Most methods involving distillation for ethanol dehydration utilized in the indus­try comprise at least three steps: (1) distillation of dilute ethanol until it reaches a concentration near the azeotropic point, (2) distillation using a third component

added that allows the ethanol removal, and (3) distillation to recover the third component and reutilize it in the process (Montoya et al., 2005). The azeotropic distillation corresponds to this scheme. This technology consists of the addition of an entrainer to the ethanol-water mixture to form a new azeotrope. The azeo­trope formed is ternary (involves three components) and allows a much easier separation in schemes involving two or three distillation columns. Among the substances most used as entrainers for separation of ethanol-water mixtures are benzene, toluene, я-pentane, and cyclohexane.

In the case of benzene, the process comprises one dehydration (azeotropic) col­umn, which is fed with the mixture containing 90 to 92% ethanol from rectifica­tion column (Figure 8.3). The benzene is added in the upper plate. From the lower part of the azeotropic column, ethanol is removed with water content below 1%, while the overhead vapors in the column top, which correspond to a mixture with a composition equal or near to the composition of the ethanol-water-benzene ternary azeotrope, are condensed and sent to a liquid-liquid separator (decanter). Due to the mixture properties, the ternary azeotrope is located in the immiscibil — ity zone of the ethanol-water-benzene system (Figure 8.4), so once condensed, it is separated into two liquid phases: one phase with high benzene content that is recirculated as a reflux to the azeotropic column, and the another phase with higher water content that is fed to a smaller column for entrainer recovery (strip­ping column). The distillate from the stripping column has a significant benzene concentration and, for this reason, this stream is recycled back to the azeotropic column or to the decanter. The bottoms of the stripping column contain mostly water. If these bottoms have an important amount of ethanol, they are recircu­lated to the concentration column; in this way, the separation of water and ethanol is attained and the entrainer is recovered. As the process is operated in continuous

regime, the benzene is permanently recirculating within the system. Nevertheless, small amounts of this compound leave the scheme along with the ethanol or water streams, thus a make-up stream is required. This latter stream is fed to the first plate (from the top) of the azeotropic column or is mixed with the reflux stream coming from the decanter to this same column.

The phase equilibrium properties are crucial for the design of an azeotropic distillation scheme. This equilibrium can be represented in the ternary diagram shown in Figure 8.4 for the case of benzene. The principles of topologic ther­modynamics can be applied to the analysis of this diagram (Pisarenko et al., 2001). To provide more clarity, molar fractions are employed through the analysis are identical for compositions expressed in mass fractions. The feeding of the starting ethanol-water mixture is indicated by the straight line FB and is accom­plished in such a way that the point M representing the pseudo-starting state of the system is located inside the distillation region I. This region is delimited by the two distillation boundaries, which coincide in the ternary azeotrope with the minimum boiling point. The distillation boundaries define the process constraints because any distillation operation (indicated by straight lines of mass balances) cannot have distillates and bottoms whose compositions are in different regions. When drawing a balance line corresponding to the indirect distillation for the point M (the prolongation of the straight line EM until the distillation boundary represented by the curve AC), bottoms with a composition corresponding to pure ethanol E and distillate with a composition near to the ternary azeotrope repre­sented by the point N are obtained. The composition of this distillate corresponds to the immiscibility zone of the system so it is separated into two liquid phases indicated by the points R and S that are determined following the tie lines of the liquid-liquid equilibrium plot (bimodal plot). The point R represents the liquid phase with higher water content (the raffinate) and the point S represents the liq­uid phase with higher benzene content (the extract) that is evidenced by its higher proximity to the vertex B (pure benzene) compared to point R. The stream with the composition of the point S is recirculated as the reflux to the azeotropic col­umn. The raffinate stream, in turn, undergoes distillation in the stripping column, which is represented by the balance line WRP that is located in the distillation region II. The composition of point P corresponds to the composition of the distil­late stream from the stripping column that is recycled back to either the azeotropic column or the decanter. This type of analysis allows one to predict the behavior of the system without carrying out a rigorous assessment (short-cut method). These short-cut methods allow one to obtain valuable information for the subsequent rigorous modeling of the system. In particular, the application of these methods facilitates the specification of the operating conditions in the distillation columns when commercial process simulators employing rigorous methods are used.

