In the case of lignocellulosic biomass, a very promising integrated configuration for bioethanol production is the inclusion of pentose fermentation in the SSF, as illustrated in Figure 9.6. This process is known as simultaneous saccharifica­tion and co-fermentation (SSCF). This configuration implies a higher degree of intensification through its reaction-reaction integration. In this case, the hydro­lysis of cellulose, the fermentation of glucose released, and the fermentation of pentoses present in the feed stream is simultaneously accomplished as a single unit. Besides the effectiveness of employed cellulases, the key factor in SSCF is the utilization of an efficient ethanol-producing microorganism with the ability of assimilating not only hexoses (mainly glucose), but also pentoses (mainly xylose) released during the pretreatment step as a result of the hemicellulose hydrolysis. In nature, there exist several microorganisms able to assimilate both types of sug­ars, but their ethanol yields are low as is their growth rate. Therefore, genetically modified microorganisms have been developed and successfully proven in SSCF processes for ethanol production from lignocellulosic materials.

Lignocellulosic

— Ethanol

In an initial stage, the co-fermentation of mixed cultures was studied (Cardona and Sanchez, 2007). For example, the co-culture of Pichia stipitis and Brettanomyces clausennii has been employed for the SSCF of aspen at 38°C and pH of 4.8 yielding 369 L EtOH per ton of aspen during 48 h batch process, as reported by Olsson and Hahn-Hagerdal (1996). In this configuration, it is neces­sary that both fermenting microorganisms have compatible in terms of operating pH and temperature. Chandrakant and Bisaria (1998) suggest that a combination of C. shehatae and S. cerevisiae is suitable for this kind of process.

The actual SSCF process has been demonstrated in the case of ethanol produc­tion from yellow poplar through a bench-scale integrated process that included the dilute-acid pretreatment of feedstock, conditioning of hydrolyzate for fermen­tation, and a batch SSCF (McMillan et al., 1999). In this case, the recombinant bacterium Zymomonas mobilis assimilating xylose was used. SSCF is the process on which is based the technology designed as a model process by the National Renewable Energy Laboratory (NREL) for production of fuel ethanol from aspen wood chips (Wooley et al., 1999b) and corn stover (Aden et al., 2002). In this design, the utilization of recombinant Z. mobilis exhibiting a glucose conversion to ethanol of 92% and a xylose conversion to ethanol of 85% is proposed. It is projected that SSCF can be carried out in a continuous regime with a residence time for the entire system of cascade fermenters of 7 d at 30°C (Cardona and Sanchez, 2007).

As in the case of SSF of biomass, the development of microbial strains able to grow at elevated temperatures can improve the technoeconomic indicators of the process (Cardona and Sanchez, 2007). Thus, ethanol-producing microorganisms capable of assimilating both types of sugars at temperatures higher than 50°C could reduce the cellulase costs by half, taking into account that a 20°C increase during saccharification can lead to double cellulose hydrolysis rate (Wooley et al., 1999a). Three examples of SSCF of lignocellulosic materials pretreated are presented in Table 9.4.

Using the simulation results from the last case study, the environmental assessment of bioethanol production from dry-milled corn employing the SSF process was car­ried out applying the WAR algorithm (Quintero et al., 2008). The results obtained were compared to the outcomes corresponding to the process for ethanol produc­tion from sugarcane.

The total output rate of environmental impact for corn ethanol process is shown in Figure 11.3 and the potential environmental impact generated within the sys­tem is shown in Figure 11.4. It is evident that ethanol production from sugarcane has also lower impact on the environment compared to the corn process: the latter process exhibits a higher PEI per mass of products. This environmental indicator is the index to be considered for the overall evaluation of both processes. On the other hand, the corn-based process has a more negative generated PEI meaning that the PEI of the substances entering the system is reduced by their transforma­tion into other less dangerous compounds. For the sugarcane process, the higher value of generated PEI (although negative) indicates that the conversion of entering substances also occurs, but to a lesser degree. This feature can be related to the fact that this process requires the input of a greater amount of feedstock. The commer­cialization of DDGS implies the elimination of the polluting stillage stream.

The four case studies presented above demonstrate the power of the simula­tion approach employed. In order to obtain a general framework for comparing different technological configurations for ethanol production from diverse feed­stocks according to technoeconomic and environmental criteria, the formulation of an overall comparison criterion is required. This is shown in the following case study.

