Product Recovery, and Stillage Evaporation Steps for Fuel Ethanol Production

In a previous work (Grisales et al., 2005), the heat integration approach was uti­lized for the analysis of the fermentation, distillation, and evaporation steps of the fuel ethanol production process from lignocellulosic biomass using azeotropic dis­tillation for ethanol dehydration. Low ethanol concentrations in the culture broth exiting the fermenter increase energy costs in the distillation train and, therefore, in the evaporation train utilized for obtaining concentrated stillage (the first opera­tion of the effluent treatment scheme). For this reason, it is of great importance that the application of energy integration be instituted in order to improve process performance and make it more environmentally friendly (via the reduction in the consumption of external nonrenewable sources of energy).

Process simulator Aspen Plus was employed for calculating mass and energy balances of the analyzed technological configuration. Through a graphical repre­sentation of the energy requirements of the process, the exchanged heat was identi­fied considering the external utilities (steam and cooling water). The process was represented by its hot and cold profiles, which were defined by the corresponding hot and cold composite curves (Figure 11.15). These curves show how much energy could be transferred from hot streams to cold streams within the process. To com­plete the global heat balance, hot utilities (vapor at 2 bar) and cold utilities (cooling water at 10°C) were utilized. For the definition of the required amount of hot and cold utilities, a grand composite curve was built. Consumed energy by hot and cold utilities was determined by simulation. For the design of HEN, a grid diagram was employed in which streams were represented with their respective supply and tar­get temperatures as well as the position of pinch. Through heuristic rules for pinch (Shenoy, 1995), different configurations of HEN were proposed and evaluated in terms of total recovered heat and operation costs. These HENs should ensure the target temperatures of the streams. Heat transfer areas were also calculated in order to define the capital costs. In particular, it was established that the hot stream exit­ing the top of the first distillation column (concentration column) should be split

into two substreams. These two substreams are organized in such a way that they transfer heat to the second effect of evaporation and to the heat exchanger utilized for preheating the evaporated liquid exiting this second effect which is sent to the third effect of evaporation. This configuration contrasts with the base case configu­ration where this stream is condensed and sent as a distillate to the second distilla­tion column (rectification column) without taking advantage of its caloric energy.

Applying the described procedure, the energy saving of the new HEN was determined compared to the original network for the studied process steps. This information allowed quantifying the economic benefits that could be obtained if the defined HENs by means of pinch analysis were implemented. For instance, the external energy supplied to the process by the hot utilities was reduced by 22.8% for a minimum temperature difference of 5°C. The achieved energy recovery is 75.5% of the maximum possible energy recovery calculated in the targeting step (Grisales et al., 2005).

Although MTBE volatility is relatively low, the standards for reformulated gasoline in the developed countries are very exigent during summer in order to reduce the smog formation in the cities. This makes the RVP specifications for gasoline lower than the corresponding value for MTBE. For this reason, the employing of ethers with more branched carbon chains and lower volatilities has been explored. Precisely, the ethyl tert-butyl ether (ETBE), the second most utilized ether as an oxygenate, has an RVP less than MTBE and antiknocking properties slightly higher (see Table 1.1). ETBE is produced by the exothermic reaction between isobutene and ethanol. As in the case of MTBE production, this reaction requires ionic exchange resins as a catalyst (Ancillotti and Fattore, 1998). ETBE is produced mainly in the United States and Europe. The produc­tion of ETBE incorporates reactive distillation using acid ionic exchange res­ins (Thiel et al., 1997) or structured zeolites as packing materials (Oudshoorn et al., 1999). One substantial difference in ETBE production is that one of its feedstocks, ethanol, can be obtained from renewable resources like the bio­mass, which entails the integration of the petrochemical industry with the bio­technological sector. One example is the case of France where bioethanol has been produced from sugar beets since 1990. In this way, the ETBE obtained is partially renewable, which implies greater environmental benefits compared to MTBE whose production is totally from fossil origin. These benefits are repre­sented in a lower emission of greenhouse gases (N2O, CH4, and CO2), a lower contribution to the depletion of natural resources, and better air quality in the cities (less unburned hydrocarbons and formed formaldehyde, although more acetaldehyde produced). In fact, a 3.8% reduction in the amount of unburned hydrocarbons emitted and 17.6% decrease in benzene emissions has been noted in vehicles provided with catalyst employing gasoline blends with 15% (by vol­ume) ETBE (Poitrat, 1999). On the other hand, Spain also employs the ETBE as an oxygenate for its gasoline. In this case, the ETBE production has reached its maximum due to the availability of isobutene in the Spanish refineries (Espinal et al., 2005).

