The pyrolysis products of different biomass are difficult to predict due to the complex
biomass properties and wide range of operating conditions. In this study, machine learning
techniques based on artificial neural networks, gradient boosting, decision trees, random
forest, K-nearest-neighbors, bagging regressor, and lasso regression were employed to
develop different predictive models for char, liquid/bio-oil, and gas product yields estimation.
The performance of these models was evaluated by R 2 score. All models performed well (R …