The above-described distillation receives the name hetero-azeotropic distillation considering that the entrainers form azeotropes located within the immiscibility zone of the system. This implies its separation into two liquid phases. The utiliza­tion of n-octane as a co-entrainer along with benzene has been proposed in order to decrease the energy costs of the traditional process (Chianese and Zinnamosca, 1990). The simulation and optimization accomplished based on a mass-transfer model (nonequilibrium model) for this process show that if the values of operat­ing parameters of the column are adjusted to minimize the amount of plates in the azeotropic column, it is possible to reduce the capital costs, but increasing the heat flow rates required implies an increase of the energy costs. In terms of energy costs, the most influencing process parameters are the reflux ratio and flow rate of the stream recirculated from the stripping column to the azeotropic column. For these parameters, their optimum values have been obtained according to economic con­siderations (Mortaheb and Kosuge, 2004). However, the utilization of benzene as an entrainer is not desirable due to its carcinogenic properties. In addition, the azeo­tropic distillation using this compound leads to the appearance of multiple steady states and the occurrence of a parametric sensibility related to small changes in col­umn pressure (Wolf Maciel and Brito, 1995). Taking into account these drawbacks, the use of less contaminant entrainers has been attempted. In particular, some new ethanol-producing facilities in Brazil employ cyclohexane as the entrainer for etha­nol dehydration by hetero-azeotropic distillation.

Significant efforts to reduce the elevated energy consumption of the azeotropic distillation using such entrainers, such as benzene, cyclohexane, diethyl ether, and я-pentane, have been made. Under real conditions, distillation columns are configured in such a way that the dehydration process is operated using the heat recovered from the primary distillation system (concentration and rectification columns). Alternatively, rectification and stripping columns can be operated using the heat released in the azeotropic column. For fuel ethanol industry in the United States, the consumption of thermal energy during the separation and dehydration steps employing the azeotropic distillation is about 4.73 MJ/L ethanol on average (Madson and Monceaux, 1995).

The stillage obtained in processes employing sugarcane, acid hydrolyzates of wood, or spent sulfite liquors as feedstocks can be incinerated, which offers a posi­tive energy return as well as the recovery of minerals. Before its incineration, the stillage should be concentrated up to 50 to 60% solids in 4- or 5-effect evapora­tors. Just the combustion of stillage offers the energy needed for this concentration process. The incineration ash has about 30 to 40% K2O and 2 to 3% P2O5, which converts it into a fertilizer after its dilution in water and neutralization with sulfu­ric acid (Nguyen, 2003; Olguin et al., 1995; Sheehan and Greenfield, 1980). The burners of stillage require special designs in order to support its high ash content.

Most of the fuel ethanol produced in the world is currently sourced from starchy biomass or sucrose (molasses or cane juice), but the technology for ethanol pro­duction from nonfood plant sources is being developed rapidly so that large-scale production will be a reality in the coming years (Cardona Alzate, 2008; Lin, 2006). Moreover, when using nonfood raw materials, food security is not affected by this industry and improves ethanol’s social and environmental impacts. The negatives of lignocellulosic biomass are the access (including transport costs), pretreatment cost for breaking its complex structure, and the production of nonde­sired products that can inhibit the enzymes and microorganism activities during hydrolysis and fermentation steps.

The importance of a particular type of biomass depends on the chemical and physical properties of the large molecules from which it is made. The chemical structure and major organic components in biomass are important in the devel­opment of processes for producing fuel and chemicals derived from it. Biomass contains varying amounts of cellulose, hemicellulose, and lignin, and a small amount of extractive (Bridgewater, 1999).

Worldwide generation of lignocellulosic residues is estimated to be more than about 4 billion tons each year. However, there are concerns about the importance of harvest residues in maintaining soil quality. Adding harvest residues to soils is very important in the provision of plant nutrients and for the water binding capacity of soils. Low levels of soil organic carbon contribute much to poor agricultural yields in large parts of sub-Saharan Africa and the tropical and subtropical areas of Asia. If this practice is not controlled, use for bioethanol of crop residues will exacerbate soil degradation and aggravate food insecurity (Lal, 2008; Reijnders, 2008).

