Besides design procedures, process systems engineering applied to fuel ethanol production processes consists of operation and control, especially when differ­ent technologies are to be implemented at industrial level. This is particularly important for continuous ethanologenic fermentation. If such a system is operated under conditions corresponding to a stable steady state, any small perturbation in input parameters (like dilution rate, temperature, or substrate concentration of the feed) will be compensated for by the same system. If the system is operated near an unstable steady-state, any small perturbation could not be offset by the culture and the system function can result in conditions of lower productivity or oscillate with the time.

The problem of multiple steady-states is related to the fact that for a same single value of process operating parameters, typically the dilution rate and inlet substrate concentration, the system can attain different steady-states, each with different performance indicators (yield, productivity, conversion). The analysis becomes much more complicated if considering that these steady-states can be stable or unstable. Often, just the unstable steady-state exhibits higher


FIGURE 7.10 Operational diagram for continuous cultivation of yeasts. Continuous lines correspond to stable states while the dashed line corresponds to unstable states.

productivities or ethanol yields. This makes the industrial operation of these processes very complex because small variations in dilution rate or composition of culture medium can make the system migrate to the steady-state with a lower, though more stable, performance indicator. This situation is schematically illus­trated in Figure 7.10 for continuous yeast cultivation. Just under the conditions corresponding to the point marked with the arrow, the system presents its high­est productivity. In this way, an optimum operation of the bioreactor is obtained when the system is near the point in which the fermentation destabilizes. If a perturbation occurs, the system can fall down to its stable state with lower pro­ductivity. The goal of control is to keep the system precisely in its optimum operating point.

This indicates the importance of conducting studies about stable states in continuous bioreactors. These studies could provide the optimal values of opera­tion variables in order to design highly effective processes. Perego et al. (1985) showed that instability during the operation of continuous fermentation from sugarcane molasses depends at a high degree on the temperature of cultivation. However, these authors did not report any mathematical description of the pro­cess in order to explain this characteristic behavior. Laluce et al. (2002) con­structed a special five-stage continuous fermentation system with cell recycling and different temperatures in each stage. With this system, they experimentally assessed the effect of fluctuations in operating temperature that occur under industrial conditions on fermentation performance. These fluctuations produced variations in the cell concentration and cell viability. Hojo et al. (1999) showed that microaeration plays an important role in the stabilization of concentrations of ethanol, substrate, and cells during continuous cultivation of sugarcane syrup with cell recirculation of S. cerevisiae. Without air addition at low rates (0.05 vvm), these concentrations had significant fluctuations. These authors adjusted the obtained data to one simple model, but no dynamic simulation of the studied process was performed.

One source of fluctuations leading to oscillatory behavior of continuous etha — nolic fermentation using S. cerevisiae is the high content of ethanol in the broth. This high concentration is typical of VHG fermentations, and wide variations in ethanol, cell, and substrate concentrations are observed under VHG condi­tions. Bai et al. (2004) showed that the utilization of packed-bed reactors attenu­ates these oscillations and quasi-steady-states can be attained, but the causes and mechanism of this attenuation require further research. Alternatively, an oscilla­tory regime of fermentation can be employed for ethanol production as patented by Elnashaie and Garhyan (2005). In this case, the required equipment comprises a fermenter, a process control system capable of operating the fermenter under chaotic conditions, and a membrane selective for ethanol.

The development of proper models describing the continuous oscillatory fer­mentation allows the deep stability analysis of cultures presenting this behavior that is characteristic of continuous cultures of Z. mobilis and S. cerevisiae under certain conditions, such as specific dilution rates or ethanol concentrations in the broth. Tools like dynamic simulation and, especially, bifurcation analysis can provide valuable information for design of more effective continuous fermenta­tion processes. Dynamic simulation is required for control of fermentation pro­cesses including those carried out in batch, fed-batch, and continuous regimes. For instance, through a nonstructured mathematical model that considers four state variables (concentrations of cells, substrate, product, and CO2 evolution rate), Thatipamala et al. (1996) developed an algorithm for the prediction of nonmea­surable state variables and critic parameters varying with time, which allowed the online estimation of these variables and the adaptive optimization of a continuous bioreactor for ethanol production.

Oscillatory behavior of fermentations imposes great challenges for biopro­cess design. Several experimental runs with forced oscillations of Z. mobilis culture were carried out in order to formulate and test a model describing the oscillatory behavior (Daugulis et al., 1997; McLellan et al., 1999). The model makes use of the concept of “dynamic specific growth rate,” which considers inhibitory culture conditions in the recent past affecting subsequent cell behav­ior. Through dynamic simulation, it was shown that the lag in the cells response was the major factor contributing to the oscillations. Moreover, the change in morphology to a more filamentous form may explain the change in specific growth rate and product formation characteristics. However, Zhang and Henson (2001) point out that dynamic simulation has several limitations for analyz­ing the dynamic behavior of fermentation processes only a limited number of simulations tests can be performed and that it does not easily reveal the model characteristics leading to certain dynamic behaviors. In contrast, nonlinear analysis allows a deeper insight into this type of processes. Nonlinear analy­sis provides tools for studying the appearance of multiple steady states with changes in parameter values of the model. These authors performed the bifur­cation analysis for models describing continuous alcoholic fermentation of Z. mobilis and S. cerevisiae and concluded that employed tools allowed revealing
an important characteristic of the employed models as the lack of model robust­ness to small parameter variations and the coexistence of multiple stable solutions under the same operating conditions. An experimentally verified, unsegregated, two-compartment model of ethanol fermentation was utilized to assess the dynamic behavior of a stirred-tank bioreactor with a membrane for the in situ removal of ethanol (Garhyan and Elnashaie, 2004; Mahecha-Botero et al., 2006). Through bifurcation analysis, it was shown that the operation of the reactor under periodic/chaotic attractor’s conditions gives higher substrate conversions, yields, and production rates than the corresponding steady-states. It also has been shown that the membrane acts as a stabilizer of the process eliminating the oscillations (Cardona and Sanchez, 2007).

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