We explore a way to improve the efficiency of fermentation of lignocellulosic sugars (i.e., glucose and xylose) to bioethanol in a bioreactor. For this purpose, we employ the hybrid cybernetic model developed by Song et al. (Biotechnol and Bioeng, 103: 984-1000, 2009), which provides an accurate description on metabolism of recombinant S. cerevisiae due to its unique feature of accounting for cellular regulation. A comprehensive analysis of the model reveals many interesting features of the process whose balance is critical for increasing the productivity of bioethanol. In particular, the addition of extra xylose to the medium may increase ethanol productivity (a somewhat counterintuitive result as xylose metabolism is slower!), but one that must be orchestrated with control of other important variables. Effects of xylose addition are shown to be different for different reactor environments. In a batch culture, xylose addition substantially improves ethanol productivity at low sugar concentration (e.g., about 45% up by increasing initial xylose concentration from 10 to 30 g/L with glucose concentration of 20 g/L), but worsens it at high sugar concentration (e.g., about 10% drop by increasing xylose concentration from 40 to 160 g/L with glucose concentration of 80 g/L). On the other hand, the productivity of chemostats is constantly improved by increasing the ratio of xylose to glucose level in the feed. It is found that multiple local maxima can exist in chemostats and, consequently, optimal composition for mixed sugars is different depending on the allowable range of xylose addition. Batch operation, however, is found to be superior when mixed sugars are consumed slowly, while continuous operation becomes attractive for rapidly metabolized sugars such as pure glucose. Optimal reactor configurations for given lignocellulosic sugars are shown to depend on calculated operating curves. Reasonably close comparison of model simulations with existing batch fermentation data provides support in part to the value of the current effort. The lesson that emerges is the importance of modeling in improving the efficiency of bioprocesses.