The solubility of ETBE in water is also relatively high compared to the remaining gasoline components (12.2 g/L). Like MTBE, ETBE can also be eas­ily transferred to groundwater from gasoline leakages from storage tanks, pipe­lines, or other distribution systems (Ancillotti and Fattore, 1998). The ETBE biodegradation under aerobic or anaerobic conditions has been researched to evaluate the potential for its decontamination (Kharoune et al., 2001).

Corn (Zea mays) is the most employed feedstock in the world to produce starch either for the food industry or for ethanol production. Corn is a crop whose origin is in the Americas. Its stalks can reach 4 m in height and does not have branches. Its seeds are the structure with the highest value of starch in this plant and are the base for human food in many communities in Central and South America. The starch accumulates in the endosperm of corn seeds. Corn requires a temperature of 25 to 30°C and an important degree of sunlight for its growth. Corn can toler­ate minimum temperatures down to 8°C. At 30°C, the adsorption of nutrients and water can be difficult. This crop is exigent in water, but adapts itself very well to all kind of soils preferring those with a pH of 6 to 7. Similarly, corn requires deep soils rich in organic matter (Infoagro, 2007).

Corn is the most cropped grain in the world, followed by wheat and rice. The United States is the major corn producer, followed by China and Brazil (FAO, 2008), as shown in Table 3.7. The United States reported the planting of 35.02

million ha in 2007; China, 28.07 million ha; and Brazil, 13.82 million ha. The U. S. yield is very high (about 9.48 ton/ha), although Kuwait has the maximum yield (21.0 ton/ha), but it has a very small annual production: 1,050 ton in 2007 (FAO, 2008). In the United States, both private and public sectors have been sys­tematically investing in research and development of corn cropping oriented to the production of sweeteners for the food sector and ethanol for transportation sector. In addition, the U. S. government has granted certain tax exemptions to farmers in order to support corn production, which is considered a strategic crop. It should be noted that the powerful lobby of corn producers has confronted the big oil industry supporting the implementation of renewable fuels from corn. In fact, most ethanol-producing plants in the United States employ corn as a feedstock. In 2007, 22.5% of U. S. corn production was dedicated to ethanol production (FAO, 2008; Renewable Fuel Association, 2009; Sanchez and Cardona, 2008b). China also has been increasing its ethanol production from grains including corn. This fact, along with the expected increase in ethanol production in the United States, has been pushing corn prices upward. This has had direct effects on the costs of corn-containing foods in such importing countries, such as Mexico.

Starch is the corn component that is directly used for ethanol production. Likewise, starch is corn’s main component, as seen in Table 3.8 from data com­piled by Cardona et al. (2005). There exist two technologies for using corn starch as a feedstock for fuel ethanol production. One technology employs the separation of starch from all the other components of the corn kernel and this is called wet milling. In this way, during the wet milling process, the corn kernel is separated into its components: starch, fiber, gluten, germ, and oil. The other technology does not involve the separation of starch. By contrast, the whole milled grain enters into the ethanol production process directly (dry milling).

The corn wet milling process allows the production of a series of value-added co-products that offset to a certain degree the fuel ethanol production costs. These co-products are directly related to the structure of corn grain. Starch represents 60% of the grain and is located in the endosperm. Grain proteins are concen­trated in the gluten. Corn oil is located in the germ and represents 4% of the grain

(Gulati et al., 1996; U. S. Grains Council, 2007). The overall wet milling process is depicted in Figure 3.5. The first step in the corn wet milling process is the steeping of the grains in large tanks that can process from 1,500 to 6,000 bushels of corn, according to the typical volumes of starch industry in the United States (U. S. Grains Council, 2007). Steeping is carried out during 30 to 50 h at 49 to 54°C in water containing 0.1 to 0.2% sulfur dioxide. The sulfurous acid that is formed contributes to the separation of the starch and insoluble proteins, break­ing the protein matrix of the endosperm through the destruction of disulfur bonds in gluten proteins. During this step, about 6% of dry matter is dissolved in the liquid containing the corn steep liquor. These dissolved components provide the nutritional value to the corn extractives that are condensed and fermented after the partial dehydration of steeping in water.

