Evolutionary Modification

Evolutionary modification is the conventional approach for process synthesis based on the experience of engineers and researchers. This approach condenses this experience into a programmed set of heuristic rules intended to making deci­sions on the process structure. Considering that this method corresponds to a trial — and-error approximation, its main limitation consists in the difficulty of obtaining relevant information in a way suitable for computational calculations.

2.2.1 Hierarchical Decomposition

The hierarchical heuristic method is an extension of the purely heuristic approach that entails the evolutionary modification and combines the heuristic rules with an evolutionary strategy for process design (Li and Kraslawski, 2004). Douglas (1988) proposed a method by which any process can be decomposed into five lev­els of analysis for its design. This strategy has a hierarchical sequential character considering that in each level of analysis different decisions are made based on heuristic rules. This allows generating different alternatives, which are evaluated from an economic point of view using short-cut models. As the method is applied in each one of the five levels, more information becomes available and the tech­nological scheme of the process evolves until its completion. According to this author, hierarchical decomposition comprises the analysis of the process in the following levels:

• Batch versus continuous

• Input-output structure of the flowsheet

• Recycle structure of the flowsheet

• Separation system synthesis

• Heat recovery network

A slightly different hierarchical decomposition scheme corresponds to the onion diagram (Smith, 2005), which starts from the reaction step toward effluent treatment:

• Reactor

• Separation and recycle system

• Heat recovery system

• Heating and cooling utilities

• Wastewater and effluent treatment

The design begins with the reactor selection to move toward the outer surface of the diagram by adding other layers, such as the separation and recycle system. These heuristic methods emphasize the decomposition strategy and screening of alternatives, which allow the fast identification of technological configurations often located near optimal solutions (Li and Kraslawski, 2004). However, the main drawback of these methods is the impossibility of handling the interactions among the different levels or layers due to their sequential character. In spite of this, the nature of this approach allows rapidly discarding many alternative configurations not leading to “good” designs. In addition, the analysis by design levels permits the utilization of process simulators with which the process flow­sheets are completed in an evolutionary way. This methodology has been mostly applied to chemical and petro-chemical processes. Nevertheless, the utilization of these procedures and design schemes is less frequent in the processes of the microbiologic industry.


When cereals are used for producing fuel ethanol, the feedstock enters the pro­cess in the form of grains, which need to undergo some preliminary operations like washing and milling. As pointed out in Chapter 3, the milling of cereal grains like corn, wheat, and barley can be carried out by either the wet-milling or dry­milling process. In wet-milling technology only the starch enters the process for fuel ethanol production once it is separated from the rest of the grain components. In the case of corn, these components represent value-added products employed mostly for animal feed and human food. Moreover, part of the starch can be devi­ated toward the production of sweetening syrups like high-fructose corn syrup (HFCS). This type of milling technology was discussed in Chapter 3 for corn. Currently, 67% of ethanol produced in the United States is obtained in plants using the corn wet-milling technology. The ethanol yields for this process reach 403.1 L EtOH/ton corn (Gulati et al., 1996).

During the dry milling of cereals, the whole grain enters the ethanol produc­tion line, which means that the rest of its components are processed along with starch. The nonutilized components are accumulated in the bottom of the first distillation column and are concentrated as a co-product employed in animal feed. In the United States, 33% of ethanol is produced in plants employing the dry milling of corn, although other grains are used to a lesser degree. While the

Подпись: Steam FIGURE 4.1 Scheme of cooking and liquefaction steps of ground corn grains.
dry-milling process generates co-products with lower value than the wet-milling process, this technology offers higher ethanol yields that can be in the range

419.4 to 460.6 L/ton (Gulati et al., 1996). Furthermore, the dry milling has lower capital and labor costs.

The dry milling of corn is made up of washing and milling the grains until they reach 3 to 5 mm. Then, some impurities are removed (Cardona et al., 2005). The milling is generally accomplished with the use of hammer mills. The starch from the ground grain should be gelatinized since the granules of native starch are not susceptible to enzymatic attack. To carry out this process, a starch suspension con­taining no more than 45% solids is prepared and cooked. To start the cooking pro­cess, the suspension is prepared in hot water (88°C). Madson and Monceaux (1995) emphasize that one of the key issues during the design and operation of production processes for ethanol production from starch is the elimination of contaminating bacteria which requires the maintenance of sterile conditions along the production line until the fermentation step. The decisive factor during the design of cooking systems is not that the starch cooks itself, but the elimination of bacteria. The over­all process for cooking and liquefying ground corn grains is presented in Figure 4.1, which is based on information provided by Madson and Monceaux (1995).

Considering that the conditions needed to reach the sterility are different from the cooking conditions, the preliminary cooking of the suspension of ground grains should be accomplished with minimum solubilization of the potential fermentable substances in order to avoid undesired reactions. These substances should be released only during the subsequent steps of liquefaction, saccharifica­tion, and fermentation. This is explained taking into account that a premature solubilization can lead to the risk of undesired reactions involving fermentable substances, such as sugars contained in the corn fiber. These secondary reactions can provoke the retrogradation of starch (crystallization of soluble starch after
cooling the gelatinized starch) or reactions between carbohydrates and amino acids causing the fermentable compounds to convert into nonfermentable com­pounds (Madson and Monceaux, 1995). In addition, these reactions also contrib­ute to an increased infection risk. Considering these issues, the size of the ground grain should be selected taking into account that the starch and sugars contained in the matrix of the grain particles have minimal mobility, but ensuring, in turn, a suitable hydration of such particles.

The resulting slurry undergoes instantaneous cooking in order to complete the gelatinization process, i. e., to reach the total solubilization of starch components (amylose and amylopectin) and the release of all fermentable substances. For this, jet cookers are employed. In these units, the initial heating is carried out by inject­ing steam directly to the slurry. In the jet cookers, the slurry is maintained at 105 to 110°C for 10 to 15 sec (Lopez-Munguia, 1993). The high temperatures and the mechanical forces allow a fast gelatinization by breaking up the starch granules. The maximum availability of these substances for fermentation with yeasts can be achieved if the process manages to keep the fermentable substances within the matrix of grain particles just until the moment in which the liquefaction is started.