Case Study 11.5 Combined Evaluation of Technoeconomic and Environmental Performance of Two Bioethanol Production Processes

The evaluation considering economic and environmental indexes of the two simu­lated processes for ethanol production from sugarcane and corn under Colombian conditions was carried out to obtain a combined index useful for the selection of the most appropriate technology as formulated in previous works (Quintero et al., 2008; Sanchez, 2008). This combination was done by following the procedure developed by Chen et al. (2002) where the economic and environmental objec­tives were aggregated into a single objective function using the analytic hierar­chy process (AHP) approach. The AHP is one variant of multicriteria analysis that uses a number of pair-wise comparisons between quantitative or qualitative crite­ria to assess the relative importance of each criterion. These comparisons can be arranged in a hierarchical manner to form sets of attributes, and qualities (levels) within these attributes (Hussain et al., 2006).

The hierarchical structure for this case study is shown in Figure 11.9. Once mass and energy balances have been calculated by simulation, the economic and environmental evaluations are performed by using the corresponding tools (process

evaluation software and WAR algorithm, respectively). The indexes (NPV and PEI) for each process are determined from these evaluations. Alternatively, other eco­nomic indexes, such as IRR, can be used. The indexes are normalized, so that they do not exceed a normalization value, and converted to quantitative scores. The normalization value for each index was calculated as the sum of the index values in both processes. The economic score of a given process was determined as the ratio between the NPV of the process and the corresponding normalization value, i. e., the sum of the two calculated NPVs. The environmental score was calculated tak­ing the difference between the corresponding normalization value and the PEI of the process and dividing the result obtained by the normalization value. The AHP score of a process design represents the sum of the products of the average process score for a given attribute and the weighting for that attribute, that is:

AHP = PEcn ■ weight + PEnv ■ weight (11.1)

where PEcn is the normalized economic score calculated from the NPV of the two analyzed processes, and PEnv is the normalized environmental score resulting from the PEI values of the two processes. The qualitative weightings of economic and environmental attributes were taken as 0.82 and 0.18, respectively. These values are suggested by Chen et al. (2002) who applied them to several chemical processes based on the survey carried out by Dechanpanya (1998) on the comparison of eco­nomic and environmental attributes for a chemical process from several faculty members and graduate students at Michigan Technological University using the AHP approach (Hussain et al. 2006).

The results obtained from the integration of the economic and environmental assessments following the proposed methodological approach are presented in Table 11.9. From these results, the sugarcane-based process exhibits a higher AHP score, indicating that it has better performance than the corn-based process when both economic and environmental criteria are analyzed in a combined manner. Changes in the evaluation of the AHP when different weightings are selected for both processes show that the sugarcane-based process will always have a better

performance with any value of the economic weighting. When this weighting is increased (this is equivalent to the reduction of the environmental weighting), the AHP score will also increase. In contrast, the corn-based process shows slightly worse performance when the economic attribute has a higher weight. This means that the economic advantages for the sugarcane process are actually better than those displayed for the corn scenario. Therefore, assigning weightings for this case study does not affect the final qualitative result of the combined evaluation within the AHP framework. Thus, the process that employs sugarcane for producing fuel ethanol shows better performance.

The procedure proposed to analyze different flowsheet configurations proved to be a useful methodology for process synthesis and can support the decision making for further experimental studies at pilot scale and industrial levels. This is a key issue considering the limited resources for extensive and long-term research in such countries as Colombia.

The authors wish to acknowledge the following institutions for their support and collaboration:

• Pilot Plants of Biotechnology and Agro-Industry, National University of Colombia at Manizales, Colombia, for providing scientific and labora­tory resources during research projects that supported part of the knowl­edge presented in this book.

• Research Direction of National University of Colombia at Manizales (DIMA), Colombia, for funding different research projects in fuel etha­nol production.

• Colombian Institute for Science and Technology Development (Colciencias), Colombia, for granting fellowships and research projects related to the biofuels area.

• Moscow State Academy of Fine Chemical Technology M. V. Lomonosov, Moscow, Russian Federation, for providing the authors with science and technology bases in biotechnology, process design, and integration approach.

• Institute of Agricultural Biotechnology and Department of Engineering, University of Caldas, Manizales, Colombia, for financial and scientific support in fuel ethanol production projects.

• Government of the Department of Caldas, Colombia, for financial sup­port of biofuels projects.