After grain steeping, the swelled corn grain contains about 45% water. This soft grain is ground and the germ is removed by flotation. The germ is cracked and its oil is extracted using hexane as a solvent. The corn oil is refined while the solid residue of the germ is dried to prepare the corn germ meal. Once the germ is removed, the resulting material undergoes milling that crushes starch particles and releases the fiber. This fiber is separated from the starch and gluten proteins by using a screen. The thick mixture of starch and gluten is pumped to a rotary disk column where these two components are separated by applying centrifugal force. In this centrifugal separator, the product that is obtained contains approxi­mately 60% protein. This product is concentrated, filtered, and dried to obtain the corn gluten meal. The starch is separated again to reduce its protein content down to 0.3% (U. S. Grains Council, 2007). The fiber in the first part of milling is added to the evaporated corn steep liquor. The stream from this process evaporates in order to produce the corn gluten feed, as illustrated in Figure 3.5.

The co-products obtained are mostly intended for animal feed. The corn gluten meal has a high protein and energy content and is a good source of the amino acid methionine. This co-product is used for cattle feed as a protein supplement (about 60% protein) as well as for feeding poultry and pigs. Corn gluten feed contains about 20% protein and a reduced amount of oil and fats. This material is also used as a protein supplement, although it has a lower nutritive value in comparison to corn gluten meal. It can be used for poultry and pig feed and even for ruminants due to its significant content of digestible fiber. Corn oil is utilized for human food. In general, from each 100 kg of corn processed by this technology, 2.87 kg corn oil, 4.65 kg corn gluten meal, and 24.09 kg corn gluten feed can be obtained. According to the U. S. Department of Agriculture, the average price per tonne of these products is as follows: US$357 for corn gluten meal, US$88 for corn gluten feed, and US$662 for corn oil (Bothast and Schlicher, 2005).

Due to the need for improving fuel ethanol production processes especially when lignocellulosic biomass is used, the development of microorganism strains with better performance in terms of ethanol yield and productivity is required, par­ticularly concerning the direct conversion of polymeric feedstocks. For instance, a way to improve the technoeconomic indexes of ethanol production processes from starch consists in developing yeasts able to hydrolyze this polysaccharide and then ferment the glucose formed without the addition of amylases. Moreover, the effective utilization of alternative feedstocks to obtain ethanol as the lignocel — lulosic residue requires microorganisms with traits difficult to find in a single spe­cies (cellulase production, pentose assimilation, high ethanol yields, high ethanol tolerance, among others). The native strains of microorganisms cannot meet all these exigencies; therefore, their genetic modification is needed.

Of the thousands of genes contained in the microbial DNA, 90 to 95% are repressed, i. e., at a given moment, the microbial cells require only the expression of a reduced amount of genes for them to accomplish all their metabolic func­tions directed to cell biomass growth. This implies that most genes are involved in complex regulation processes so that the information contained in them sup­plies the necessary instructions for the cells to produce myriad metabolites. From an industrial viewpoint, the higher the number of genes expressed, the wider the spectrum of potential value-added products synthesized as well as the greater the number of substrates that can be assimilated by the microorganism. This is accomplished through the synthesis of a higher number of enzymes responsible for that wider assimilation. Similarly, if the natural repression of the genes encod­ing the production of a given metabolite is eliminated, significant increases in the yield of that metabolite can be reached, though the cell growth may be reduced. In this way, super-producing strains of microorganisms can be developed for pro­duction of different products with commercial importance. Therefore, the selec­tion of an industrial strain of microorganisms requires a selection program that can include the genetic modification of native strains of those microorganisms. The genetic modification of industrial microorganisms can mainly be done in two ways: random modification of DNA and directed modification.

In a previous work (Quintero et al., 2007), a simulation of the ethanol dehydration process in addition to an evaluation of energy and capital costs was performed. For this, several representative configurations were compared under the same input conditions as the case of a fermented mash with a given ethanol concentration (11.4%) and considering the physical-chemical properties of the components of the culture broth. To this end, commercial process simulators were utilized for solving mass and energy balances and performing the economic evaluation.

The simulation of mass and energy balances was accomplished by using the commercial package Aspen Plus v11.1 (Aspen Technology, Inc., Burlington, MA). The composition of the feed stream to the separation and dehydration steps cor­responded to the culture broth resulting from the fermentation step after the cell biomass removal by centrifugation. The composition of this stream for the particu­lar case of fermentation of starchy materials is as follows (in % by weight): water 63.70, ethanol 11.40, CO2 10.80, protein 7.44, linoleic acid 1.71, hemicellulose 1.50, cellulose 1.12, oleic acid 1.40, ash 0.38, glucose 0.22, dextrins 0.20, lignin 0.05, starch 0.01, and others. The flowrate of this feed stream was set to 152,153 kg/h for all the separation and dehydration schemes analyzed. Part of the data for simulation of physical properties was obtained from Wooley and Putsche (1996) who compiled information from the literature, estimated the properties when necessary, and deter­mined a consistent set of physical properties for some key components of fuel etha­nol production process. The remaining data were obtained from secondary sources of information (handbooks, monographs, papers, presentations, etc.). During the simulation, the nonrandom two-liquid (NRTL) thermodynamic model was used to calculate the activity coefficients in the liquid phase and the Hayden-O’Conell equation of state was used to model the vapor phase. The biological transformations were simulated based on a stoichiometric approach as shown in the Chapter 7.

