Enhancing biomass Pyrolysis: Predictive insights from process simulation integrated with interpretable Machine learning models

Abstract

Waste biomass pyrolysis is a promising thermochemical conversion process for the production of biofuels and sustainable materials. However, it is challenging to accurately predict the properties and yield of products formed during pyrolysis. Machine learning (ML) is a useful tool for predicting the performance of a process. In the present study, ML algorithms integrated with process simulation were explored to accurately model waste biomass pyrolysis based on properties such as H/C, O/ C, oil yield, gas yield, and char yield. Six different ML models including Random Forest (RF), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Artificial Neural Network (ANN), and Stochastic Gradient Descent (SGD) were used to model pyrolysis process. It was found that the out-of-the-box (without optimization) models for RF, XGBoost, ANN, and GBR performed the best and did not benefit from hyperparameter optimization. The GBR was identified as the most effective among various ML models. It accurately predicted yields of gas, biochar, bio-oil yields, and their H/C and O/C compositions. GBR effectively demonstrated the complex relationships between these variables. The box plot showing the root mean squared logarithmic error (RMSE) revealed that the GBR model had the best overall performance with a value less than 0.03. Also, the partial dependence plot and SHAP feature importance were evaluated to better understand each feature’s effect on the output. Lastly, a shareable graphical user interface (GUI) was created to enable researchers explore and predict pyrolysis yield