Kim and Dale (2004) analyzed the size of the bioethanol feedstock resource at global and regional levels taking into consideration wasted crops (crops lost in distribution) and lignocellulosic biomass (crop residues and sugar cane bagasse). These authors estimate that the global potential ethanol production from these feedstocks accounts for 491 gallons/year, which is 16 times higher than current ethanol production and which could replace 32% of the global gasoline consump­tion. Rice straw is the feedstock that potentially could produce the largest amounts of ethanol, followed by wheat straw. In general, the total volume of energy from agricultural residues is estimated at 12 EJ (ExaJoule = 1018 J), as shown in Hall et al. (1993).

The main disadvantage of producing fuel ethanol is that its production is more expensive than the production of fossil fuels. From a technical viewpoint, gasoline

Source: Canadian Renewable Fuels Association. 2000. Environmental Benefits of Ethanol.

blended with ethanol conducts electricity and its RVP is higher than nonblended gasoline. This implies a higher volatilization rate that can lead to ozone and smog formation (Thomas and Kwong, 2001). Although the addition of ethanol increases the evaporation rate of organic volatile compounds, many experts consider that the reduction of CO emissions effectively offsets the volume losses due to the volatility increase (Ghosh and Ghose, 2003).

One of the most troubling issues regarding the utilization of gasoline blends is the tendency of ethanol to form two liquid phases in the presence of water: one aqueous phase with an important ethanol content and one organic phase. If water contaminates the fuel, the water dissolves into the ethanol and disperses through the tank. Once it exceeds the tolerance level, the alcohol-water mixture will sepa­rate from the gasoline. Depending on individual conditions, about 40 to 80% of the ethanol will be drawn away from the gasoline by the water, forming two distinct layers. The top layer will be a gasoline that is a lower octane and perhaps out of specification, while the bottom layer is a mix of water and ethanol that will not burn (Central Illinois Manufacturing Company, 2006). To avoid the phase separation, ethanol-gasoline blends are not directly transported by pipelines. In general, ethanol is added to gasoline in bulk terminals (retail outlets, the end link of the supply chain for wholesale distribution) or in tanker trucks at the terminal immediately before delivery to the service station. In these points, reception and storage tanks are steel-made to minimize the exposure to water that can infil­trate into the distribution and storage systems for gasoline as well. This problem can be overcome by using stabilizing additives such as higher alcohols, fusel oils (mixture of higher alcohols, fatty acids, and esters), aromatic amines, ethers, and ketones. For instance, the addition of 2.5 to 3% isobutanol ensures the stability of ethanol-gasoline blends containing up to 5% water at temperatures down to -20°C. In fact, the phase stability of gasoline blends increases with high ethanol concentrations (Rasskazchikova et al., 2004). Another approach for stabilization
of these gasoline blends is the modification of the carburetor that also can allow the utilization of blends with higher ethanol contents (Yuksel and Yuksel, 2004).

On the other hand, ethanol, like all the alcohols in general, is highly corrosive, depending on water content. The higher the molecular weight of the alcohol, the less corrosive it is. To neutralize this effect, corrosion inhibitors can be added. Among these inhibitors are hydroxyethylated alkylphenols, alkyl imidazolins, and different oils obtained during cyclohexane production. Ethanol can have a negative effect on rubber and plastic materials because it penetrates hoses and tight seals, which increases fuel losses due to evaporation. Nevertheless, the cur­rent level of development of the polymer industry makes it possible to select mate­rials resistant to penetration of alcohols so that fuel losses are eliminated. These new polymers are being used in the automobile parts industry (Rasskazchikova et al., 2004).

