Comparative analysis of coconut husk and laminated plastic packaging co-pyrolysis: Response surface methodology and artificial neural network approach
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Full Text |
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Author |
Joselito A. Olalo and Aser N. Dino
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e-ISSN |
1819-6608 |
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On Pages
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1296-1303
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Volume No. |
19
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Issue No. |
21
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Issue Date |
December 27, 2024
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DOI |
https://doi.org/10.59018/112461
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Keywords |
coconut husk, LPP, RSM, ANN, co-pyrolysis.
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Abstract
The co-pyrolysis of plastic with biomass offers a promising solution to mitigate environmental health concerns associated with plastic waste. This technique has shown effectiveness in addressing waste issues by generating fuel oil from plastic and biomass waste. Many researchers have utilized pyrolysis technology, employing various optimization techniques to produce significant amounts of pyrolytic oil. This study aims to compare predictions of the percentage mass oil yield using an artificial neural network (ANN) and response surface methodology (RSM). Before the pyrolysis process, a 3k factorial Box-Behnken Design was employed to determine the number of experiments required. In RSM, a 2D contour plot illustrated the correlation of each parameter with the percentage oil yield. ANOVA analysis revealed the significance of the produced quadratic mathematical model, with a p-value below 0.05. Through the ANN modeling, the temperature, particle size, and the percentage of LPP were employed as the input, while two neurons were employed in 1 hidden layer. The resulting percentage oil yields were calculated, indicating a significant influence from temperature and the percentage of laminated plastic packaging. Simulation results from the ANN demonstrated strong agreement with a correlation coefficient of 99.5%, surpassing the 90.71% correlation coefficient observed in RSM. This underscores the advantage of using ANN for predictive modeling.
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