During the liquefaction, thermoresistant a-amylase is used in order to hydro­lyze the starch slurry in a preliminary way that allows abruptly decreasing the viscosity and improving the system operation. This process is carried out in a tank at 80 to 90°C. Moreover, this hydrolysis process allows for the avoid­ance of starch retrogradation, which is a latent danger since the amylose and amylopectin are dissolved in the system. With the aim of preventing undesired secondary reactions, the liquefaction is accomplished in such a way that a mini­mum amount of starch is hydrolyzed, thereby avoiding its conversion into other nonfermentable substances before the fermentation is started (Madson and Monceaux, 1995). Among the products of the liquefaction step are the dextrins, which are oligosaccharides formed as a consequence of the partial breakdown of amylose and amylopectin chains. The stream leaving the liquefaction tank undergoes cooling to reach the optimal temperature for the following steps (sac­charification and fermentation).

Another variant of the process involves a preliquefaction step that is carried out during the cooking step. For this, 10% of the a-amylase dosage is added to the cooking tank. When the slurry is sent to the jet cooker, while the starch gran­ules are broken down, part of the starch chains begin their hydrolysis process. The gelatinized (cooked) corn slurry is cooled to 80 to 90°C, then the remaining dosage of a-amylase is added and the hydrolysis is kept at least 30 min in the liq­uefaction tank (Bothast and Schlicher, 2005). When the corn wet-milling technol­ogy is used, the starch obtained undergoes cooking and liquefaction in a similar way as the ground corn grains, though process conditions can vary slightly.

From the viewpoint of process synthesis, the different schemes for pretreat­ment of starchy materials can cause the evaluation of multiple alternative process configurations derived from a set of several combinations of methods and tech­nological procedures. For this, the simulation tools are of paramount importance. Unfortunately, mathematical models describing starch cooking and liquefaction

have not been effectively published in the open literature. This fact limits the quality of the related simulations and the possibility of involving a more detailed description of these steps during the application of process synthesis procedures to elucidate the best conditions for fuel ethanol production.

Semicontinuous Fermentation

Fed-batch fermentation is one of the most employed cultivation regimes when process microorganisms present catabolic repression, i. e., when high substrate concentrations inhibit specific metabolic processes like those related to cell growth rate. For this reason, the microorganisms grow faster at low substrate concentrations. In fact, the cultivation of S. cerevisiae to produce baker’s yeasts is accomplished by this process. Nevertheless, the application of fed-batch culti­vation to ethanolic fermentation has also offered important results by maintain­ing low substrate levels as ethanol is accumulated in the medium. This type of cultivation regime along with the cell recycling is the most utilized technology in Brazil for bioethanol production due to the possibility of achieving higher volu­metric productivities (Sanchez and Cardona, 2008). To implement such a process, conventional batch fermentation is performed though using a less-concentrated medium. Once the sugars have been consumed, the bioreactor is fed with por­tions of fresh medium or by adding a small amount of medium permanently until the end of fermentation. This continuous feeding of the medium can be done in a linear way (with a constant feeding rate) or according to a more complex func­tion defining the rate with which the fresh medium is added to the fermenter, e. g., by an exponential feeding rate. Control of flow rate of medium feeding is quite advantageous because the inhibitory effect caused by high concentrations of substrate or product in fermentation broth is neutralized. It was observed that the addition of sucrose in a linear or exponentially decreasing way leads to 10 to 14% increase in ethanol productivity (Echegaray et al., 2000). It has been reported that immobilized yeast cells have better performance in fed-batch cultures regard­ing ethanol production (Roukas 1996). Aeration is an important factor during this fermentation regime as well. For fed-batch cultures, Alfenore et al. (2004)

Подпись:Some Fermentation Processes for Ethanol Production from Sugarcane Molasses Using Saccharomyces cerevisiae


ethanol Conc.


Yield, % of



in Broth/g/L

g/(l. h)

Theor. Max.



Reuse of yeast from previous batches; yeast separation by centrifugation




Claassen et al.,1999


Stirred tank with variable feeding rate (exponent. depend. with time)




Echegaray et al., 2000

Repeated batch

Stirred tank; flocculating yeast; up to 47 stable batches




Kida et al., 1991; Morimura et al., 1997

Stirred tank; auto-flocculating yeast separated by settling at the completion of each batch; up to 22 batches; cane juice




Hawgood et al., 1985


CSTR; cell recycling using a settler; flocculating yeast; aeration 0.05 vvm



Hojo et al., 1999

Biostill; residence time 3-6 h; cell recycling by centrifugation; recycled stream from distillation column to fermenter




Ehnstroem, 1984; Kosaric and Velikonja 1995

Cascade of two reactors; flocculating yeast, no cell recycling

Tower reactor; flocculating yeast; cell recycling by settling






Kida et al., 1990 Kuriyama et al., 1993

Fluidized bed; highly flocculant yeast; aeration 0.1 vvm




Wieczorek and Michalski, 1994

Continuous removal of

Removal by vacuum; cell recycling



Costa et al., 2001; Cysewski and Wilke, 1977; Maiorella et al., 1984


Removal by membrane



Maiorella et al., 1984; Shabtai et al., 1991

Source: Modified from Sanchez, O. J., and C. A. Cardona. 2008. Bioresource Technology 99:5270-5295. Elsevier Ltd.

Подпись: Dilute Molasses
Подпись: Yeast-free wine
Подпись: Ethanol

Culture broth

Stillage recycle

FIGURE 7.2 Reutilization of yeast cells and stillage during batch fermentation: (1) fer­menter, (2) separation of cells by centrifugation, (3) distillation column.

have shown that higher ethanol concentrations (147 g/L) could be obtained during cultivation without oxygen limitation (0.2 volumes of air per volume of broth in 1 min or vvm) during only 45 h in comparison to microaerobic conditions.