From an economic point of view, fuel ethanol also presents some drawbacks that depend on the situation in all countries of the world. Particularly, in the case of the sugar sector, there exists the risk that sugar producers involved in ethanol production can reduce the amount of ethanol produced when sugar prices are especially high on the international market. For this reason, some governments, like Colombia’s for instance, have adopted measures to avoid this situation by link­ing the international sugar price to the value paid to ethanol producers. However, several authors and nongovernmental organizations (NGOs) have expressed their concern regarding the fact that this price structure and related tax exemptions favor the economic groups controlling sugar markets in each country (Chaves, 2004). Another great concern, when an ethanol oxygenation program for gasoline is being implemented, is in the pressure over food prices related to feedstocks from which ethanol is produced, especially sugar and corn. In particular, the bio­fuels were hardly criticized when both oil and food commodities prices reached their historical peaks during 2008. Specifically, it was estimated that the “bio­fuels effect” could have provoked an increase in the international price of food commodities and crops of about 35% in that year. This statement was weakened when the oil price fell at the end of 2008 (corresponding to the beginning of the global financial crisis) and the price of food commodities also fell to a percentage higher than 35%. This could indicate than the elevated value of food feedstocks was more linked to the oil price than to the biofuels prices. However, it should be pointed out that the price of biofuels in the international market depends on the oil price as well. In any case, the effect of producing bioethanol and biodiesel from agricultural resources on food and feed prices cannot be neglected and should be thoroughly assessed. In this regard, the production of the so-called second-gener­ation biofuels represents an important option for producing biofuels from sources other than those related to food and feed production. In this case, bioethanol can be produced from lignocellulosic residues or by-products, such as cane bagasse, corn stover, or wheat straw, that have no influence on food production structure.

Considering the effect of bioethanol production on the environment, some con­cerns have been expressed based on the fact that ethanol usage in gasoline blends increases the aldehyde level, mostly acetaldehyde, compared to the combustion

of conventional gasoline. The aldehydes are formed during the incomplete com­bustion of ethanol and have been linked to some potentially harmful effects on human health. Nonetheless, it should be emphasized that all oxygenates form higher amounts of aldehyde emission than nonoxygenated gasoline. Furthermore, the effect on the health is negligible as proven by the Royal Society of Canada tak­ing into account, in addition, that the catalytic convertors of new vehicles reduce these aldehyde levels to a higher degree (Canadian Renewable Fuels Association, 2000). In the case of old automobiles that do not have this kind of convertor, the level of formed aldehydes, although increased when ethanol is employed as the gasoline oxygenate, is always below the permissible limits. Actually, the emission of this type of organic compound is very low compared to other types of danger­ous emissions (e. g., aromatic hydrocarbons), which are effectively reduced when fuel ethanol is used (see Table 1.2).

There exists a great debate on the environmental suitability of ethanol usage as an oxygenate, especially when blends with low ethanol contents are employed. Reviewing various literature sources, Niven (2005) points out that gasoline blends with 10% content of ethanol (E10) offer few advantages in terms of greenhouse gas emissions, energy efficiency, or environmental sustainability. In addition, this author indicates that E10 blends increase both the risk and severity of soil and groundwater contamination, although greenhouse gas benefits for 85% etha­nol blends (E85) are recognized. By contrast, the Argonne National Laboratory (USA) estimates that an 8 to 10% reduction in greenhouse gas emissions per vehicle mile traveled is achieved when biomass ethanol is used in E10 blends and 68 to 91% reduction when used in E85 blends (Wang et al., 1999). This contro­versy has arisen in countries with an important ethanol industry, as in the case of the United States. Authors such as Pimentel (2003) have maintained for several years that the energy required for producing ethanol is greater than the energy contained in the ethanol itself, particularly when starchy materials like corn are used. This implies that important natural resources of a national economy are being squandered by maintaining an artificial biofuels program. Nevertheless, most studies have concluded that the energy invested in ethanol production is less than its energy content, which allows achieving significant environmental ben­efits. These studies have been accomplished by both independent research groups and governmental centers and have belied Pimentel’s arguments, as shown in the works of Shapouri et al. (2003) and Wang et al. (1999).

Corn stover is composed of stems, leaves, and cobs resulting from the corn har­vest. This lignocellulosic material is considered as one of the most promising feedstocks for ethanol production in the United States since it is the most abun­dant agricultural residue in that country. For instance, the corn was the second most cultivated crop in the United States in 2005 (29.8 million ha) after soybeans (29.9 million ha) and followed by wheat (20.2 million ha) and cotton (5.3 million ha; FAO, 2007a). However, in terms of product harvested, corn widely outpaces the soybean (299.9 million ton of corn versus 85.0 million ton of soybeans). In addition, the generation of residues in the case of soybeans is modest com­pared to corn. Corn stover availabilities have been estimated at 153 million for per year (dry basis) in the United States relative to other agricultural residues (about 58 million ton) and demonstrates the huge volumes generated (Kadam and McMillan, 2003).

