Model predictive control of a dual fluidized bed gasification plant

Dual fluidized bed (DFB) gasification is a promising method for producing valuable gaseous energy carriers from biogenic feedstocks as a substitute for fossil fuels. State-of-the-art DFB gasification plants mainly rely on manual operation or single-input single-output control loops, and scientific contributions only exist for controlling individual process variables. This leaves a research gap in terms of comprehensive control strategies for DFB gasification. To address this gap, we propose a multivariate control strategy that focuses on crucial process variables, such as product gas quantity, gasification temperature, and bed material circulation rate. Our approach utilizes model predictive control (MPC), which enables effective process control while explicitly considering process constraints. A simulation study is given demonstrating how different MPC parametrizations influence the behavior of the closed-loop system. Experimental results from a 100kW pilot plant at TU Wien demonstrate the successful control achieved by the proposed control algorithm.