In the case of multiple or repeated batch fermentation, the use of flocculating strains of yeasts is of great importance. In this type of culture, after starting a conventional batch, the yeasts are decanted in the same vessel where they were cultivated and then the clarified culture broth located in the upper zone of the fermenter is removed. Then, an equal amount of fresh culture medium is added for the following batch. In this way, high cell concentrations are reached and inhi­bition effect by ethanol is reduced without the need of adding flocculation aids or using separation or recirculation devices. These repeated batches can be accom­plished until the moment when the activity and viability of culture are lost as a consequence of a high exposition to fermentation environment. When this occurs, the system should be reinoculated (Sanchez and Cardona, 2008). Some factors, such as agitation, allow flock size to be optimum for reaching higher ethanol concentrations. Even small levels of dissolved oxygen in the medium can facili­tate the neutralization of inhibition effect by ethanol, as suggested by Hawgood et al. (1985). Maia and Nelson (1993) point out that the addition of unsaturated fatty acids can reduce or eliminate the need for microaeration because the oxygen requirement is related to the synthesis of these acids. These authors evaluated the supplementation of sucrose-based medium with fatty acids sources (soy and corn flours) in repeated-batch cultures obtaining best results with corn flour and justi­fying the traditional and empiric use of this component during the fermentation step by the small Brazilian distillers. Some examples of fed-batch and repeated batch fermentations for bioethanol production from sugarcane molasses can be observed in Table 7.1.




V, X, S, P

(a) Continuous Fermentation

Continuous fermentation consists of the cultivation of cells in a bioreactor to which the fresh medium is permanently added and from which an effluent stream of culture broth is permanently removed, as shown in Figure 7.3. The microor­ganisms are reproduced within the bioreactor at a grow rate that offsets the cells withdrawal with the effluent achieving the corresponding steady state. To ensure the system homogeneity and reduce concentration gradients in culture broth, continuous stirred-tank reactors (CSTR) are employed. In this way, a constant production of fermented wort can be obtained without the need of stopping the bioreactor operation in order to perform the periodic procedures typical of batch processes, such as filling-up and unloading. This allows a remarkable increase in volumetric productivity compared to discontinuous or semicontinuous processes (see Table 7.1).

The design and development of continuous fermentation systems have allowed the implementation of more effective processes from the viewpoint of the produc­tion costs. Continuous processes have a series of advantages in comparison to conventional batch processes mainly due to reduced construction costs of the bio­reactors, lower requirements of maintenance and operation, better control of the process, and higher productivities (Sanchez and Cardona, 2008). For those very reasons, 30% of ethanol production facilities in Brazil utilize continuous fermen­tation processes (Monte Alegre et al., 2003). Most of these advantages are due to the high cell concentration found in this type of bioreactor. Such high densities can be reached by immobilization techniques, recovery and recycling of biomass, or control of cell growth. The major drawback is that cultivation of yeasts during a long time under anaerobic conditions diminishes their ability to produce etha­nol. In addition, at high dilution rates (a magnitude proportional to the feed or effluent flow rate) ensuring elevated productivities, the substrate is not completely
consumed and yields are reduced. In general, in commercial processes for etha­nol production, although the productivity is important, it is more relevant for the substrate conversion considering that the main part of the production costs cor­respond to feedstocks (Gil et al., 1991). On the other hand, aeration also plays an important role during continuous ethanolic cultivations. Cell concentration, cell yield from glucose, and yeast viability may be enhanced by increasing air supply, whereas ethanol concentration decreases under both microaerobic and aerobic conditions. Cell growth inhibition by ethanol is reduced at microaerobic condi­tions compared to fully anaerobic cultivation, and specific ethanol productivity is stimulated with the increase of oxygen percentage in the feed stream (Alfenore et al., 2004; Sanchez and Cardona, 2008).

An important feature of continuous processes is related to the diminution of the product inhibition effect. Through cascade of continuous reactors, ethanol obtained in the first reactors is easily transported to the next ones reducing in this way its inhibitory effect (see Figure 7.3b). On the other hand, other configurations employing one fermenter can contribute to the reduction of product inhibition. In particular, the Swedish company Alfa Laval implemented a continuous process for producing 150,000 L EtOH/d in Brazil by Biostill technology (Kosaric and Velikonja, 1995). This process is based on yeast cultivation carried out in a fer­mentation vessel from which a liquid stream is continuously withdrawn to be sent to a centrifuge. From the centrifuge, one concentrated yeast stream is continu­ously removed and recycled back to the fermenter. The other yeast-free stream is directed to a distillation tower. From this tower, concentrated solution of ethanol and stillage are removed. A portion of the stillage is also recycled back to the fermenter in order to maintain the mass balance necessary for conserving steady — state conditions according to the original configuration patented by Alfa Laval (Ehnstroem, 1984), which is shown in Figure 7.4. In this process, there is signifi­cant savings in process water, which reduces stillage volumes and low residence times (3 to 6 h) in the fermenter can be achieved. A modification to this process using no recirculation stream from a distillation column and reaching yields of 96% of theoretical has been patented as well (Da Silva and Vaz, 1989).

Other alternatives of continuous fermentation have been proposed, but many of them still have not reached the commercial level. Some of them require the use of highly flocculating yeast strains similar to the tower and fluidized-bed reactors. These types of reactors allow much higher cell concentrations (70 to 100 g/L) and ethanol productivities, and have a long-term stability due to the self-replenishing of fresh yeasts. Moreover, these fermenters do not require stir­ring devices or centrifugation (Gong et al., 1999). S. uvarum is one of the most promising yeasts to be employed in these configurations thanks to its flocculating properties. All of these efforts have been directed to the increase of productivity and yield, as can be seen in Table 7.1. Another approach for increasing process productivity is the continuous ethanol removal from culture broth during fer­mentation by means of a vacuum or membranes. These configurations enhance efficiency of the process remarkably well, but imply an increase in capital costs. The use of vacuum flash coupled with continuous fermenters could eliminate the

need of heat exchangers and increase the productivity (Costa et al., 2001). These types of configurations involving the application of reaction-separation integra­tion are discussed in Chapter 9.

Case Study. Modeling of SSF of Biomass in Batch and Continuous Regime

The importance of modeling SSF processes is invaluable considering the design of fuel ethanol production processes employing lignocellulosic materials as feedstocks. In a previous work (Sanchez et al., 2005), the analysis of SSF for conversion of cellulose into ethanol was performed in both batch and continu­ous regimes. The kinetic model of such a process was based on the mathemati­cal description developed by South et al. (1995). However, considering that this model will be employed in subsequent procedures and algorithms for process synthesis of ethanol production, the expressions were simplified to not add more complexity to the calculations to be performed during process synthesis and opti­mization procedures. This is justified because process synthesis tools deal with many alternative process flowsheets. These flowsheets involve all the processing steps for conversion of feedstocks into products. As pointed out by Grossmann et al. (2000), there exist different levels of detail for the mathematical description of each unit processes and operations involved in each flowsheet (see Chapter 2). In fact, for the task of process synthesis, it is not desirable to consider models with a higher degree of detail especially if equation-oriented simulators are used, or optimization-based process synthesis procedures are applied. The simplification of the kinetic model mentioned above wasn’t meant to consider its population and adsorption components, but to take into account the rigorous description of the kinetic processes involved.

