In recent years, predicting biomass gasification syngas components using machine learning (ML) methods has received significant attention and interest. However, insufficient experimental data can hardly guide ML models in capturing complete knowledge of the gasification process, which can be reflected explicitly in the mismatch between ML model predictions and physical knowledge. Therefore, this study proposes a novel hard-constrained physics-informed neural network (HC-PINN) to predict biomass gasification syngas components by integrating experimental data with prior monotonicity relationships. Firstly, the monotonicity constraints between syngas components and key operating parameters are derived as inequality constraints involving solely the network parameters. Since the inequality constraints bypass the presence of model input parameters, the HC-PINN model can ensure that the model predictions in the entire input space satisfy the monotonicity relationships while avoiding the hard-to-trade-off synthetic samples. Secondly, the constrained particle swarm optimization algorithm is used to optimize the network parameters between regression losses and inequality constraints. The additional physical loss terms introduced in the soft constraints are substituted by searching for the feasible regions that satisfy the inequality constraints, avoiding the complexity associated with weight trade-offs across multiple loss terms. Finally, the results show that the HC-PINN model can strictly adhere to the monotonicity relationships even in the no-training-sample region, reflecting better interpretability and generalization than the three state-of-the-art traditional ML methods.