Exposing and understanding synergistic effects in co-pyrolysis of biomass and plastic waste via machine learning

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 R2 for BOY while greater than 0.85 R2 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.

Automated summary

This study developed interpretable models using XGBoost machine learning and Shapley additive explanation to predict bio-oil yields and synergistic effects during co-pyrolysis of biomass and plastic waste. The XGBoost models achieved a prediction accuracy of nearly 0.90 R2 for bio-oil yields and greater than 0.85 R2 for synergistic effects. The findings revealed that reaction temperature and biomass-to-plastic ratio were the top two important features, but the overall contributions of feedstock characteristics exceeded 60% in the co-pyrolysis system. This provides insights into co-pyrolysis and potential pathways for improvement.