Before the definitive simulation of the schemes, a preliminary simulation of the processes involving distillation columns was performed. The preliminary simu­lation of such columns was done by using the DSTWU module of Aspen Plus, which employs a short-cut method based on the equations and correlations of Win-Underwood-Gilliland. This module was chosen taking into account the pres­ence of the binary ethanol-water azeotrope. This module also provides an initial

estimation of the minimum amount of theoretical stages, minimum reflux ratio, location of the feed stage, and component distribution.

Once performed, the preliminary simulation distillation processes were analyzed using rigorous thermodynamic models. Thus, the rigorous calculation of operating conditions in distillation columns was carried out using the module RadFrac of Aspen Plus based on the method inside-out that employs the MESH equations. This method implies the simultaneous solving of mass balance equations (M), phase equilibrium equations (E), expressions for summation of the compositions (S), and heat balance equations (H) for all the components in all the stages of the given dis­tillation column. Other than the results obtained by the DSTWU module, the infor­mation required by the simulator for the specification of input data of distillation columns was obtained with the help of short-cut methods based on the principles of the topological thermodynamics, especially the analysis of the statics (Pisarenko et al., 2001). Sensitivity analyses were performed to study the effect of main operating variables (reflux ratio, temperature of feed streams, ratio between the solvent and feed, etc.) on the purity of ethanol obtained and the energy consumption. The final result was the establishment of the operating conditions that allow accomplishing more efficient ethanol dehydration processes. The estimation of the energy con­sumption was carried out based on the results of the simulation, taking into account the thermal energy required by the heat exchangers and reboilers. Capital costs, overall operating costs, and other related expenditures were calculated by using Aspen Icarus Process Evaluator™ v11.1 (Aspen Technology, Inc.).

Pressure-swing adsorption was the studied technology for the analysis of etha­nol dehydration by adsorption with molecular sieves. The simulation of this process considered that the adsorption was carried out in vapor phase so the distillate from the rectification column was not condensed and its temperature was raised to 116°C before sending it to the adsorption unit. The technology simulated corresponded to the PSA. The desorption cycle considered vacuum conditions at 0.14 atm. The vapors from desorption were recirculated to the rectification column where the used ethanol was recovered (see Figure 8.7). Vacuum distillation, azeotropic distillation using benzene as the entrainer and extractive distillation using ethylene glycol as the solvent were also analyzed as ethanol dehydration configurations. Each one of the schemes included a scrubber for recovery of 98% of ethanol volatilized and a preheater that brought the stream to be fed to the first distillation column until its saturation point. To concentrate the wine, two distillation columns were consid­ered. In the first (concentration) column, ethanol content was raised up to 50%, while in the second one (rectification column), the ethanol concentration reached 91%. Both columns, the scrubber, and the pre-heater were included in all the dehy­dration schemes simulated (see Figure 8.1).

Obtained results showed the inconvenience of using pressure-swing (vacuum) distillation for dehydrating ethanol. The simulation indicated that large distillation columns with many stages (above 40) are required to obtain a high purity product. In addition, high reflux ratios are needed. These conditions imply high energy costs due to the high heat duty of the column reboilers and to the maintenance of vacuum conditions in the second column having a large amount of trays. Thus, the energy consumption of this dehydration scheme reaches 12.17 MJ/L of ethanol. According to the performed calculations, the distillate of the vacuum column has an ethanol content of 99.3%. The capital costs of this scheme along with the corresponding costs of the other dehydration schemes are presented in Table 8.1.

TABLE 8.1 Capital and Operating Costs (in US$) for Different Separation and Dehydration technologies used for Fuel ethanol Production Vacuum azeotropic extractive Molecular Item units Distillation Distillation Distillation sieves References Ethanol produced kg/yr 141,560,084 142,609,349 141,897,940 142,726,998 Montoya et al. (2005); Total capital costs US$ 14,156,063 9,547,963 9,525,920 12,809,706 Quintero et al. (2007); Total operation costs US$/yr 11,539,808 8,943,642 8,023,714 7,730,563 Sanchez(2008) Utilities US$/yr 9,063,508 7,113,850 6,266,715 53,821,429 Labor US$/yr 600,000 600,000 600,000 600,000 Maintenance costs US$/yr 381,000 78,000 75,100 191,000 Other US$/yr 1,495,300 1,151,592 1,081,899 1,118,134 Unit capital costs US$/kg 0.1000 0.0670 0.0671 0.0897 Unit operation costs US$/kg 0.0815 0.0627 0.0565 0.0542

