Exposing and understanding synergistic effects in co-pyrolysis of biomass and plastic waste via machine learning. (February 2023) - Prasertpong, Prapaporn; Onsree, Thossaporn; Khuenkaeo, Nattawut; Tippayawong, Nakorn; Lauterbach, Jochen
Graphical abstract: Highlights: Machine learning prediction of oil yields and synergistic effects of input variables from co-pyrolysis of biomass and plastic waste was conducted. XGBoost gave prediction accuracy with R 2 of 0.86–0.90 using 26 input features. With Shapley additive explanation, temperature and biomass-to-plastic ratio identified as the top two important features. Abstract: During co-pyrolysis of biomass with plastic waste, bio-oil yields (BOY) could be either induced or reduced significantly via synergistic effects (SE). However, investigating/ interpreting the SE and BOY in multidimensional domains is complicated and limited. This work applied XGBoost machine-learning and Shapley additive explanation (SHAP) to develop interpretable/ explainable models for predicting BOY and SE from co-pyrolysis of biomass and plastic waste using 26 input features. Imbalanced training datasets were improved by synthetic minority over-sampling technique. The prediction accuracy of XGBoost models was nearly 0.90 R 2 for BOY while greater than 0.85 R 2 for SE. By SHAP, individual impact and interaction of input features on the XGBoost models can be achieved. Although reaction temperature and biomass-to-plastic ratio were the top two important features, overall contributions of feedstock characteristics were more than 60 % in the system of co-pyrolysis. The finding provides a better understanding of co-pyrolysis and a way of further improvements.