With the increase of global plastics production,the problem of waste plastics disposal has become increasingly serious.Pyrolysis technology has attracted widespread attention as a method to convert waste plastics into high value-added products.The pyrolysis process of polyethylene(PE)was investigated by combining molecular-level kinetic model with machine learning methods.First,a large-scale pyrolysis dataset was generated for PE raw materials with different molecular weight distributions using molecular level kinetic model.Then,9 machine learning models were constructed based on the large-scale dataset to evaluate their predictive ability and feature importance,and analyze the key factors affecting the product yields.The results show that reaction time and pyrolysis temperature are the main factors,and the KNN model performs the best in the prediction of gas and liquid phase products.The study also demonstrates that the simulation accuracy and efficiency of the pyrolysis process can be significantly improved by optimizing the machine learning model and expanding the dataset,which provides new ideas and methods for waste plastics resourcing.
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