In this chapter, the different configurations for fuel ethanol production employing the three most important types of raw materials (sucrose-containing, starchy, and lignocellulosic materials) are presented. In particular, such configurations involv­ing integrated processes are discussed. The role played by process systems engi­neering during the definition and development of the diverse process flowsheets is emphasized. Finally, examples of process synthesis procedures applied to ethanol production are presented.

11.1 ETHANOL PRODUCTION FROM

SUCROSE-CONTAINING MATERIALS

Average ethanol yields from sucrose-containing feedstocks based on sugarcane can reach 70 L/ton cane and 9 L/ton of C-grade molasses (in addition to about 100 kg of sugar; Moreira, 2000). The most used fermenting microorganism is Saccharomyces cerevisiae due to its ability to hydrolyze cane sucrose for conver­sion to glucose and fructose, two easily assimilable hexoses. Fermentation pH is 4 to 5 and temperature is 30° to 35°C. Ethanol in Brazil is obtained from sugar­cane, and the country is the world’s leading producer, followed by India. About 80% of ethanol in Brazil is produced from fresh sugarcane juice and the remain­ing percentage from molasses (Wilkie et al., 2000). Sugar cane molasses is the main feedstock for ethanol production in India (Cardona and Sanchez, 2007). In Colombia, different sucrose-containing streams are used to produce fuel ethanol, especially the B-grade molasses. Berg (2001) indicates that the output/input ratio of energy for ethanol production from cane is the highest among the main types of feedstocks, reaching a value of 8. This indicator expresses the ratio between the energy released during the combustion of ethanol and the energy required for its production using the whole life cycle of the product from extraction of raw materials until the transformation process producing ethanol (Sanchez and Cardona, 2005).

In general, the process for ethanol production for sugarcane includes the extrac­tion and conditioning of cane juice to make it more assimilable by yeasts during

fermentation. From the resulting culture broth, cell biomass is separated and then the concentration of ethanol and its dehydration are carried out employing differ­ent unit operations. The product is the anhydrous ethanol that is the trade form in which fuel ethanol is utilized as a gasoline oxygenate. This process can utilize not only the crushed cane but also cane molasses as a feedstock as well as other streams with high content of fermentable sugars derived from the process for cane sugar production in sugar mills, as mentioned in Chapter 3, Section 3.1.5 (for example, a fraction of the clarified syrup). In the latter case, ethanol production facilities are located next to sugar mills, as in the case of Colombian distilleries. In the former case, distilleries can operate in an independent way as in Brazil, where an important number of autonomous (stand-alone) distilleries employing cane juice are currently in operation.

The overall process for fuel ethanol production from sugarcane in autonomous distilleries is shown in Figure 11.1. Production process in a distillery co-located at a sugar mill differs from the autonomous distilleries in the first steps (cleaning, milling, clarification). After these steps, the process is almost the same, although it is necessary to condition the molasses before fermentation. Fuel ethanol pro­duction process from sugar beet is similar to the process from sugarcane. Culture broth (fermented wort) is extracted from fermenters and sent to centrifuges for cell biomass separation. The removed microorganisms can be reutilized in the fermentation step. The obtained wine is directed to the first distillation column (concentration column) where the ethanol concentration of the wine is increased. Exhaust gas from fermenters having a fraction of volatilized ethanol is collected and sent to the scrubber. In this unit, ethanol is dissolved in a water stream, where a dilute alcoholic solution is obtained that is also sent to the first distillation col­umn. The resulting ethanol-enriched stream is fed to the second distillation col­umn (rectification column) whose distillate has a high ethanol concentration (near

96% by weight). This stream is sent through the dehydration step that can be car­ried out using different technologies. In the flowsheet depicted in Figure 11.1, it is indicated that the ethanol dehydration is performed by adsorption with molec­ular sieves. The bottoms of the concentration column or stillage (vinasses) are directed to the effluent treatment step where they are generally evaporated for their concentration.

The fermentation step is central to the overall process for fuel ethanol produc­tion because it represents the transformation of sugar-containing raw materials into ethyl alcohol employing yeasts or other ethanol-producing microorganisms. Ethanolic fermentation technologies of sucrose-based media, mainly cane juice and cane or beet molasses, can be considered relatively mature, especially if they are operated batchwise. However, many research efforts are being made worldwide in order to improve the efficiency of the process. In particular, these efforts are aimed at increasing conversion of the feedstock and ethanol produc­tivity, and at reducing production costs, especially energy costs. The features of ethanolic fermentation of sucrose-containing materials are discussed in Chapter 7, Section 7.1.2.

Process simulation plays a crucial role during the analysis of the technical, economic, and environmental performance of fuel ethanol production from sucrose-containing materials. In addition, simulation tools are very significant when process synthesis procedures are being applied, particularly when the knowledge-based process synthesis approach is employed (see Chapter 2). Thus, if the hierarchical decomposition procedure is applied, process simulation can provide the necessary data on the process behavior in order to select or discard the different alternatives proposed during each hierarchical level of analysis (see, for example, the work of Sanchez, 2008).

Process engineering could provide the means to develop economically viable and environmentally friendly technologies for the production of fuel ethanol. Process synthesis will play a very important role in the evaluation of different technologi­cal proposals, especially those related to the integration of reaction-separation processes, which could have major effects on the economy globally. Similarly, the integration of different chemical and biological processes for the complete

Research Trends and Priorities for improving Fuel Ethanol Production from Different Feedstocks

TABLE 13.1 (Continued) Research Trends and Priorities for improving Fuel Ethanol Production from Different Feedstocks
Issue An feedstocks a sucrose-Containing Materials starchy Materials Hydrolysis Low temperature digestion of starch	lignocellulosic Biomass Increase in specific activity, thermal stability and cellulose-specific binding of cellulases (e. g., by protein engineering) Reduction of costs of cellulases production (10-fold reduction) