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Machine Learning-driven Prediction and Optimization of Pyrolysis Oil and Limonene Production from Waste Tires by Jingwei Qi; Pengcheng Xu; Ming Hu; Taoli Huhe; Xiang Ling; Haoran Yuan; Yijie Wang; Yong Chen is a Engineering article available to read on EtoBox.

What is Machine Learning-driven Prediction and Optimization of Pyrolysis Oil and Limonene Production from Waste Tires about?

The pyrolysis oil from waste tires possesses significant economic value and is a crucial factor in determining the industrial viability of tire pyrolysis processes. Limonene in pyrolysis oil is a significant component with extremely high industrial application value. In the process of tire pyrolysis, predicting the pyrolysis products through operating conditions and feedstock composition can effectively control industrial operations and enhance operational efficiency. However, there is currently a lack of robust prediction methods for pyrolysis oil and limonene yield. This study proposes the application of machine learning to predict the yield of tire pyrolysis oil and limonene. Artificial Neural Network (ANN) and Random Forest (RF) models were developed to create prediction models. In the statistical analysis, RF achieved optimal R 2 median values of 0.83 and 0.64 for pyrolysis oil and limonene predictions during the testing stage.For the prediction of limonene yield, the best R 2 and RMSE values in the testing and training stages were 0.844, 3.76, and 0.964, 1.91, respectively. For the prediction of pyrolysis oil yield, the corresponding values were 0.926, 4.1, and 0.985, 1.889,

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Author

Jingwei Qi; Pengcheng Xu; Ming Hu; Taoli Huhe; Xiang Ling; Haoran Yuan; Yijie Wang; Yong Chen

Publisher

Elsevier BV

Published

2024

Language

EN

Field

Engineering (Physical Sciences)