• Department of Chemical Engineering, University College London, United Kingdom, for scientific and computational support in process systems engineering.

• Korea Institute of Energy Research, South Korea, for scientific collabo­ration in biofuels research and development.

In addition, the authors wish to acknowledge Annie Ceron, M. Sc., for her assistance and support during the preparation of the manuscript.

Sugarcane is the raw material for producing sugar. The sugar is one of the most important commodities in the world market. Besides cane, sugar can be obtained from the sugar beet. The sugar is extracted from these two feedstocks in the form of sucrose crystals (Figure 3.1) with high purity. The annual worldwide production of sugar was 163.3 million ton (raw value) in 2007, according to the International Sugar Organization (ISO, 2008). The world sugar consumption, in turn, was 157.7 million ton (raw value) in the same year. Main producers of sugar are Brazil, India, the European Union (EU), and China (Table 3.3). Sugar is mostly produced

FIGURE 3.1 Chemical structure of sucrose.

from sugar beets in the case of the EU countries. Brazil is the main actor in the world sugar market (first producer, third consumer, and first exporter), thus, any change in the production in this country, due to either changes in climate or the balance of ethanol production related to sugar, provokes changes in the inter­national sugar price. This situation will continue in the future considering that Brazil is expanding the area dedicated to sugarcane plantations. It is expected that about 40 and 50 new sugar mills will be put into operation by 2010 (Decision News Media SAS, 2006).

Mature cane is collected manually or mechanically. Hand cutting is the most usual method. The cane is transported to the sugar mills where the sucrose is extracted and crystallized. The first step in the production of cane sugar in mills consists in the cane juice extraction (Figure 3.2). For this, the cane is sampled, washed, weighed, and prepared using rotating knives to shred the stalks into pieces. Then, the shredded cane is ground in heavy-duty roller mills, which extract the raw juice while hot water is sprayed onto the cane bed to dissolve the sugars. The sucrose extraction yield reaches 90 to 95% in modern sugar mills. The fiber fraction of the cane, called bagasse, is generated in this step. This material is pre­dried and utilized in the same sugar mill as an energy source burning it in special

boilers to produce steam that generates electricity through turbo generators. The raw juice is clarified by adding sulfur dioxide (SO2) to oxidize color substances, destroy microorganisms, and favor the agglutination of colloids. Then, lime is added to neutralize the juice, thus avoiding, in this way, the sucrose hydrolysis into glucose and fructose (sucrose inversion). The limed juice is heated to 105 to 110°C in the clarifiers, to which flocculants also are added. Solid separation is favored because the calcium sulfite formed is insoluble and precipitates removing the suspended particles and other impurities. This precipitate (known as mud) is mixed with flocculants, lime, and small amounts of the light fraction of bagasse, separated from the juice, and sent to vacuum rotary filters where the filter cake is recovered. The filter cake (press mud) is a fibrous material that can be used for animal feed or composted to produce manure. Part of the clarified juice can be used for ethanol production. In the evaporation step, the clarified juice is con­centrated by removing up to 75% of the water in multiple-effect evaporators. The removed water is used in other process steps either as steam or as condensate. The concentrated juice or syrup contains 58 to 62% solids (about 60° Brix).

The obtained syrup is treated with direct steam to reduce its viscosity. Then, the syrup is clarified by adding SO2, phosphoric acid, and lime. After this treat­ment, the syrup is sent to a flotation tank where it is clarified. This clarified syrup can be used for ethanol production. In the next step or pan stage, the sucrose con­tained in the syrup is crystallized by removing excess water with vacuum evapo­rators called pans. In order to increase the speed of this process, the pan stage operates in a manner that utilizes seed crystals and a combination of products
with varying levels of sugar content to produce a range of crystal sizes and, hence, qualities. The crystals are separated from the mother liquor in centrifuges. After three evaporation/centrifugation stages, crystals of raw sugar and the residual mother liquor (called C-molasses) are obtained. The molasses is one of the most employed feedstocks, not only for ethanol production, but also for producing a wide range of microbiological products. The sucrose crystals are dried in rotary drum driers where the commercial raw sugar is obtained. To produce refined sugar, the raw sugar is redissolved in clean water and treated with phosphoric acid and saccharate to remove the remaining impurities. The clarified solution is treated with activated carbon to remove colors and then filtered. Finally, this mass is evaporated and crystallized in vacuum pans. The refined crystals are washed with steam and hot water, air-dried, classified according to crystal size, and stored.