For simulation of the batch SSF process, the rate equations were extracted from South et al. (1995). Equations (9.1) and (9.2) correspond to the enzymatic hydrolysis

of cellulose and cellobiose, respectively. Equations (9.3) through (9.5) represent cell biomass production, glucose uptake and formation, and ethanol biosynthesis, respectively:












The nomenclature of all the variables and kinetic parameters involved in the above equations are presented in Table 9.3. In Equation (9.1), the last two terms represent the inhibition by cellobiose and ethanol. These terms influence all the rate equation directly or indirectly. Similar expressions can be observed in Equation (9.2) for the inhibitory effect of glucose on the P-glucosidase activity. In Equation (9.3), the expression for biomass formation rate has a lowering term due to high ethanol concentrations present in the broth. For this case, a cellulose conversion (x) of 0.70 was preset. The general mass balance expression for each one of i components (cellulose S, cellobiuse C, cell biomass X, glucose G, and ethanol P) is:


d (Ci) = dt Г




In all cases, a lignocellulosic substrate with cellulose loading of 60 g/L and initial concentrations of cellobiose, biomass, glucose, and ethanol of 0, 1, 8.5, and 0 g/L, respectively, were considered. The selected kinetic model involved the use of T. reesei cellulases and fermentation by S. cerevisiae, according to South et al. (1995). The system of five nonlinear ordinary differential equations was solved by fourth-order Runge-Kutta method using Matlab™ (MathWorks, Inc., USA) with the initial values mentioned above and for a process time of 72 h. Ethanol pro­ductivity and product yield were calculated for this type of regime. The parameter values used for solving the kinetic model can be found in South et al. (1995).




Nomenclature of the Variables and Kinetic Parameters Involved in Equations Derived from the Model of south et al. (1995)






Input stream to the pervaporator, l/h


Exponent of the declining substrate reactivity, dimensionless


p-glucosidase concentration in solution, U/L


Ethanol concentration, g/L


Conversion independent component in rate function, 1/h


Initial ethanol concentration, g/L


Cellobiose concentration, g/L


Output stream for pervaporation (permeate), L/h


Concentration of the i-th component


Recirculation (retentate) stream from pervaporation unit to reactor, L/h


Initial cellobiose concentration, g/L


Rate of formation of compound i,

g/(L x h)


Concentration of cellulose-cellulase complex, U/L


Cellulose component of the biomass substrate remaining, g/L


Feed reactor stream, L/h


Initial cellulose component of the biomass substrate, g/L


Glucose concentration, g/L


Reaction volume, L


Initial glucose concentration, g/L


Residual flow, L/h


Hydrolysis rate constant, g/L


Fractional reactor cellulose conversion, dimensionless


Rate constant for hydrolysis of cellobiose to glucose, g/(Uxh)


Cell concentration, g/L


Monod constant, g/L


Initial cell concentration, g/L


Inhibition of cellobiose hydrolysis

by glucose, g/L


Cell yield per substrate consumed, dimensionless


Inhibition of cellulose hydrolysis by cellobiose, g/L


Ethanol yield per substrate consumed, dimensionless


Inhibition of cellulose hydrolysis by ethanol, g/L


Separation factor in pervaporation


Adsorption constant for cellulosic


Specific capacity of cellulosic

fraction of biomass, L/U

component for cellulose, U/g



Adsorption constant for p-glucosidase for cellobiose, g/L


Maximum cell growth rate, 1/h


Any of the substances involved in the fermentation



Initial concentration in batch processes or feed concentration in continuous processes Product (ethanol)

Source: Adapted from South, C. R., D. A.L. Hogsett, and L. R. Lynd. 1995. Enzyme and Microbial Technology 17:797-803.

FIGURE 9.5 Behavior of batch SSF process for ethanol production from cellulose.

The results for batch SSF process can be seen in Figure 9.5. The final cellu­lose concentration was 28.6 g/L and the ethanol concentration reached at the end of fermentation was 17.5 g/L. The amounts of cellobiose and glucose when the cultivation was finished were near zero, which shows the efficiency of the com­bined process and the neutralization of the inhibitory effects of glucose on cel — lulases. In the case of the SHF process, the accumulating glucose in the medium during cellulose saccharification leads to reduced conversion of cellulose and hydrolyzates with lower concentrations of fermentable sugars. In contrast, dur­ing the SSF process, the accumulation of ethanol in the medium can inhibit the growth rate and, therefore, the ethanol production rate according to the kinetic expressions on which the model was based. The productivity attained by the batch SSF process was 0.292 g/(L x h) and the ethanol yield was 0.454 g/g, cal­culated at 48 h of cultivation.

For solving the model of an SSF process in a CSTR, the mass balance for each of the i substances involved in the process (cellulose, cellobiose, biomass, glucose, and ethanol) was considered according to following equation:

Подпись: (9.7)


FCi0 — WCi + Vrt = 0

Taking into consideration that Equations (9.1) through (9.5) describe the forma­tion or consumption rate of each component, a system of five nonlinear algebraic equations with five unknowns was obtained by applying equation (9.7). For solving this system, the Newton-Raphson algorithm was used with the same initial con­centrations used for the SSF process in batch regime. Equation (9.1) includes a term for cellulose conversion (x) that in the original paper of South et al. (1995) is a func­tion of mean residence time of particulate matter of cellulose. In this case study, the conversion was set to a value of 0.70 with the use of a CSTR for carrying out both transformations (cellulose hydrolysis and ethanol fermentation) and, therefore, assuming an intensive mixing of the reaction volume.

The results obtained for continuous SSF process with a mean residence time of 72 h showed that the cellulose had a more complete conversion and that the ethanol was produced in higher amounts. The concentrations of cellulose and ethanol in the outlet stream were 10.7 and 24.9 g/L, respectively. The biomass concentration in the exiting stream was 5.6 g/L, which is comparable with that of the corresponding batch process. The concentrations of the other involved components in the effluent of the fermenter were near zero, demonstrating the good performance of the SSF process. In this case, the concentration of cellulose in the feed stream was 60 g/L. The productivity attained by the continuous SSF process was 0.345 g/(L x h) and the ethanol yield was 0.506 g/g showing favorable performance indexes related to the batch SSF process.