In contrast, adsorption with molecular sieves showed the best results regard­ing operation costs, i. e., this technology presents lower energy costs (7.68 MJ/L). Elevated capital costs for the configuration involving adsorption are related to the complexity of the automation and control system inherent to the pressure swing adsorption technology. The higher energy consumption for azeotropic (9.77 MJ/L) and extractive (8.44 MJ/L) distillation is explained by the presence of two addi­tional distillation towers that increase the energy costs. The product for these lat­ter schemes contains traces of the entrainer or the solvent unlike the dehydration by adsorption where these third components are not utilized. Simulation results indicate that the extractive distillation can be competitive compared to azeotropic distillation from an energy point of view, as pointed out by Meirelles et al. (1992). According to the simulations performed, the amount of ethylene glycol required to attain the desired dehydration of ethanol was 17,900 kg/h, but it is necessary to cre­ate only 50 kg/h of this solvent thanks to the recirculation stream from the recovery column. The amount of benzene required for azeotropic distillation was 19,980 kg/h, but the amount of fresh benzene in the make-up stream was only 17 kg/h. It is worth noting that the convergence of the simulation of this dehydration scheme was a difficult task that required numerous successive simulation runs. This behavior can be explained by the appearance of multiple steady states and the presence of a parametric sensitivity with small changes in the column pressure when benzene is the entrainer used, as indicated by Wolf and Brito (1995). The obtained results represented a suitable approximation of the results published in different sources (Chianese and Zinnamosca, 1990; Luyben, 2006) as well as the predictions of the thermodynamic-topological analysis. It should be emphasized that the azeotropic distillation using benzene is not an environmentally friendly process and the opera­tion of such dehydration schemes imply the utilization of a carcinogenic substance that can involve potential risks for the operating staff.

From outcomes achieved, it is evident that process simulation represents a pow­erful tool to design the downstream processes of such biotechnological processes as fuel ethanol production. Similarly, the importance of applying a suitable ther­modynamic approach to study the separation operations is another crucial factor influencing the success of the simulation procedures.

The assessment of fuel ethanol production processes from an environmental point of view implies not only the quantification and analysis of the polluting burdens generated by the conversion technologies, but also the evaluation of the environ­mental performance of such processes considering their influence on the planet in terms of the depletion of natural resources, contamination of the ecosystems, and global environmental impacts generated.

The starting point to satisfy the standard of clean production is the environmen­tal diagnosis in order to determine the opportunities for preventing or reducing the contamination sources and the viable alternatives to carry out such reduction. In the last four decades, the design of processes and the production of chemicals have experienced a great evolution (Montoya et al., 2006). Initially, reaction and separation systems were designed and optimized according to only one economic objective. In the 1970s and 1980s due to the global energy crisis, the utility sys­tem (thermal energy, power, cooling water) was included in the process design and optimization procedures. Nowadays, under the clean production scheme, the environmental objective should be taken into account along with the economic objective. In this sense, the environmental objective should be to consider not only the specific environmental impact of the process, but also its impacts in other steps of its life cycle.

The waste minimization as a means to attain the clean production and con­tribute to the sustainable development has been extensively studied in the process industry and in academic circles. The waste minimization incorporates both the waste reduction in the source and the use of recycling to reduce the amount and risk of the residues. Nevertheless, the difference between hazardous and nonhaz­ardous wastes is not considered. In this connection, the minimization of the envi­ronmental impact is a much stricter norm and, although its scope is similar to the waste minimization, emphasizes in a higher degree the different impacts of the chemical species on the environment (Yang and Shi, 2000). The measurement of the environmental performance of a process can be viewed as a decision problem involving two levels: screening indices on the process level and environmental performance indicators on the chemical species level. The latter indicators are the base of the screening indexes and offer enough flexibility to consider the fate of all the components involved and their subsequent impacts. Such impacts are in the framework of a group of categories, i. e., environmental performance indi­cators show the impact with which the process contributes to a given category. Among these categories, the following should be highlighted, as reported by Yang and Shi (2000): energy consumption, resource consumption, greenhouse effect, ozone depletion, acidification, eutrophication, photochemical smog, human toxic­ity, ecotoxicity, area used and species diversity, odor, and noise. To consider the environmental impact of a process, several methodologies have been developed, such as the life cycle assessment (LCA) and the waste reduction (WAR) algo­rithm, among others.

