In the preceding section, the possibility of improving the productivity in batch reactors was examined by increasing the initial concentration of xylose. Elevation of xylose concentration has both positive and negative effects, i. e., it facilitates simultaneous consumption initially, but prolongs the fermentation time after glucose consumption. Overall, this trade-off resulted in the increase of ethanol productivity only at low sugar concentrations. It was further shown that continuous operation produces significantly more ethanol than batch when only glucose is consumed, but less when mixed sugars are consumed. These findings suggest the investigation of new reactor configurations which may outperform conventional batch fermentation.

We consider the following five configurations (denoted by C1 to C5), each of which combines two different reactor operations (O1 and O2) (Table 4.1). C1 represents a conventional batch operation where mixed sugars are fermented to ethanol by recombinant S. cerevisiae. The same is repeated at every batch (i. e., O1 is identical to O2). In C2, O1 is a batch reactor for the growth of the "wild-type" S. cerevisiae which can ferment glucose alone. Leftover sugars in O1 are then fed to O2 (i. e., fed-batch operation) where mixed sugars are fermented using the recombinant strain. C3 is the same as C2 except that a chemostat is used

for O1. C4 and C5 are respective counterparts of C2 and C3, and these two groups are differentiated only by the xylose feeding policy in O2. That is, in C2 and C3, all leftover sugars in O1 are fed into O2 at its start-up (which implies that O2 is a batch system with elevated initial concentration of xylose). In C4 and C5, on the other hand, the xylose feeding rate is optimized such that the ethanol productivity in O2 is maximized.

We introduced a continuous reactor in C3 and C5 in the above. Chemostats have been preferred less than batch reactors in practice. One of the primary reasons for this is the genetic instability of fermenting organisms as continuous operation will impose strong selective pressure of fast growing cells instead of efficient ethanol producers. This will pose a serious problem for recombinant yeast strains, but may not for the wild-type. Thus, we consider C3 and C5 also as practically meaningful configurations.

In Table 4.2, an overall comparison is made for C1 to C5 at three different sugar concentrations with respect to the actual productivity and its relative change (in comparison to C1), respectively. From the comparison of the C2-C3 group and the C4-C5 group, it is clear that the effect of optimizing the feed rate is most significant at high sugar concentration, and appreciable at medium, but least at low. Strangely, at [GLC]/[XYL]=20/10, the productivities of C4 and C5 with optimal feeding policies are lower than those of C2 and C3, respectively, where all extra sugars are dumped into reactors at their start-up without optimization. This is because the initial feeding of C2 and C3 is closer to the "true" optimal than the feed profiles of C4 and C5 obtained from direct methods involving control profile discretization (Song et al., 2011). Other than this exception, C5 exhibits the highest productivity among all other configurations. In

comparison to C1, C5 achieves a substantial increase of the bioethanol productivity, i. e., by 48, 29 and 23 % when [GLC]/[XYL] = 20/10, 70/35, and 120/60, respectively. [GLC]/[XYL] denotes the mass concentration ratio of glucose and xylose.

4. Conclusion

Various possibilities of increasing the productivity of lignocellosic bioethanol at the fermentation step have been discussed, including metabolic pathway modification of fermenting organisms, optimization of reactor operating conditions, and synergistic combination of different types of reactors. Mathematical models play a key role in establishing rational strategies at such diverse levels. The success of the proposed methods, of course, depends on the reliability of the employed mode. We have demonstrated that the cybernetic models are uniquely effective for the in silico analysis of fermentation systems in view of their capacity to address productivity.

In regard to strain modification, it is emphasized that increasing the productivity rather than the yield is a more suitable goal as the former is directly related to economic competiveness. Note that emphasis on productivity is not at undue expense of yield since any pronounced drop on yield would also lead to a drop in productivity. On the other hand, sole stress on yield at the expense of productivity (due to a possible drop in growth rate) is not conducive to economics. Therefore, in the course of metabolic engineering undergoing several rounds of analysis and synthesis of strains, the productivity issue must be considered from the very outset. While the HCM framework based on a reduced subset of EMs can be useful in developing basic guidelines for flux redistribution of fermenting organisms, reasonable interpretation should be made under the possible loss of modes with significance for strain improvement. For metabolic engineering application, more sophisticated frameworks such as Lumped HCM (L-HCM) (Song & Ramkrishna, 2010; 2011) or Young’s model (Young et al., 2008) represent promising methodologies in the future.

It is also shown that the productivity of lignocellulosic bioethanol can significantly be enhanced by synergistic combination of continuous and fed-batch reactors and optimizing their operating conditions. While experimental verification should follow, our model-based study provides solid proof-of-concept support for the success of the proposed methods.

5. Acknowledgment

The authors acknowledge a special grant from the Dean’s Research Office at Purdue University for support.