Simulation of Fuel Ethanol Production from Corn by SSF and Comparison to Sugarcane-Based Process

As mentioned in Case Study 11.1, the aim of simulating the process to obtain bio­ethanol from dry-milled corn can provide valuable information on the suitability of different technological configurations, in this case, the configuration involving the SSF of the starch contained in the corn grain. This simulation was performed in previous works (Cardona et al. 2005b; Quintero et al. 2008) under Colombian conditions and corresponds to the scheme depicted in Figure 11.8. As in the case of cane-to-ethanol conversion, the downstream processes are practically the same (see Case Study 11.1). The differences lie in the biological transformation step. After washing, crushing, and milling the corn grains (dry-milling process), the starchy material is gelatinized in order to dissolve the amylose and amylopectin.

In dissolved form, starch is accessible for enzymatic attack in the following liq­uefaction step. In this step, a partial hydrolysis (about 10%) of the starch chains using thermostable bacterial a-amylase is achieved. The hydrolyzate obtained has reduced viscosity and contains starch oligomers called dextrins. Then, the liquefied starch enters the SSF process where it is hydrolyzed by microbial glucoamylase to produce glucose. This sugar is immediately assimilated by the yeast S. cerevisiae in the same reactor and converted into ethanol. As mentioned above, the dry-milling technology allows the production of DDGS using the recovered solids from the bot­toms of the concentration column. In this case, no co-generation is contemplated;



FIGURE 11.7 Technological configuration of dry-milling process for production of fuel ethanol from corn grains by simultaneous saccharification and fermentation (SSF).

thus, the acquisition and utilization of fossil fuels to supply the steam for the pro­cess are required.

The simulation of this process was carried out using Aspen Plus as well. Main input data employed for process simulation are shown in Table 11.7. As in the case of cane ethanol, the simulation used a production capacity of about 17,830 kg/h anhydrous ethanol. The simulation approach described in Chapter 8, Case Study 8.1 and others was also applied for this case study. Enzymatic hydrolysis and con­tinuous fermentation processes were simulated based on a stoichiometric approach that considered the conversion of starch into glucose as well as the transformation of glucose into cell biomass, ethyl alcohol, and other fermentation by-products. The economic analysis was performed by using the Aspen Icarus Process Evaluator package and the same local conditions of Case Study 11.1.

Some simulation results of main streams for this process are shown in Table 11.8. The compositions of the streams calculated by simulation agree very well with those reported for commercial processes. The DDGS generally contains 9% moisture and 27 to 32% protein (McAloon et al., 2000). Results of many analyses done during a five-year period (1997 to 2001) to determine the composition of DDGS obtained in corn dry-milling ethanol production facilities in the United States (Belyea et al., 2004) revealed good agreement with the simulation data obtained (see Table 11.8). The results obtained for ethanol yield in the process analyzed, along with total operating and capital costs, are shown in Table 11.3. In this regard, the average


FIGURE 11.8 Simplified flowsheet of fuel ethanol production from corn: (1) wash­ing tank, (2) crusher, (3) liquefaction reactor, (4) SSF reactor, (5) ethanol absorber, (6) concentration column, (7) rectification column, (8) molecular sieves, (9) first evaporator train, (10) centrifuge, (11) second evaporator train, (12) dryer. (From Quintero, J. A., M. I. Montoya, O. J. Sanchez, O. H. Giraldo, and C. A. Cardona. 2008. Energy 33 (3):385-399. Elsevier Ltd. With permission.)

yield of technified corn crop in Colombia is about 5 ton/ha for a harvesting time of four months (Quintero et al., 2004). This yield is lower than the corn yield in the United States, the major corn producer in the world. Note from Table 11.3 that the calculated ethanol yield from corn (in terms of produced ethanol per tonne of feedstock entering the plant) is greater than that from sugarcane because of the higher amount of fermentable sugars (glucose) that may be released from the origi­nal starchy material. However, the annual ethanol yield from each hectare of cul­tivated corn is 23.6% lower than that for sugarcane. This preliminary fact shows the comparative advantage of using sugarcane as feedstock for ethanol production under high-productivity conditions for cane cropping in Colombia.

Total operating costs are significantly higher for ethanol production from corn than from sugarcane under Colombian conditions (Table 11.3). This is mostly explained by the feedstock cost, as shown in Table 11.4, where operating costs were

TABLE 11.7

Подпись: Feature feedstock Composition Подпись: Value Corn Starch 60.6%a, cellulose 3.46%, hemicellulose 4.6%, lignin 0.4%, glucose 8.7%, protein 2.2%, fatty acids 3.64%, ash 1.17, moisture 15.5% 50,630 kg/h DDGS Подпись: Feature Product Composition Flow rate Подпись: Value fuel ethanol Ethanol 99.5%, water 0.5% 17,837 kg/h
Подпись: Feed flow rate Co-product Pretreatment Milling Number of mills Hydrolysis (liquefaction) Bioagent Temperature Residence time Number of units Starch conversion Simultaneous saccharif. and fermentation Bioagent Temperature Residence time Number of units Ethanol percentage
Подпись: Ethanol dehydration Technology Number of units Temperature Pressure Cycle time DDGS processing Number of evaporator trains Number of evaporators (1st train) Number of evaporators (2nd train) Average area of each evaporation unit (1st train) Average area of each evaporation unit (2nd train) Type of dryer
Подпись: PSA with molecular sieves 2 116 °C 1.7 atm (adsorption) 0.14 atm (desorption) 10 min 2 2
Подпись: 2
Подпись: a-amylase 88°C 5 min 6 99%
Подпись: 2
Подпись: Glucoamylase and Saccharomyces cerevisiae 33°C
Подпись: 1,186 m2
Подпись: 42 m2
Подпись: Indirect contact rotary dryer
Подпись: 48 h 10 11%

Main Process Data for Simulation of Fuel Ethanol Production from Corn

TABLE 11.7 (Continued)

Main Process Data for simulation

of fuel Ethanol Production from Corn








fuel ethanol



Number of columns


Pressure of columns

1 atm

Involved components


Ethanol content at




distillate (1st column) Ethanol content at




distillate (2nd column)

Substreams in



Source: Quintero, J. A., M. I. Montoya, O. J. Sanchez, O. H. Giraldo, and C. A. Cardona. 2008. Energy 33 (3):385-399. Elsevier Ltd. With permission. a All the percentages are expressed by weight.