The yeast Saccharomyces cerevisiae and the bacterium Zymomonas mobilis are not able to directly utilize the cellulose for ethanol production. In general, these microorganisms have the highest probability to be used in an industrial process for conversion of lignocellulosic biomass into fuel ethanol. For this reason, a step for cellulose hydrolysis (saccharification) to obtain a fermentable solution of glucose is required in an analogous way to the starch saccharification. As in the case of starch, the cellulose can be hydrolyzed with the help of acids either dilute or concentrated. If dilute acids are used, temperatures of 200 to 240°C at 1.5% acid concentrations are needed in order to hydrolyze the crystalline cellulose, but the degradation of glucose into hydroxymethylfurfural (HMF) and other nonde­sired products is unavoidable under these conditions. In a similar way, xylose is degraded into furfural and other compounds (Wyman, 1994). H2SO4 and HCl have been historically used for these purposes (Jones and Semrau, 1984; Song and Lee, 1984). As mentioned in Chapter 4, the pretreatment and cellulose hydrolysis of lignocellulosic biomass can be carried out in two stages using dilute acids. Thus, a first stage under mild conditions (190°C, 0.7% acid, 3 min) is carried out to recover pentoses, while the remaining solids undergo harsher conditions (215°C, 0.4% acid, 3 min) to recover hexoses from cellulose hydrolysis in the second stage. In this way, 50% glucose yield is obtained (Hamelinck et al., 2005). One variant of the acid hydrolysis is the employment of extremely low acid and high tempera­ture conditions during batch processes (autohydrolysis approach) that have been applied to sawdust (Ojumu and Ogunkunle, 2005; Sanchez and Cardona, 2008). The degradation of cellulose with near-critical water has been proposed, although the obtained glucose yield is low (40%) and the fermentability of resulting hydro — lyzate is limited due to the formation of unknown inhibitors (Sakaki et al., 1996).

The hydrolysis of cellulose with concentrated acids allows achieving glucose yields near 90%, but in this case, the recovery of the acid is a key factor in the process economy (Hamelinck et al., 2005). Several configurations for separation of formed glucose and recovery of employed acid have been proposed. Among the early procedures suggested, the lime addition, ionic exclusion columns based on commercial resins (Neuman et al., 1987), or electrodialysis (Baltz et al., 1982) can be highlighted. Concentrated acid processes using 30 to 70% H2SO4 have a higher glucose yield (90%) and are relatively fast (10 to 12 h), although the amount of acid used is a critical economic factor. By continuous ionic exchange, it is pos­sible to recover over 97% of the acid (Hamelinck et al., 2005). In general, the acid hydrolysis of cellulose implies high energy costs and the construction of reactors resistant to the corrosion, which increase the capital costs.

To describe the oscillatory behavior of continuous ethanolic fermentation using yeasts or bacteria, the mathematical model employed should contain the necessary expressions showing the variation of the state variables with time. Some models have been proposed with this aim and contrasted with the experimental data in order to validate their suitability. Among these models, the mathematical descrip­tion of Jarz^bski (1992) should be highlighted. This model is based on stochastic assumptions taking into account the distribution of cell population and corresponds to the continuous fermentation in a stirred tank using the yeast Saccharomyces cerevisiae. This model divides the cell biomass into three groups: viable biomass (cells able to reproduce and biosynthesize ethanol), nonviable biomass (cells able to biosynthesize ethanol, but not to reproduce), and dead biomass (cells able nei­ther to reproduce nor biosynthesize ethanol). Moreover, the kinetic model includes terms describing the maintenance of viable and nonviable cells. The results derived from this model were compared to the experimental data obtained by Perego et al. (1985). The equations of the model are as follows:

rate (in h-1), p is the specific growth rate (in h-1). The subindexes v, nv, and d refer

to viable, nonviable, and dead cells, respectively. The biomass growth (death) rate expressions are as follows:

(7.9)

where pmax, K1, K2 , and Pc are kinetic constants, which can be found in the original work of Jarz^bski (1992).

