Dynamic modeling of dual fluidized bed steam gasification for control design

Dual fluidized bed steam gasification allows the production of high-value product gas from woody biomass or biogenic residuals. Advanced control concepts such as model predictive control are promising approaches to improve the process performance and efficiency. These control techniques require dynamic models of the process that can predict the plant's behavior as a function of the manipulated variables. This paper presents a gray-box modeling approach based on mass and energy balances to obtain a mathematical description of the temperatures inside the two reactors and the total mass flows leaving the reactors. The model incorporates data-driven components where first-principle modeling is hardly possible with reasonable effort. An artificial neural network is utilized to model the bed material circulation between the two reactors. Experiments were carried out at a 100 kW pilot plant to generate measurement data both for system identification and model validation. Simulations verify that the model achieves reliable predictions for the dual fluidized bed gasification process.

Automated summary

Dual fluidized bed steam gasification enables the production of valuable gas from woody biomass or biogenic residuals. Advanced control concepts, like model predictive control, are promising for improving process performance and efficiency. This paper presents a gray-box modeling approach, incorporating data-driven components, to obtain a mathematical description of temperatures and mass flows in the reactors. Artificial neural networks are used to model bed material circulation. Experimental data from a pilot plant validate the model's predictions for the dual fluidized bed gasification process.