TABLE 11.8

Flow Rates and Composition of Some Streams for Corn-Based Ethanol Process



Corn (wt.%)

Purge (wt.%)

ethanol (wt.%)

DDGs (wt.%)
































Total flow rate (kg/h)





Source: Quintero, J. A., M. I. Montoya, O. J. Sanchez, O. H. Giraldo, and C. A. Cardona. 2008. Energy 33 (3):385-399. Elsevier Ltd. With permission.

disaggregated. In comparison to corn, the greater cane demand for producing the same amount of ethanol (about six times the grain requirements) is compensated for by the lower cost of this raw material. In fact, the main part of fuel ethanol costs corresponds to the feedstock: 66.45% and 70.84% using sugarcane and corn, respectively. Usually the feedstock cost for Brazilian cane ethanol is about 60% of the production costs (Xavier, 2007), whereas for corn (mostly transgenic) the cost is about 63% in the United States (McAloon et al., 2000). These results confirm the validity of the data obtained by simulation, as well as the assumptions considered during the economic analysis. Steam and power generation through the combustion of cane bagasse reduces the utilities cost considerably. This makes a big differ­ence in cane-to-ethanol processes and justifies the installation and operation of bagasse combustion systems. On the contrary, a corn-based process requires the consumption of fossil fuels that negatively affects its operating costs and environ­mental performance. Total capital costs are lower for the corn process (Table 11.3), even though it has a more complex configuration involving an additional enzymatic hydrolysis step. For the cane-based process and due to the higher amount of feed­stock to be handled, a greater capacity of equipment is required. In addition, the co-generation system increases the required investment for such types of configura­tions. Nevertheless, the possibility of selling electricity contributes to offset these additional expenses.

The production costs structure of one liter of ethanol produced from corn (as shown in Table 11.4) is comparable to the costs structure estimated by the NREL for the mature process of ethanol production from corn via dry-milling technology in the United States. In the latter case, ethanol production costs reach US$0.232 per liter of anhydrous ethanol (McAloon et al., 2000). The main difference in the production costs are mostly explained by the higher corn prices in Colombia related to cheaper U. S. corn (0.076 US$/kg). In fact, the utilization of imported corn from the United States as feedstock for new ethanol production facilities located on the Colombian Caribbean coast has been proposed by different orga­nizations including the Colombian government. In any case, the volatility of corn prices is a crucial factor to be accounted for. Production costs obtained in this case study are very close to those reported by Macedo and Nogueira (2005) for ethanol production from milo (a kind of sorghum) in the United States. It should be noted that co-product (DDGS) sales in corn ethanol production play a significant role in process sustainability.

The confirmation of the economic viability of the two analyzed processes is presented in Table 11.5. In relation to their profitability indicators, both processes are comparable although the production costs for sugarcane are clearly smaller. Moreover, ethanol from cane offers a higher NPV with a lower internal rate of return (IRR). Different evaluations simulating changes in the price of the main feedstock show that the process employing sugarcane is much more stable to these kinds of variations. Thus, a 71% increase in the price of corn (likely to occur under Colombian conditions) leads to negative NPV during the lifetime of the project. In contrast, this same increase in the price of sugarcane (whose price is much more stable in Colombia) only leads to a 38.7% reduction in NPV and 28.4% decrease in IRR. These results allow the conclusion that ethanol pro­duction process from sugarcane represents the best investment possibility under Colombian conditions.


The mitigation of climate change, energy versus food security, and equity in rural areas are some of the main global goals facing the world today. One of the real options to achieving these goals is the development of cleaner and renewable energy sources, such as biofuels. Fuel ethanol from sugarcane and corn is world­wide the most important biofuel, followed by biodiesel from rapeseed. However, speculations about the possible impacts of fuel ethanol production create confu­sion about its convenience.

This book stresses the need to analyze and design accurately fuel ethanol pro­duction systems based on a process engineering approach as the source of techni­cal information for assessing the real impacts of this biofuel on energy, food, and environmental balances. The processes for producing fuel ethanol from different feedstocks are not all the same in terms of technologies, impacts, and benefits. There is diversity among scientists, engineers, governments, or decision mak­ers who try to analyze fuel ethanol projects. The book, through the 13 chapters, describes a logic and structured strategy for further analysis and development of fuel ethanol production from different feedstocks including energy crops and lignocellulosic biomasses.

The authors aim to offer a comprehensive review as well as results from more than 15 years in process engineering and ethanol research developed by the Chemical and Biotechnological Processes Design Group at the National University of Colombia, Manizales campus. Additionally, the process intensifica­tion reached by integration of reaction and separation processes in fuel ethanol production is analyzed in detail.

Chapter 1 discusses bioenergy focusing on liquid biofuels and describes the development of biofuels production in the world. Chapter 2 discusses the role of process synthesis and design as a key strategy for rapid and high-tech analysis and design of complex biotechnological processes. Chapters 3 and 4 describe the characteristics and technological implications of using different sugary and starchy crops as well as lignocellulosic feedstocks. Chapter 5 emphasizes the hydrolysis technologies for the saccharification of carbohydrate polymers as cel­lulose and starch. Chapter 6 analyzes the microorganisms used in ethanol produc­tion. Chapters 7 and 8 describe in detail the fuel ethanol production technologies for different feedstocks. Chapter 9 analyzes the new technological innovations based on process integration as a way for reducing energy consumption. Chapter 10 addresses the environmental issues regarding bioethanol production. Here, environmental impacts are discussed in terms of waste reduction algorithm and the life cycle assessment. Then, these impacts are calculated as a result of over­all energy and material balances obtained from a process engineering approach. Chapter 11 describes the technological configurations for fuel ethanol produc­tion in the industry. Chapter 12 discusses the possible factors that could affect food security when fuel ethanol production and consumption are encouraged in different countries. Chapter 13 summarizes the main topics discussed in the book in terms of perspectives and challenges in research and development for fuel etha­nol production. Most of these chapters are supported by case studies that include calculations and discussion of results.

