The yield prediction of synthetic fuel production from pyrolysis of plasticwaste by Levenberg-Marquardt approach in feedforward neural networks model

Faisal Abnisa, Shafferina Dayana Anuar Sharuddin, Mohd Fauzi bin Zanil, Wan Mohd Ashri Wan Daud, Teuku Meurah Indra Mahlia

Research output: Contribution to journalArticle


The conversion of plastic waste into fuel by pyrolysis has been recognized as a potential strategy for commercialization. The amount of plastic waste is basically different for each country which normally refers to non-recycled plastics data; consequently, the production target will also be different. This study attempted to build a model to predict fuel production from different non-recycled plastics data. The predictive model was developed via Levenberg-Marquardt approach in feed-forward neural networks model. The optimal number of hidden neurons was selected based on the lowest total of the mean square error. The proposed model was evaluated using the statistical analysis and graphical presentation for its accuracy and reliability. The results showed that the model was capable to predict product yields from pyrolysis of non-recycled plastics with high accuracy and the output values were strongly correlated with the values in literature.

Original languageEnglish
Article number1853
Issue number11
Publication statusPublished - 01 Nov 2019


All Science Journal Classification (ASJC) codes

  • Chemistry(all)
  • Polymers and Plastics

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