A factor to be considered in the case of agricultural residues is that, unlike cane bagasse, their total utilization can lead to soil erosion and reduction of its organic matter content. For this, the sustainable fraction of residues to be collected should be defined. This fraction depends on the climate conditions, crop rotation, soil fertility, land scope, and employed agronomic practices. For example, it is estimated that it is necessary to cover the soil surface after har­vesting with more than 30% of harvest residues to avoid the erosion due to water runoff. For this reason and to evaluate the availability of feedstocks for ethanol production, higher percentages are used to consider the uncertainty generated by different local conditions (Kim and Dale, 2004). Taking into account this factor, the amount of corn stover that can be collected in a sustainable way in the United States has been estimated at 80 to 100 million ton (dry basis) per year. Only a small portion of this amount (about 20 million ton) has the potential to be used for purposes other than ethanol production, such as, for instance, the production of agglomerates, pulp, or furfural. In this way, the production of 11 million L of fuel ethanol will imply the utilization of only 40% of collectable corn stover (Kadam and McMillan, 2003). The global potential for ethanol production from this material reaches 72.45 million L. Undoubtedly, the implementation of a pro­gram for fuel ethanol production from corn stover would reduce the pressure over corn prices.

The fermentation step is central in the overall fuel ethanol production process since it represents the actual transformation of the conditioned and pretreated raw materials into the main product, ethyl alcohol, using bioagents such as yeast or other ethanol-producing microorganism. Ethanolic fermentation is one of the most studied biological processes. Nevertheless, the need of increasing the effi­ciency of ethanol production including the usage of alternative feedstocks has led to the development of new fermentation methods with better technoeconomic and environmental indicators.

Traditionally, the most used microorganism for ethanolic fermentation is the yeast Saccharomyces cerevisiae. This is valid for practically every one of the main types of feedstocks employed for ethanol production: sucrose-based media, starchy materials, and even lignocellulosic materials. However, in the last case, there is a wider variety of process microorganisms employed (e. g., Zymomonas bacteria, xylose-assimilating yeasts, or thermophilic clostridia).

SSF technology born in the 1970s was assimilated by the starch-processing industry for ethanol production obtaining high and sustainable yields on the

order of 0.410 L/kg of corn (Madson and Monceaux, 1995). In the case of the saccharification step when starch is used as feedstock, glucoamylase experiences the inhibitory effect caused by glucose released as the hydrolysis of this bio­polymer advancement. This effect is more pronounced at high conversions of starch into ethanol. In contrast, integration by SSF makes it possible for yeasts to consume the glucose immediately as it forms under the action of the amylases on starch. In addition, the risk of bacterial contamination of the wort is dras­tically reduced because of the low level of glucose in the medium during the SSF process. The elimination of the external step of saccharification, the major source of infection by bacteria, also contributes to the reduction of contamination (Madson and Monceaux, 1995). In the same way, capital costs are reduced as a consequence of the increase in the compactness of the system (fewer numbers of units). Moreover, low glucose concentrations in the medium decrease the osmotic pressure over the yeasts because the use of concentrated solutions is avoided (Bothast and Schlicher, 2005). Energy costs can also be reduced considering that the SSF process is operated at temperatures less than those of the separate sac­charification process; this implies the reduction in the steam consumption. All these synergic features have allowed gains of ethanol yields higher than those of the SHF process.

The main disadvantage of the SSF process is that the optimum temperature of glucoamylase (65°C) does not coincide with the optimum temperature for yeast growth (30°C). Fortunately, starch saccharification can be carried out at 30 to 35°C although at a slower rate. For this reason, higher enzyme dosages are required. Finally, processing times for batch SSF are longer than the correspond­ing times for batch SHF.

Most ethanol production facilities utilizing the corn dry-milling technology employ batch SSF processes. The duration of this process is 48 to 72 h achiev­ing final ethanol concentrations in the medium of 10 to 12% by volume (Bothast and Schlicher, 2005). A number of modifications of the SSF of starchy materials have been proposed in order to decrease the production costs. Some of them are included in Table 9.1. Montesinos and Navarro (2000) have studied the possibility of utilizing raw wheat flour during the batch SSF process with the aim of reducing costs attaining a decrease in the process time. On the other hand, the SSF per­formed at a temperature above 34°C using a thermotolerant yeast, which enabled the reduction of cooling requirements and the improvement of the conversion process, as claimed in the patent of Otto and Escovar-Kousen (2004).