In a previous work (Restrepo et al., 2007), the simulation of the continuous system using the available information and parameters reported by Jarz^bski (1992) was performed, but the results were not satisfactory because there was no appropriate correspondence with the experimental data reported by Perego et al. (1985) for continuous fermentation using sugarcane molasses as a feedstock. Using the software Matlab™ (Mathworks, Inc., USA), the curves corresponding to the dynamic behavior of such fermentation system were obtained (Figure 7.11). Nondynamic analysis (bifurcation analysis) is a powerful tool to study the oscil­latory behavior of continuous ethanolic fermentations. This tool was used in the previous mentioned work in order to assess whether the model can describe the dynamic behavior of the system. For the analysis of the fermentation system stud­ied, the software MatCont developed in the University of Gent (Belgium) was employed as a toolbox in Matlab package. The results of bifurcation analysis for the Jarz^bski’s model are shown in Figure 7.12. From this diagram in the zone hav­ing a physical sense (positive dilution rates), it can be observed that the dynamic system behaves in an ordinary way and presents no sustained oscillations (Hopf bifurcations). In the zone of negative dilution rates, limit point (LP or node-saddle bifurcation) and Hopf bifurcation are present indicating that the model does have the possibility to represent the oscillations.

For representing the sustained oscillations and the instability of the contin­uous fermentation, the Jarz^bski’s model was modified and adjusted in such a way that the model represents not only the oscillatory fermentation reported by Perego, Jr. et al. (1985) appropriately, but also the data for continuous nonoscil­latory fermentation obtained by these same authors. The modification consisted in the simplification of the nonviable biomass growth rate (pnv) considering that the inhibition of cell biomass is mainly due to the high ethanol concentration. In addition, the expression describing the biomass death rate (pnv) was changed in such a way that this rate was proportional to a fraction d of the viable biomass growth rate (pnv). Therefore, the model modified comprises the system (7.8) and the following rate

Once the model was modified, an optimization applying a simple multiobjec­tive strategy was performed with the aim of adjusting the model parameters by minimizing the nonlinear least squares. Using the Nelder-Mead algorithm, a point is searched without calculating the derivatives. This facilitates the formulation of the two objective functions used (one for adjusting the biomass concentration, and another one for adjusting the substrate concentration). These functions numerically integrate the system using the numeric differentiation formulae (NDF) and an algo­rithm for controlling the integration step size with a defined absolute tolerance. Then, the experimental data reported for both steady-state fermentation and oscil­latory fermentation were employed to calculate the sum of the squares of the residu­als, which were subsequently minimized by using the mentioned algorithm. The outcomes obtained are presented in Figure 7.13. As can be observed, the adjust­ment of the model to the experimental data was appropriate and allowed describing the behavior of the two types of fermentations studied. Unlike the nonmodified Jarz^bski model, all the concentrations had physical sense. In the previous work reported (Restrepo et al., 2007), the values of the kinetic parameters were calcu­lated. These values are presented in Table 7.5.

D (1/h)

D (1/h)

FIGuRE 7.14 Response diagram of the continuous ethanolic fermentation process for viable cell biomass in dependence on the dilution rate D.

When the bifurcation analysis has been performed based on the modified model, a Hopf bifurcation can be noted in the response diagram near D = 0.089 h-1 for the viable biomass in dependence on the dilution rate (Figure 7.14) indicating the appearance of the oscillatory fermentation. Similarly, the point with the maximum concentration of viable cells was also established. This point matches with the max­imum ethanol concentration and corresponds to an inlet substrate concentration of

137.5 kg/m3. Finally, through bifurcation analysis, it is possible to delimit the zones of the operating diagram S0 versus D (Figure 7.15). In particular, the parameter S0 has demonstrated a strong influence on the fermentation behavior. Small changes in the nutrient composition of the culture broth entering the continuous fermenter can provoke significant changes in the response variables (concentrations of biomass, ethanol, and residual substrate). Knowing these zones, the task of operating the fermenter in the stable zones becomes easier. Moreover, the design of the control

FIGURE 7.15 Operating diagram of the continuous ethanolic fermentation for inlet sub­strate concentration (S0) and dilution rate (D).

structure can be based on these nonlinear analysis tools. Therefore, the modified model can predict when the instability of the fermentation will occur. At this point, the instability of the system can be considered during the early step of conceptual design, which permits a better design of the automatic control system. This issue is of great importance during the operation of industrial ethanolic fermentations.

This chapter deals with the wastes generated during the different processing steps for fuel ethanol production. The influence of the feedstock is recognized by the type of wastes produced. Main methods for the treatment of stillage (the major polluting effluent of all ethanol production processes) are discussed. In addition, the environmental issues related to the overall fuel ethanol production process are highlighted emphasizing some methodologies for assessment of its environmental performance.