The authors believe that accurate analysis and precise design as well as pro­active government policies in fuel ethanol production will contribute to fair and sustainable development of energy crops in the world promoting new alternatives for poor rural areas. Finally, this book is an open and dynamic work waiting for improvements and suggestions from the readers.

C. A. Cardona

O. J. Sanchez L. F. Gutierrez


Sugarcane (Saccharum officinarum L.) is a perennial grass cropped in tropical and subtropical zones from Spain to South Africa. The origin of sugarcane is in the South Pacific islands and New Guinea. The main feature of sugarcane is that a sucrose-enriched juice is formed and accumulated in its stalk. This juice is extracted and used for sugar production. For its growth, sugarcane requires a moist and warm climate alternating with dry seasons. It grows better in plain or slightly sloping lands with alluvial or clay soil with abundant luminosity. Nevertheless, sugarcane grows in any soil of good quality provided there is appropriate humid­ity. This crop is grown at 16 to 29.9°C and with a pH of 4.3 to 8.4 in soils with annual precipitations of 47 to 429 mm. Cane tolerates flooding (Duke, 1998). Sugarcane is cut every 12 months on average, although the range goes from 6 to 24 months. One plantation can last up to five years.


World Production of Sugarcane (2007)


















































South Africa





































Source: FAO. 2008. FAOSTAT. Food and Agriculture Organization of the United Nations (FAO). http:// faostat. fao. org (accessed February 2009)

Sugarcane is one of the most important crops in the world and plays a crucial role in the economies of many developing and emerging countries. Brazil is the major sugarcane producer followed by China and India (FAO, 2008; Table 3.1). Brazil fielded about 5.14 million ha in 2007, while China fielded 3.55 million. Among main cane producers, Colombia has the highest yields achieving on aver­age 88.89 ton/ha (FAO, 2008). If considering only the cane intended for sugar and ethanol production, the Colombian cane yield reaches more than 122 ton/ha (Asocana, 2006; Espinal et al., 2005b). In some developing countries, sugarcane is cultivated by many rural communities for producing noncentrifugal sugar (solid brown sugar), a low-cost sweetener with significant content of minerals and traces of vitamins, widely used by the populace in those countries. This product is known as gur in India or panela in Colombia. In particular, the Colombian government is encouraging the use of the cane varieties normally employed for panela produc­tion (generally, low-yield varieties) in order to utilize them for fuel ethanol produc­tion. In this way, these communities can improve their socioeconomic conditions.


Подпись: Components Cellulose Fructose Glucose Fat Hemicellulose Lignin Protein Sucrose Water Nonfermentable sugar Other reduced compounds Organics acids Ash image017

Average Sugarcane Composition

The content of sucrose, glucose, and fructose in sugarcane is significant. Process microorganisms to synthesize ethanol assimilate these sugars. Besides these carbohydrates, cane contains fiber (mainly cellulose, hemicellulose, and lignin), proteins, fats, ash, and small amounts of other substances, such as other nonfermentable sugars (e. g., raffinose), organic acids, and other reducing com­pounds. The composition of sugarcane depends on the conditions under which it was cultivated. In particular, there are great variations in the content of mois­ture, sugars, and ash. The composition of feedstocks is very important during the simulation of ethanol production processes. As process simulation is a funda­mental tool during process synthesis, the suitable specification of cane compo­nents directly influences the quality of obtained results. As an example of such specification, average percentages (by mass) of the cane constituents are shown in Table 3.2. The data were taken from information corresponding to cane varieties from Brazil, Venezuela, Colombia, and Cuba (Andrade et al., 2004; Gonzalez and Gonzalez, 2004; Sanchez and Cardona, 2008a; Suarez and Morin, 2005).

Hydrolysis of Carbohydrate Polymers

The hydrolysis of glucans is a significant source of fermentable sugars for fuel ethanol production. The most important glucans in ethanol industry are starch and cellulose. In this chapter, the features of starch and cellulose hydrolyses are discussed. In particular, the difficulties related to the enzymatic hydrolysis of cellulose are analyzed taking into consideration the enzyme complexes used and the presence of solid particles in the reaction mixture. Some process engineering aspects emphasizing the kinetic modeling of these processes are also discussed.


In Chapter 4, the use of a-amylase during starch liquefaction was mentioned. It is worth emphasizing here some general issues related to the enzymatic hydrolysis of starch. The utilization of enzymes to break down the starch has some advantages over the hydrolysis using acids. In the latter case, strong conditions are required to achieve the degradation of starch (150°C, pH of 1.5 to 1.8). The amylolytic enzymes work under milder conditions (temperatures lower than 110°C, neutral pH) with the corresponding energy savings. In addition, the enzymatic process does not generate compounds resulting from degradation or oxidation of sugars due to the very high specificity of the enzymes (Lopez-Munguia, 1993). For these reasons, the sweeteners industry does not use the acid hydrolysis of starch. This process has been taken over by the fuel ethanol industry in order to obtain the fermentable sugars needed for yeast cultivation.

Main amylases employed for starch hydrolysis at the industrial level are from bacterial and fungal origin though some plant enzymes are eventually used (Table 5.1). The enzyme a-amylase obtained from thermophilic bacteria or produced by a recombinant microorganism using the gene obtained from a thermophilic organism is one of the most used amylases. This enzyme randomly hydrolyzes the a(1,4) glycosidic bonds within the chains of both amylose and amylopectin. For this enzyme to attack the starch, the previous gelatinization of starch should have been carried out. Thus, the broken starch granules release the amylose and amylopectin and the enzymatic action can be started. The a-amylase can support high temperatures of up to 110°C while keeping its activity, thus it is ideal for starch liquefaction process. The molecular weights and optimum temperatures of enzymatic activity of the a-amylases most commonly employed for starch processing are shown in Table 5.2. Apar and Ozbek (2004) provide information about the effects of operating conditions on the enzymatic hydroly­sis of corn starch using commercial a-amylase. In general, the optimum pH of

TABLE 5.1 Main enzymes

used for starch Hydrolysis








Bacillus licheniformis B. subtilis







Asergillus oryzae Bacillus cereus Barley

a(1,4) from nonreducing ends






A. niger 1 Rhizopus sp.

a(1,4) from nonreducing ends and a(1,6)




B. acidopullulyticus Klebsiella pneumoniae




Origin and Properties of Different a-Amylases and Glucoamylases


Microbial source








Bacillus subtilis




Nigam and Singh

(1995); Pandey et




al. (2000a)


Bacillus licheniformis





Aspergillus awamori




Nigam and Singh (1995); Pandey et

Aspergillus niger I




al. (2000a)

Aspergillus niger II




Aspergillus oryzae I




Aspergillus oryzae II




Aspergillus oryzae III




Aspergillus saitoi Cephalosporium










Mucor rouxianus I




Mucor rouxianus II




Penicillium oxalicum I




Penicillium oxalicum II




Rhizophus delemar




the a-amylase is about 6. For this reason, the pH of the cooked starch should be adjusted before the enzyme addition during the liquefaction process.

