Abstract:To construct a digital twin of the gasification process for agricultural and forestry waste and enable real-time analysis and prediction of the gasification reaction rate, a fast-response theoretical model suitable for the analysis and prediction layer of the digital twin system was established based on simple collision theory. This model accounts for factors such as pore structure, reaction conditions, and the catalytic or inhibitory effects of inorganic elements. With input parameters including feedstock characteristics (proximate/ultimate analysis, inorganic element content, etc.) and operating conditions (temperature, atmosphere, etc.), the model can rapidly predict the gasification reaction rate of char in the kinetics-controlled zone. Within the digital twin system, this model serves as the core simulation module, capable of mapping the physical gasification process in real time using offline data. Its applicability across various biomass samples under different conditions has been validated through experimental testing. The results show that, within the temperature range of 725–800 ℃, the model's predictions of gasification reactivity under CO2 and H2O atmospheres closely match the experimental values. The model achieves a prediction accuracy of over 90% and demonstrates generalizability across different types of char samples, indicating its reliability as a simulation module for digital twins. Compared to the CO2 atmosphere, the gasification rate of char is significantly higher in the H2O atmosphere, primarily due to the stronger diffusivity of H2O and the catalytic promotion by active intermediates. The gasification rate of char in the H2O atmosphere is 3.16–76.52 times higher than that in the CO2 atmosphere, depending on the sample type. Under CO2/H2O gasification conditions, the catalytic or inhibitory effects of inorganic elements (Fe, Si, Al) vary significantly, mainly due to the formation of various oxygen-containing compounds induced by the specific atmosphere. These changes alter the occurrence forms of elements and the surface chemical bond structures, thereby affecting gasification reactivity. The quantitative effects of these inorganic elements were characterized through the model. The theoretical mapping model of char gasification reactivity developed in this study enriches the theoretical framework of biomass thermochemical conversion and provides fast-response model support for the development of digital twin technology for agricultural and forestry wastein the thermal conversion of agricultural and forestry waste. The model is applicable to reaction conditions within the kinetics-controlled zone. Under non-ideal conditions, diffusion and combined kinetic-diffusion control mechanisms dominate the thermochemical conversion process. To further refine the model, the effects of diffusion mechanisms need to be incorporated. For the complex system of solid waste thermal conversion, using a highly reliable simplified theoretical model as a foundation—combined with big data, machine learning, and other advanced techniques—may offer a reasonable approach for model construction in future digital energy systems. Close-