The Food and Agriculture Organization of United Nations clearly defines the basis for food security existence: Food security exists when all people, at all times, have access to sufficient, safe, and nutritious food to meet their dietary needs and food preferences for an active and healthy life. The Rome declaration on world food security in 1996 established the strategies of global dimensions to solve the prob­lems of hunger and food insecurity (United Nations, 1996). However, at that time, the bioenergy or biofuels were not mentioned or considered as a threat.

Today, the opposition considers that the use of biofuels exacerbates world hun­ger based on the land competition for bioenergy and food crops. On the other hand, defenders believe that far from creating food shortages, biofuels represent the best opportunity for sustainable economic prospects in many developing countries. To discuss these positions objectively, different factors must be analyzed in order to explore the global panorama of the problem (or the opportunity). First, potentials for growing edible crops for biofuels or food should be identified. Second, the residues obtained in food crops production and processing are a source for cel — lulosic ethanol without using new lands for biofuels. The genetic modification of crops looking for high productivity is also an issue of high importance. Another important factor is the complex relationships between bioethanol and fossil oil dependence not only for energy but also for agrochemicals and fertilizers. But one of the most significant factors to be considered in this discussion is the economical issue (including agricultural market impacts) of producing crops for bioethanol instead of foods. Hereinafter, these factors are studied and discussed.

For this type of discussion, technical tools are needed. An integral strategy was proposed by the Bioenergy and Food Security (BEFS) Project, whereby the risks and opportunities of bioenergy and how it can affect food security in developing countries are analyzed. An overview of the project, its strategy and objectives, as well as some modifications given by the authors, is presented in this chapter.

The production of tert-amyl methyl ether (TAME) began at the commercial level at the end of the 1980s both in the United States and in Europe. TAME is used as a gasoline oxygenate like MTBE, but it uses another type of feedstock: tertiary olefins with five carbon chains (tert-C5 olefins) that exist among the components of the different kinds of gasoline produced in an oil refinery. The tert-C5 olefins are selectively converted into tertiary ethers employing methanol. In this way, TAME production represents an expansion of the raw material basis for producing oxygen­ates as well as the possibility of reducing the content of tertiary olefins and increas­ing the oxygen content in gasoline (Huttunen et al., 1997). TAME is obtained by the liquid-phase reaction of methanol and two of the three isoamylenes (branched olefins of five atoms of carbon): 2-methyl-1-butene and 2-methyl-2-butene. The third isoamylene (3-methyl-1-butene) is not reactive. This reaction is accomplished using acid ionic exchange resins (Oost et al., 1995). TAME solubility is compa­rable to that of ETBE (12 g/L) and can be transferred to surface water streams and groundwater as well. TAME is not easily biodegradable and is persistent in the soil and sediments (Huttunen et al., 1997). In some cases, the refineries employ blends of MTBE, ETBE, and TAME. This results in water streams having contents of these three ethers. The possibility of aerobically degrading this kind of blend using fixed bed reactors has been demonstrated (Kharoune et al., 2001).

Alternatively, the production of the ethyl homologue of TAME, the tert-amyl ethyl ether (TAEE), has been proposed as an attractive option when the ethanol production costs decrease (Ancillotti and Fattore, 1998). TAEE can be produced from isoamylenes as well, but the use of ethanol could make it renewable. In addi­tion, TAEE has one of the lowest RVPs compared to the main oxygenates used in the refineries (see Table 1.1).

After corn, wheat (Triticum spp.) is the most employed grain for fuel ethanol pro­duction, especially in Europe and North America, due to its high starch content (Table 3.8). Other than the sugar beet, wheat is the main feedstock for ethanol production in France (Poitrat, 1999). In fact, France was the fifth world producer of wheat in 2007 (33.21 million tons) after China (109.86 million tons), India (74.89 million tons), United States (53.60 million tons), and Russian Federation (49.38 million tons; FAO, 2007b). The wheat yield in France is 6.25 ton/ha, while in Ireland, it reaches up to 8.1 ton/ha (FAO, 2007a).