10.2.1 WAR Algorithm

In Chapter 2, Table 2.1, the different steps involved in the development of an industrial process during its life cycle were highlighted. During the conceptual process design step, the information available is quite limited and uncertain. In this case, the screening indexes and environmental performance indicators should be based on simple mass and energy balances. Thus, the environmental indicators developed by Heinzie et al. (1998) that take into account the different decision levels during the conceptual design are based on mass losses. In a similar way, the ecotoxicological models developed by Elliot et al. (1996) are also based in mass units instead of concentration units (Yang and Shi, 2000).

The waste reduction algorithm originally proposed by Hilaly and Sikdar (1994) was based on the concept of pollution balance. Currently, the WAR algorithm is one of the most practical methodologies for assessing the environmental impact of a process, especially at the conceptual design step. It allows assessing and com­paring the environmental friendliness of many different industrial processes. The methodology of the WAR algorithm developed by the National Risk Management Research Laboratory of the U. S. Environmental Protection Agency (EPA) uses the concept of potential environmental impact (PEI) and proposes to add a con­servation relationship over the PEI based on the input and output impact flows of the process. In this context, the PEI of a given quantity of mass and energy is understood as the effect that this mass and energy would have on average on the environment if they were to be discharged into the environment from this process (Cardona et al., 2004; Young and Cabezas, 1999). This definition implies that the impact is a quantity still not realized and the PEI has a probabilistic nature. Thus, the PEI of a chemical process is usually caused by the mass and energy that this process acquires or emits into the environment. In this way, the WAR algorithm is a tool to perform the evaluation of the PEI of a process based on the product streams, outlet wastes, and the feed to the process.

The overall PEI of a chemical k is determined by summing the specific PEI of the chemical over all the possible impact categories:

¥ k =^a‘ ¥ ш (10.1)

і

where ykl represents every impact category and al is the weighting factor, which is used to emphasize the particular areas of concern. These categories fall into two general areas of concern— global atmospheric and local toxicological—with four categories in each area. The four global atmospheric impact categories include

1. Global warming potential (GWP)

2. Ozone depletion potential (ODP)

3. Acidification or acid-rain potential (AP)

4. Photochemical oxidation or smog formation potential (PCOP)

The four local toxicological impact categories include

1. Human toxicity potential by ingestion (HTPI)

2. Human toxicity potential by either inhalation or dermal exposure (HTPE)

3. Aquatic toxicity potential (ATP)

4. Terrestrial toxicity potential (TTP; Cardona et al., 2004; Young and Cabezas, 1999)

This algorithm was modified in a previous work (Cardona et al., 2004) and the changes incorporated into the new versions of WARGUI (WAste Reduction algorithm—Graphical User Interface) software released by the EPA.

The WAR algorithm handles two classes of indexes to assess the environmen­tal impact of a chemical process. The first class measures the PEI emitted by the process while the other one measures the PEI generated within the process. Each class has two main indexes: total output rate of PEI (expressed as PEI per time unit) and PEI leaving the system per mass of product streams. The first class characterizes the PEI emitted by the system and is used to answer questions about the external environmental efficiency of the process, i. e., the ability of the plant to produce the desired products at a minimum discharge PEI. The second class of indexes characterizes the PEI generated by the system and its importance lies in the determination of the internal environmental efficiency of the process, i. e., how much PEI is being generated or consumed inside the process. The lower the values of these indexes, the more environmentally efficient the process is. Considering the variation of the plant capacities, the index per mass of products should be employed if the goal is to assess the environmental impact of a process independent of its production rate, especially if different alternative processes are to be evaluated (process synthesis).

Phase 1: Develop analytical framework and guidance to assess the bioen­ergy and food security nexus.

Phase 2: Assess bioenergy potential and food security implications.

Phase 3: Strengthen institutional capacities, exchange knowledge, pilot sustainable and food-secure bioenergy practices, and recommend stan­dards and policies

BEFS partners include Cambodia, Peru, Tanzania, and Thailand. The project has already begun in Tanzania. The project provides to these countries a science- based quantitative methodology to minimize food security risks. This approach helps to build their own capacity and management, at the same time, appreciat­ing food security concerns. The project itself is not just an assessment. BEFS produces a permanent economic forecast and food security monitoring, which emphasizes deepening insights for developing countries’ bioenergy potentials

Figure 12.1 shows the analytical framework of the BEFS project. Every mod­ule is linearly connected, but entirely independent in relation to other modules. One axis is the basis of all the modules: consideration of food security as primary. The purposes and activities of the modules are discussed below.

