Dual fluidized bed gasification (DFBG) is an emerging technology that can be employed as a first step in the transformation of lignocellulosic materials into transportation fuels such as substitute natural gas, dimethyl ether, methanol, and Fischer-Tropsch diesel. The present work aims at (i) identifying challenges that arise in the upscaling of DFBG plants, (ii) determining whether the increased fuel residence time that results from the upscaling is sufficient for process optimization, and (iii) evaluating the impact of measures to mechanically control the fuel residence time. The investigations use a semiempirical 1-dimensional model, which is validated with industrial-scale measurements. The scope includes both DFBG units delivering gas as the main product and those in which the product gas is a byproduct in a heat and power plant. Moreover, both new designs and retrofit cases of existing CFB combustion plants (i.e., adding a gasifier to the return leg) are considered. Modeling results show that although there is an initial increase in the fuel residence time as the size of the gasifier increases, further upscaling eventually leads to a decrease in the degree of char gasification due to (i) a decrease in the fuel residence time, as there is a transition in lateral fuel mixing from the dispersion-dominant regime to the convection-dominant regime; and (ii) a decrease in the char gasification rate due to an increased bed material velocity, which increases the probability that pyrolysis occurs on the bed surface (leading to a less reactive char as the heat transfer is lower there compared to inside the dense bed). For DFBG units of around 100 MW, proper combinations of operational conditions (e.g., the solids circulation, the steam-fuel ratio, and the temperature of the circulating solids) result in an optimized process when heat and power is the main product, with gas as a byproduct. However, when gas is the sole targeted product, it is likely that baffles are also necessary to achieve sufficient fuel conversion for process optimization.