Sorghum (Sorghum bicolor) is another grain proposed for ethanol produc­tion. The United States is the world’s leading producer of sorghum with a volume of 12.82 million tons in 2007 (FAO, 2007a, 2007b). Sorghum is employed in many countries, such as Colombia, as a component of animal feed, though corn is replacing it. One of the features of sorghum as feedstock for ethanol produc­tion is the presence of significant amounts of tannins. These tannins provoke the decrease in the ethanol production rate during the fermentation process, although they do not affect either the ethanol yield or the enzymatic hydrolysis of starch contained in sorghum (Mullins and NeSmith, 1987).

One of the most promising crops for fuel ethanol production is sweet sorghum, which produces grains with high starch content, stalks with high sucrose content, and leaves and bagasse with high lignocellulosic content (Sanchez and Cardona, 2008a). In addition, this crop can be cultivated in both temperate and tropical coun­tries, it requires one third of the water needed for the sugar cane harvest and half of the water needed by corn, and it is tolerant to drought, flooding, and saline alkalin­ity (du Preez et al., 1985; Winner Network, 2002). Grassi (1999) reports that from some varieties of sweet sorghum the following productivities can be obtained: 5 ton/ha grains, 8 ton/ha sugar, and 17 ton dry matter/ha lignocellulosics.

3.2.2.1 Cassava

The cassava (Manihot esculenta) is a perennial bush achieving 2 m by height and is native to South America. The main feature of this plant is its edible roots, thus the plant is uprooted after one year of growth in order to obtain its better conditions for its consumption. The cassava root is cylindrical and oblong and can reach up to 100 cm in length and 10 cm of diameter. Its pulp is firm and presents high starch content. Cassava tubers are consumed in a cooked form and represent a crucial component in the food of more than 500 million people in America, Asia, and Africa. The cassava is not a very exigent crop, but it should be grown no higher than 1,500 m above sea level. For cassava cropping, the soils should be porous because the root requires sufficient oxygen levels to grow; it also requires good drainage. The cropping temperature should be in the range 25 to 30°C (Agronet, 2007). For this reason, cassava is one of the most important tropi­cal crops in the world. Once planted, cassava roots can be harvested after seven months and stay in the soil for three years (Alarcon and Dufour, 1998).

The world’s largest cassava producer is Nigeria, followed by Brazil, Indonesia, and Thailand (FAO, 2007b). As can be observed in Table 3.9, major cassava producers are located in Africa, Southeast Asia, and South America. The most

elevated cassava yields among the main producers are from Thailand (20.28 ton/ ha) and Brazil (13.63 ton/ha; FAO, 2007a). In general, more than 90% of cas­sava production is directed to human food, while the balance is used for produc­ing starches and snacks. The substitution of corn with cassava flour has been proposed for animal feed production taking into account its high energy content (Espinal et al., 2005a). The importance of cassava cropping from the viewpoint of its agro-industrial applications lies in its high potential for energy production in the form of starch (see Table 3.8). Espinal et al. (2005a) report that the use of improved seeds and fertilizers, and a suitable weed control allows production of 20 to 30 ton/ha of fresh roots and 10 to 12 ton/ha of dried cassava in zones where other starch-producing crops like corn, sorghum, or rice do not reach yields above 4 or 5 ton/ha.

There exist two main methods for industrial production of native cassava starch: the traditional method employed in India and some Latin American coun­tries, and the modern method of the type used by the company Alfa Laval for large-scale production. In the traditional process, fresh roots are washed and debarked before crushing in a rotary rasper. Starch is separated from the crushed pulp before passing through a series of reciprocating nylon screens of decreasing mesh size (50250 mesh). The resultant starch milk is settled over a period of four to eight hours using a shallow settling table or a series of inclined channels laid out in a zigzag pattern. Settled starch is sun-dried on large cement drying floors for approximately eight hours. During this period, the moisture content reduces from 45 to 50% down to 10 to 12%. To achieve efficient drying, sunny conditions are required with ambient temperatures of more than 30°C and relative humidity of 20 to 30%. Dried starch is ground to a fine powder and packaged for sale (FAO and IFAD, 2004). In the modern Alfa Laval-type process, roots are washed and debarked, sliced and then crushed in a rotary rasper. Starch pulp is passed through two conical rotary extractors to separate starch granules from fibrous materials, and then fed via a protective safety screen and hydro cyclone unit to a continuous centrifuge for washing and concentration. The concentrated starch milk is passed through a rotary vacuum filter to reduce water content to 40 to 45% and then flash dried. The flash drying reduces moisture content to 10 to 12% in a few seconds, so starch granules do not heat up and suffer thermal degradation.