12.5.1 Purposes and Activities of Modules

12.5.2.1 Module 1

Biomass potential helps stakeholders to understand:

• The extent and location of areas suitable for the relevant bioenergy crops.

• Assist farmers in bioenergy developments in their land-use planning.

• Highlight the advantages and disadvantages of different agricultural production systems and level of inputs.

• Detail land requirements for current and future food to safeguard food production.

The phenomena-driven approach for process synthesis considers as a starting point for the design, not the unit operations, but the phenomena occurring in them at a lower level of aggregation (Gavrila and Iedema, 1996). Reactions, phase changes, heat and mass transfer, and mixing are considered among the phenomena included in this method. The design problem is divided (decomposed) into three tasks: role assignment, phenomena grouping, and operating condition analysis. The goals of these tasks can be formulated through the following questions:

• What should occur in the process in order to achieve the global design target?

• Where should it occur?

• When and how should it occur?

In the second task, the alternative designs are proposed by grouping the phe­nomena in units and the continuous variables are described as Boolean variables (for instance, is the rate of a phenomenon equal to zero or greater than zero?). In the third task, the favorable conditions in the units are defined employing ordinal relations among quantities (dR/dt > dS/dt; Gavrila and Iedema, 1996). This method is oriented to explore innovative units and processes in order to sup­port the creativity during the design process. However, the method is based on an opportunistic identification and integration of the tasks as pointed out by Li and Kraslawski (2004). This approach has been applied to very particular cases as in the production of methyl tert-butyl ether (MTBE) by reactive distillation (Tanskanen et al., 1995).

The lignocellulosic biomass represents the most abundant source of fermentable sugar in nature, not only for fuel ethanol production, but also for producing a wide range of fermentation products, such as additives for the food industry, indus­trial chemicals, components of balanced animal feed, and pharmaceuticals. But to utilize this renewable resource in the production of most of these products, the lignocellulosic biomass must be pretreated, i. e., the lignocellulosic materi­als should be suitably processed in such a way that their constituent sugars and polysaccharides are susceptible to the action of hydrolytic enzymes as well as of fermenting microorganisms.

In the previous chapter, the complexity of the structure of lignocellulosic bio­mass was recognized considering that the lignin and hemicellulose form a sort of seal covering the polysaccharide with the highest potential to release glucose, the cellulose. In addition, it should be emphasized that most of cellulose in biomass has a crystalline structure derived from the longitudinal alignment of its linear chains. In the crystalline cellulose, the polysaccharide-polysaccharide interactions are favored and the polysaccharide-water interactions are reduced so this biopolymer is insoluble in water. A minor fraction of cellulose has an amorphous structure (Figure 4.2). The hemicellulose chains establish hydrogen bonds with the cellulose microfibers forming a matrix reinforced with lignin. The lignin presence makes it so the lignocellulosic complex cannot be directly hydrolyzed with enzymes. In this way, factors such as the crystallinity degree of cellulose, available sur­face area (porosity of the material), protection of cellulose by the lignin, pod-type cover offered by the hemicellulose to the cellulose, and heterogeneous character of the biomass particles contribute to the recalcitrance of the lignocellulosic materi­als to the hydrolysis. In addition, the relationship between the biomass structure

and its composition adds a factor implying even more variability exhibited by these materials regarding their digestibility (Mosier et al., 2005b). Therefore, the pretreatment step of the lignocellulosic complex has the following goals:

• Breakdown of the cellulose-hemicellulose matrix

• Reduction of the crystallinity degree of cellulose and increase of the fraction of amorphous cellulose

• Hydrolysis of hemicellulose

• Release and partial degradation of lignin

• Increase of the biomass porosity

In addition, the pretreatment should contribute to the formation of sugars (hexo — ses and pentoses) through the hemicellulose hydrolysis or to the ability to form glucose during the subsequent enzymatic hydrolysis of cellulose. The pretreatment should also avoid the formation of by-products inhibiting the subsequent biopro­cesses. As a complement, the pretreatment avoids the need of reducing the biomass particle size, a very energy-consuming process. The efficiency of this process is evidenced by the fact that, when the biomass is not pretreated, glucose yields dur­ing the following cellulose hydrolysis step are less than 20% of theoretical yields, whereas the yields after the pretreatment often exceed 90% of the theoretical yields (Lynd, 1996). In this way, the pretreatment is a crucial step during the overall process for fuel ethanol production from lignocellulosic materials. However, the pretreatment is one of the most expensive steps: the unit costs of pretreatment can reach 30 cents per gallon of produced ethanol (about US$0.08/L EtOH), according to Mosier et al. (2005b). Nevertheless, the improvement of pretreatment has a great potential to reduce its costs and increase the efficiency of the overall process.