Glucoamylase (amyloglucosidase) is the other most employed enzyme in starch — to-ethanol process. This enzyme is generally obtained from Aspergillus niger or a species of Rhizopus genus (Labeille et al., 1997; Nigam and Singh, 1995; Shigechi et al., 2004). The glucoamylase is an exo-enzyme that hydrolyzes the a(1,4) bonds from the nonreducing ends of amylose or amylopectin chains forming glucose. Unlike a-amylase, most glucoamylases have the ability to hydrolyze the a(1,6) bonds in branching points of the amylopectin, though the hydrolysis rate of this bond is 15 times lower than for the a(1,4) bonds (Lopez-Munguia, 1993; Pandey et al., 2000b). This feature allows this enzyme to convert the dextrins formed dur­ing the liquefaction step into glucose. The optimum temperatures of enzymatic activity for glucoamylases are lower than those of a-amylases employed during starch liquefaction (see Table 5.2). Consequently, the cooling of liquefied starch is needed to ensure an appropriate conversion toward glucose. In some commercial formulations of amylases, the enzyme pullulanase, which specifically hydrolyzes the a(1,6) bonds, is also employed. Finally, the P-amylase is mostly employed in the brewing industry and for production of maltose syrups.

The process of hydrolysis (or saccharification) of the stream exiting the lique­faction tank is aimed at obtaining a glucose-rich solution for its later fermenta­tion. This stream is adjusted at a pH of 4.5 and cooled down to 65°C in order to ensure the optimum conditions of the hydrolysis process (Bothast and Schlicher, 2005). The saccharification product is called corn mash if this cereal is the feed­stock employed or saccharified starch if starch is the feedstock employed, as in the case of wet milling.

The steps related to the starch degradation are responsible for 10 to 20% of the energy consumption of the ethanol process in the case of fuel ethanol produced from starchy materials. One potential option to minimize this high amount of energy is the substitution of enzymatic hydrolysis technologies in liquid media at high temperatures with technologies involving the starch hydrolysis using amy­lases working at low temperatures in solid phase. This approach would make pos­sible the “cold hydrolysis” of the native starch (Cardona and Sanchez, 2007). For this, the discovery and characterization of new enzymes displaying these prop­erties are required (Robertson et al., 2006). Some microorganisms, such as the bacterium Clostridium thermohydrosulfuricum, have the ability to digest nonge — latinized starch and convert it into ethanol at 66°C. However, the productivities attained are too low (Mori and Inaba, 1990).


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).


The development of technologies for separation-separation integration has been linked to the development of the different unit operations involved during down­stream processes and to new approaches for process intensification. The examples of separation-separation integration in the case of ethanol production mostly cor­respond to integration of the conjugated type, i. e., when integrated processes are carried out in different equipments closing the flowsheet by fluxes or refluxes.

Integration possibilities are particularly important for ethanol dehydration. In Chapter 8, the features and advantages of the integrated process of extractive distillation were emphasized. In the specific case of fuel ethanol production, the utilization of salts as extractive agents (saline extractive distillation) has dem­onstrated certain energetic advantages compared to other dehydration schemes according to some reports (Barba, et al. 1985; Llano-Restrepo and Aguilar-Arias, 2003). Cited results indicate that energy costs of saline distillation were lower than is the case of azeotropic distillation (using benzene, pentane, or diethyl ether), extractive distillation (using ethylene glycol or gasoline), or solvent extrac­tion, being almost the same as the costs of pervaporation. Pinto et al. (2000) employed Aspen Plus for the simulation and optimization of the saline extractive distillation for several substances (NaCl, KCl, KI, and CaCl2). This configura­tion was compared to the simulated scheme of conventional extractive distil­lation with ethylene glycol and with data for azeotropic distillation. Obtained results showed considerably lower energy consumption for the process with salts. However, for this latter case, the recovery of salts was not simulated. Thus, if evaporation and recrystallization of salts is contemplated, energy requirements could significantly increase, taking into account the energetic expenditures. In this way, the utilization of commercial simulators shows the viability for pre­dicting the behavior of a given process configuration provided the appropriate thermodynamic models of studied systems has been completed.

Gros et al. (1998) describe the process synthesis for ethanol dehydration using near critical propane. To this end, these authors combined thermodynamic mod­els for the description of ethanol recovery under supercritical conditions based on Group Contribution Associating Equation of State (GCA-EOS) with robust methods of simulation and optimization (integrating the SQP with MINLP). Considering the energy consumption as the objective function, the developed software analyzed the main units required by the configuration: high-pressure multistage extractors, distillation columns, and multiphase flash separators. Obtained results showed that configurations involving vapor recompression and feed preconcentration are competitive alternatives in comparison to azeotropic distillation (Cardona and Sanchez, 2007).

The utilization of pervaporation for the production of absolute (anhydrous) ethanol through its coupling with the previous distillation step has been reported (Cardona and Sanchez, 2007). The modeling and optimization of the process using MINLP tools showed 12% savings in the production costs with a 32% increase in membrane area and a reduction in both reflux ratio and ethanol concentration in the distillate of the column (Lelkes et al., 2000; Szitkai et al., 2002). Through pilot plant studies, the integration of distillation process with the pervaporation has been attained resulting in good indexes in terms of energy savings. These sav­ings are due to the low operation costs of pervaporation and to the high yield of dehydrated ethanol, typical of pervaporation processes (Tsuyomoto et al., 1997). The comparison between azeotropic distillation using benzene and the pervapo- ration system using multiple membrane modules showed that, at same ethanol production rate and quality (99.8 %), operation costs, including the membrane replacement every two to four years, for the pervaporation system are approxi­mately one-third to one-quarter those of azeotropic distillation.

complexed cellulose systems. Biotechnology and Bioengineering 8 (7):797-824.