Artificial Neural Network (ANN) Analysis of Co-pyrolysis of Waste Coconut Husk and Laminated Plastic Packaging

https://doi.org/10.22146/ajche.69521

Joselito Abierta Olalo(1*)

(1) Department of Mechanical Engineering, College of Engineering, Camarines Norte State Col-lege, Daet, Camarines Norte, 4600, Philippines
(*) Corresponding Author

Abstract


Co-pyrolysis of plastic with biomass was used in the possible mitigation of environmental health problems associated with plastic waste. The pyrolysis method possessed the highest solution in the reduction of waste problems. Fuel oil can be produced through the pyrolysis of plastic and biomass waste. Many researchers used pyrolysis technology to produce a suitable amount of pyrolytic oil through different optimization techniques. This study will predict the percentage mass oil yield using an artificial neural network. It uses an input layer, hidden layer and an output layer. Three input factors for the input layer were (i) temperature, (ii) particle size, and (iii) percentage coconut husk. The structure has one hidden layer with two neurons. The artificial neural network was designed to predict the percentage oil yield after 15 pyrolysis runs set by the Box-Behnken design of the experiment. Percentage oil yields after pyrolysis were calculated. Results showed that temperature and percentage of coconut husk significantly influenced the percentage oil yield. Predicted values from simulation in the artificial neural network showed a good agreement through a correlation coefficient of 99.5%. The actual percentage oil yield overlaps the predicted values, which ANN demonstrates as a viable solution.


Keywords


Artificial neural network; Co-pyrolysis; Coconut husk; Laminated plastic

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References

  1. Association of Plastic Manufacturers Europe (APME) (2015). An analysis of European plastics production, demand and waste data. Belgium: European Association of Plastics Recycling and Recovery Organisations, p. 1–32.
  2. Caroko, N., Saptoadi, H., & Rohmat, T.A. (2020). Heating Characteristics of Palm Oil Industry Solid Waste and Plastic Waste Mixture using a Microwave. ASEAN J.Chem.Eng., 20 (2), 174-183.
  3. Co, R.A.S., & Paringit, E.C. (2021). The Regional Assessment on the Solid Waste-to-Energy Potential in the Island of Luzon, Republic of the Philippines. Chemical Engineering Transactions, 83, 457-462.
  4. Costa, P., Pinto, F., Mata, R., Marques, P., Paradela, F., & Costa, L. (2021). Validation of the Application of the Pyrolysis Process for the Treatment and Transformation of Municipal Plastic Wastes. Chemical Engineering Transactions, 86, 859-864.
  5. Galang, M.G.K., & Ballesteros, Jr. F. (2018). Estimation of waste mobile phones in the Philippines using neural networks. Global NEST Journal, Vol 20, No 4, pp 767-772.
  6. Gupta, A.K., Guntuku, S.C., Desu, R.K., & Balu, A. (2015). Optimization of turning parameters by integrating genetic algorithm with support vector regression and artificial neural networks. Int J Adv Manuf Technol 77(1–4):331–9.
  7. Jambeck, J.R., Geyer, R., Wilcox, C., Siegler, T.R., Perryman, M., Andrady, A., ... & Law, K.L. (2015). Plastic waste inputs from land into the ocean. Science, 347(6223), 768-771.
  8. Karsoliya S. (2012). Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. Int J Eng Trends Technol 3(6):713–7.
  9. Kılıc, M.¸ Pütün, E., & Pütün, A.E. (2014). Optimization of Eu phorbia ri gida. fast pyrolysis conditions by using response surface methodology. J. Anal. Appl. Pyrolysis 110: 163-171.
  10. Mia, M., & Dhar, N.R. (2016). Response surface and neural network based predictive models of cutting temperature in hard turning. Journal of Advanced Research, 7(6), 1035-1044.
  11. Olalo, J. (2021). Characterization of Pyrolytic Oil Produced from Waste Plastic in Quezon City, Philippines Using Non-catalytic Pyrolysis Method. Chemical Engineering Transactions, 86, 1495-1500.
  12. Olalo, J. (2022). Pyrolytic Oil Yield from Waste Plastic in Quezon City, Philippines: Optimization Using Response Surface Methodology. International Journal of Renewable Energy Development, 11(1), 325-332.
  13. Saffarzadeh, A., Shimaoka, T., Motomura, Y., & Watanabe, K. (2006). Chemical and mineralogical evaluation of slag products derived from the pyrolysis/melting treatment of MSW. Waste Manage, 26, 1443–1452.
  14. Shihani, N., Kumbhar, B.K., & Kulshreshtha, M. (2006). Modeling of extrusion process using response surface methodology and artificial neural networks. J. Eng. Sci. Technol. 1 31–40.
  15. Sohl, J.E., & Venkatachalam, A.R. (1995). A neural network approach to forecasting model selection. Information and Management, 29(6), 297-303.
  16. UNEP (2016). Marine plastic debris and microplastics – Global lessons and research to inspire action and guide policy change. United Nations Environment Programme, Nairobi.



DOI: https://doi.org/10.22146/ajche.69521

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ASEAN Journal of Chemical Engineering  (print ISSN 1655-4418; online ISSN 2655-5409) is published by Chemical Engineering Department, Faculty of Engineering, Universitas Gadjah Mada.