With the aim of obtaining valuable information on the technoeconomic and envi­ronmental performance of ethanol production from sugarcane in a stand-alone facility under Colombian conditions, a characteristic technological configuration for bioethanol production was simulated in previous works (Cardona et al., 2005b; Quintero et al., 2008). For this, the process was analyzed considering five main pro­cessing steps: raw material conditioning, fermentation, separation and dehydration, effluent treatment, and co-generation. In the simulated process that is depicted in Figure 11.2, the feedstock is washed, crushed, and milled to extract the sugarcane juice and produce bagasse. The cane juice is sent to a clarification process, where pH is adjusted, some impurities are removed, and the press mud is generated. This material is the filter cake obtained during the removal of suspended solids in the rotary drum filter employed for juice clarification. The press mud is commercial­ized as a component of animal feed or for composting. The cane juice is sterilized and directed to the fermentation stage. Using the yeast S. cerevisiae, which is con­tinuously separated by centrifugation and recycled back to the fermenter, performs the fermentation. Fermentation gases, mostly CO2, are washed in an absorption column to recover more than 98% of the volatilized ethanol from the fermenter,

and sent to the first distillation column. The culture broth containing 8 to 11%

(by weight) ethanol is recovered in a separation step consisting of two distillation columns. In the first (concentration) column, aqueous solutions of ethanol are con­centrated up to 63%. In the second (rectification) column, the concentration of the ethanolic stream reaches a composition near the azeotrope (95.6%). The dehydra­tion of this ethanol is achieved through adsorption in vapor phase with molecular sieves by the PSA technology (see Chapter 8, Section 8.2.5). The stream obtained during the regeneration of molecular sieves containing 70% ethanol is recycled to the rectification column.

The stillage treatment consists of an evaporation step allowing the generation of a marketable by-product employed as a fertilizer of cane plantations. If the stillage is not concentrated or evaporated at a low degree, it can be used for both irriga­tion and fertilization of sugarcane plantations surrounding the ethanol production facility. Hence, the environmental impact of the whole process is reduced since the most important liquid effluent is converted into a value-added product. Condensed water from evaporators and bottoms from the rectification column are collected and sent to the wastewater treatment step. Part of this water can be used as feed water for the co-generation system. Currently, the bagasse obtained is employed in sugar
mills and cane-based distilleries for combined generation of the steam and power required by the process. For this, co-generation units have to be installed. These units basically comprise a burner (combustor) for combustion of solid bagasse, a boiler where the feed water is converted into steam, and a turbogenerator (steam turbine), where exhausted steam for the process is obtained along with power. The electricity surplus not consumed by the plant can be sold to the energy network.

The simulation of this process was carried out employing Aspen Plus®. Main input data employed for process simulation are shown in Table 11.1. The simula­tion considered 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. The economic analysis was performed using the Aspen Icarus Process Evaluator® (Aspen Technology, Inc., Burlington, MA, USA) pack­age. This analysis was estimated in US dollars for a 10-year period at an annual interest rate of 16.02% (typical for the Colombian economy), using the straight­line depreciation method and a 33% income tax. The above mentioned software estimates the capital costs of process units as well as the operating costs, among other valuable data, employing the mass and energy balance information provided by Aspen Plus. In addition, specific information regarding the local conditions was used for the economic analysis in the framework of the package utilized. In this way, the net present value (NPV) of the process was determined.

Some simulation results of main streams for the process studied are shown in Table 11.2. The compositions of the streams calculated by simulation, agree very well with those reported for commercial processes. The moisture and fiber con­tents of bagasse and press mud are close to the contents of moisture (bagasse: 50%, press mud: 75%) and fiber (bagasse: 46%, press mud: 13%) previously reported for these co-products (ETPI, 2003; Moreira, 2000). The value of generated cane stillage per liter of ethanol obtained from simulation (11.01 L/L EtOH) is within the range reported by Wilkie et al. (2000) from experimental data (10 to 20 L/L EtOH). The stillage composition calculated by simulation is close to the stillage composition of Brazilian distilleries, as cited by Sheehan and Greenfield (1980). For instance, the content of organic matter in nonconcentrated stillage is calculated at 26 g/L, while the corresponding average values in Brazilian distilleries using cane juice and cane molasses are 19.5 g/L and 63.4 g/L, respectively. In general, streams data determined through simulation for this processes were compared to available data of existing production facilities taken from literature and personal communications. Hence, the simulation results were satisfactorily validated.