Different methods have been developed for pretreatment of lignocellulosic bio­mass, which can have physical, chemical, physical-chemical, or biological nature (Sanchez and Cardona, 2008; Sun and Cheng, 2002). The evaluation of each one of these methods is related to whether it meets all the goals mentioned above, in addition to other features involving technoeconomic criteria, such as the cost of the agent or catalyst employed, the possibility of recycling the agents or catalysts involved, the degree of technological maturity of each method, the possibility of generating lignin as a co-product, and the ability of each method to be applied to the maximum possible amount of lignocellulosic materials.

One of the strategies employed to improve the ethanolic fermentation is the uti­lization of immobilized cells. Cell immobilization consists of the attachment of cells into a support or a location in a defined space in order to utilize, in a con­trolled way, their capacity to accomplish biological transformation. Thus, the cells do not leave the bioreactor, and continuous fermentation processes can be imple­mented. In this case, the substrates contained in the feed stream are transformed into products in the biocatalyst (cells + support) bed. These products abandon the system in the cell-free effluent stream. This leads to an easier product recovery as well as avoiding the risk of cell washout. A better control of the fermentation pro­cess is achieved compared to suspended cell cultivation for which microbial cells are continuously removed from the fermenter. On the other hand, the biocatalyst can be readily recovered if the process is carried out in batch regime. All of these advantages make reactors with immobilized cells exhibit higher productivities allowing the utilization of smaller bioreactors (lower capital costs).

However, employing immobilized cells implies that they do not reproduce dur­ing reactor operation. The growth means that cell layers are accumulated on the support surface until the moment they start to deattach from the solid phase lead­ing to the system destabilization. To avoid this, the necessary conditions for the cells not to grow (nonviable cells) are ensured. In addition, if aeration is needed, a constant air supply has to be available, which can be difficult when a fixed bed

FIGURE 7.5 Most employed configurations for bioreactors with immobilized cells: (a) fixed-bed reactor, (b) fluidized-bed reactor.

of biocatalysts is used (Figure 7.5a). For this reason, an auxiliary tank is used to supply air to part of the effluent stream, which is then recirculated to the reactor to ensure the aerobic conditions of the culture broth within the bed. One alternative configuration is the fluidized-bed reactor where the liquid feed stream flows up inside the reactor containing mobile biocatalyst particles. In this way, the bed is expanded, as shown in Figure 7.5b. If aerobic conditions are required, the air can be directly injected into the bioreactor. Despite these advantages, processes using immobilized cells are not widespread in industrial microbiology today due to the complexity of the systems involved.

In the case of ethanolic fermentation, the implementation of continuous cul­tivation with immobilized cells can make possible processes with higher yields, greater productivities, and increased cell concentrations at the same time (Claassen et al., 1999), as presented in Table 7.2. Nevertheless, ethanol concentrations in the effluent tend to be lower than in other variants of continuous processes (see Table 7.1). Microbial cells for ethanol production are immobilized by entrapping within them porous, solid supports, such as calcium or sodium alginate, carra­geenan or polyacrylamide. In addition, they can be adsorbed on the surface of materials, such as wood chips, bricks, synthetic polymers, or other materials with a large surface area (Gong et al., 1999). It is remarkable that support particles have influence on cellular metabolism, as has been shown in the case of solid-state fermentation, biofilm reactors, and immobilized cell reactors. Prakasham et al.

Some Continuous Processes for Bioethanol Production from Sugarcane and Related Media Using immobilized Cells

a 120 g/L of reducing sugars. b 200 g/L + nutrients.

c High test molasses supplemented with ammonium sulfate. d Higher values correspond to acid treated and clarified molasses. e Measured in g EtOH/(1011 cells. h). f Multistage fluidized-bed reactor.

(1999) claim that the simple addition of a small fraction of solids in submerged cultures facilitates cell anchorage. This kind of adhesion enhances the metabolic activity and is an easier and more economical method than the immobilization of cells. In batch flasks cultures, these authors showed that materials, such as river sand, delignified sawdust, chitin, and chitosan, make possible the adhesion of S. cerevisiae cells leading to higher ethanol production in comparison to free cells. Thus, the application of these techniques of “passive immobilization” to continu­ous cultures should be experimentally tested. Nowadays, most of the configura­tions using immobilized cells are, so far, used in commercial operations. Hence, preliminary design and simulation of this type of process could become a very useful tool for defining new research lines at pilot and semi-industrial levels con­sidering the overall bioethanol production process (Sanchez and Cardona, 2008).