01/20/ 2021. DOI: https://doi.org/10.1016/j.engeos.2021.01.001

H. Abdi, "A neural network primer," Journal of Biological Systems, vol. 2, pp. 247-281, 1994. DOI: https://doi.org/10.1142/S0218339094000179

F. d. A. Florencio, E. D. Moreno, H. T. Macedo, R. J. P. d. B. Salgueiro, F. B. d. Nascimento, and F. A. O. Santos, "Intrusion Detection via MLP Neural Network Using an Arduino Embedded System," in 2018 VIII Brazilian Symposium on Computing Systems Engineering (SBESC), 2018, pp. 190-195.

B. Saha Tchinda, D. Tchiotsop, M. Noubom, V. Louis-Dorr, and D. Wolf, "Retinal blood vessels segmentation using classical edge detection filters and the neural network," Informatics in Medicine Unlocked, vol. 23, p. 100521, 2021/01/01/ 2021. DOI: https://doi.org/10.1016/j.imu.2021.100521

J. Ramesh, P. T. Vanathi, and K. Gunavathi, "Fault Classification in Phase‐Locked Loops Using Back Propagation Neural Networks," ETRI journal, vol. 30, pp. 546-554, 2008. DOI: https://doi.org/10.4218/etrij.08.0108.0133

B. J. Wythoff, "Backpropagation neural networks: a tutorial," Chemometrics and Intelligent Laboratory Systems, vol. 18, pp. 115-155, 1993. DOI: https://doi.org/10.1016/0169-7439(93)80052-J

P. M. n. J. Chan and M. Mehralizadeh, "Forecasting East Asian Indices Futures via a Novel Hybrid of Wavelet-PCA Denoising and Artificial Neural Network Models," PLoS ONE, vol. 11, p. e0156338, 2016. DOI: https://doi.org/10.1371/journal.pone.0156338

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Artificial neural networks for predicting the generation of acetaldehyde in pet resin in the process of injection of plastic packages

Authors

Mauro Reis Nascimento

David Barbosa de Alencar

Manoel Henrique Reis Nascimento

Carlos Alberto Monteiro

DOI:

Keywords:

Abstract

Author Biographies

Mauro Reis Nascimento, Institute of Technology and Education Galileo of the Amazon

David Barbosa de Alencar, Institute of Technology and Education Galileo of the Amazon

Manoel Henrique Reis Nascimento, Institute of Technology and Education Galileo of the Amazon – ITEGAM

Carlos Alberto Monteiro, Institute Center for Research and Development in Software Technology (ICTS)

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