The results obtained for ethanol yield in the process analyzed, along with total operating and capital costs are shown in Table 11.3. For sugarcane in the case of the most productive zone in Colombia (the Cauca River valley), this value is 123 ton/ha for a harvesting time of 13 months (CENICANA, 2003). The average yield for all the country, including nontechnified cane crops, reaches 92.7 ton/ha which can be compared to the average yield of sugarcane in Brazil (73.91 ton/ha) and India (59.05 ton/ha; FAO, 2007), the major sugar producers in the world. The calculated ethanol production cost (Table 11.4) is higher than the average production cost of Brazilian ethanol (US$0.198/L in 2007; Xavier, 2007). The price of Brazilian hydrous etha­nol could be even lower—about US$0.150/L (Macedo and Nogueira, 2005). This could be explained by the lower cost of the sugarcane in Brazil (about US$0.010/kg in some producing states). As with Brazil, the high productivity of sugarcane, the advantageous output/input energy ratio of the cane-to-ethanol process compared to

Main Process Data for Simulation of Fuel Ethanol Production

Main Process Data for Simulation of Fuel Ethanol Production

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 11.2

FIow Rates and Composition of some streams for sugarcane-Based Ethanol Process

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.

TABLE 11.3

Ethanol Yields and Total Capital and Operating Costs for Fuel Ethanol Production from Two Feedstocks

Item sugarcane Corn

Ethanol yield (L/ton of feedstock) 77.19 446.51

Ethanol yield (L/[ha*year]) 8,764.00 6,698.00

Total capital costs (thous. US$) 75,613.00a 36,447.50

Total operating costs (thous. US$/year) 36,255.20 70,670.30

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 Includes the cost of the co-generation unit.

table 11.4

unit Costs of fuel Ethanol (us$/L of Anhydrous Ethanol)

corn or lignocellulosic biomass, and the low cost of labor force, among other fac­tors, makes this feedstock the more viable option for new ethanol production facili­ties. The commercialization of the co-products (e. g., press mud and concentrated stillage) allows a substantial economic balance improvement. The data presented in Table 11.5 shows a confirmation of the economic viability of this process.

One of the features of the simulation presented above is the inclusion of the co­generation unit. The simulation of this unit allows performing a more complete envi­ronmental evaluation of the overall technological configuration. The co-generation step employs the combustion of cane bagasse to cover the needs of both thermal and electric energy required by the whole ethanol production facility. In the following case study, the specific aspects of the co-generation simulation are presented.

TABLE 11.5

Some Economic Indicators of Two Processes for Fuel Ethanol Production using Different feedstocks

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.

Possible competition between bioenergy and food is so complex that many stud­ies, depending on the context (or purposes of the study), will be contradictory. But, many countries (consumers) are dependent on fossil fuels and have to contend with their unstable prices. The negative energy balance has become a problem­atic issue, and fuel ethanol for automotive transport, for example, represents the unique stable alternative. Additionally, environmental improvements are reached when fuel ethanol is blended with gasoline. Other countries can find in biofuels like ethanol the beginning of an economic development for rural areas.

The main problem we have today in this important discussion is the existence of a large quantity of speculative information about biofuels and food security. Most of this information is incorrectly used for political and economical pur­poses. The real regulator of land use for food or biofuels is the market. Displacing gasoline demand in the coming years will require the combined development of second-generation technologies and large-scale international trade in ethanol fuel. Without second-generation technologies, large-scale production of ethanol, especially from sugarcane, in developing countries will increase and along with

TABLE 13.2 Research Trends and Priorities for improving Fuel Ethanol Production by Means of Process Engineering
Issue Am feedstocks Process synthesis Process synthesis by different approaches (e. g., optimization — based process synthesis)	sugarcane Integration of ethanol production from sugarcane with facilities for ethanol production from cane bagasse	starchy Materials Integration of corn dry-milling plants with facilities for ethanol production from corn fiber	Ugnocellulosic Biomass Precise assessment of available biomass based on access and composition Process flowsheet development considering different pretreatment methods Full pilot-plant process analysis, especially for continuous processes
Process analysis Improvement of process control and operation (e. g., modeling, nonlinear analysis) Improvement of simulation and optimization tools (e. g., optimization under uncertainty, metabolic-flux models)
Continued

