Physics-informed machine learning methods for biomass gasification modeling by considering monotonic relationships

[Display omitted] •A novel PINN modeling framework was developed for biomass gasification.•The PINN seamlessly integrated experimental data and physical monotonicity.•The PINN model showed superior prediction performance (test R2 0.91–0.97).•The monotonicity depicted by the PINN models was pretty consistent with the physics.•The PINN models showed better interpretability and stronger generalization. Machine learning methods have recently shown a broad application prospect in biomass gasification modeling. However, a significant drawback of the machine learning approaches is their poor physical interpretability when relying on limited experimental data. In the present work, a physics-informed neural network method (PINN) is developed to predict biomass gasification products (N2, H2, CO, CO2, and CH4). PINN simultaneously considers regression, structure, and physical monotonicity constraints in the loss function, providing physically feasible predictions. Specifically, the PINN models have outperformed prediction capability (average test R2 0.91–0.97) compared to five other machine learning methods through 50 times random sample classifications. Furthermore, it is demonstrated that the developed models can maintain correct monotonicity even if the feedstock characteristics or gasification conditions are outside the training data. By using a reliable physical mechanism to guide machine learning, the model can ensure better generalizability and scientific interpretability.