TABLE 13.2 (Continued) Research Trends and Priorities for improving Fuel Ethanol Production by Means of Process Engineering
Issue Process integration	Am feedstocks Integration of fermentation and separation processes for reduction of product inhibition Application of membrane technology (e. g., for ethanol removal or dehydration) Energy integration (e. g., by pinch technology)	sugarcane Development of efficient co-generation technologies using cane bagasse	starchy Materials Development of consolidated bioprocessing (CBP) Recombinant microorganisms for conversion of starch into ethanol	Ugnocellulosic Biomass Increase of effectiveness of SSF and SSCF processes (e. g., by improvement of cellulase activity) Development of CBP Increase of ethanol tolerance in microorganisms converting cellulose into ethanol Development of recombinant microorganisms for CBP Develop. of efficient co-generation technology using solid residues of the process (as BIG/CC)
Improvement of environmental performance considering the whole life cycle of bioethanol Production of valuable co-products (retrofit to biorefineries) Integration with petrochemical industry (e. g., ETBE prdn.) and biofuels production (e. g., biodiesel)
Other process engineering issues

this, new problems related to food security will appear. In order to avoid a non­equilibrium development of a fuel ethanol market based on energy crops, drastic but fair regulations from governments must be instituted.

Each country has to create appropriate rules and laws for developing fuel ethanol programs based on its specific supply and demand characteristics. Additionally, the overall energy policies that include bioenergy should be care­fully designed to include all possible energy sources in harmonized and balanced form. It is not logical, for example, that renewable energies coming from the sun and wind are more developed in countries having limited access to these sources than in tropical countries.

With purposes of an open discussion about the future of food security concerns on fuel ethanol production, Table 13.3 presents an overview of the perspectives, challenges, and risks of different topics involved in this issue.

One of the main challenges in the optimization-based synthesis of technological schemes is the definition of the superstructure of alternatives that contains the best solution. Most of the works reported have been based on the hand representa­tion of the superstructure for each particular problem without following general rules. To generalize this procedure, the definition of the representation type for the superstructure is needed. Among the main types of representation proposed are the state-task network and the state-equipment network. In the first type, two classes of nodes (states and tasks) are used for the representation; the assignment of equipment is dealt implicitly through the model. In the second type of repre­sentation, the states and the equipments are employed as the nodes; the tasks in this case are treated implicitly through the model (Grossmann et al., 2000).

Other crucial aspect during optimization of superstructures consists in how to generate in a systematic way the superstructure so that all the alternatives of interest are included. One of the trends in this field is the automatic generation of superstructures, which has been developed for the case of lineal process net­works employing an algorithm based on graphs and ensuring a search space large enough to include the optimal solution (Friedler et al., 1993). Fraga (1998) and Fraga et al. (2000) have developed the Jacaranda system that is able to solve a process synthesis problem through the automatic generation of the superstructure at the same time that it executes the search procedure. The Jacaranda system is based on the use of an implicit enumeration procedure that generates and ana­lyzes a directed graph representing the synthesis problem.

The level of detail of the optimization model also plays an important role for the optimization-based approach. In general, the models can be classified into three main classes: aggregated models, short-cut models, and rigorous models (Grossmann et al., 2000). The first class of models employs high-level representa­tions in which the synthesis problem is simplified by one aspect or objective that tends to dominate the problem as mentioned above. Models for predicting the minimum utilities and the minimum amount of units in a heat exchange and mass exchange networks belong in this category. Short-cut models are employed in superstructures with a high level of detail and involve the optimization of capital and operating costs, but in which the performance of individual units is predicted with relatively simple nonlinear models in order to reduce the computational effort. Among the examples of this type, distillation sequences and technological schemes can be highlighted. Finally, rigorous models are also applied in detailed superstructures for predicting the behavior of the units as in the case of the syn­thesis of distillation trains for separation of ideal and nonideal mixtures.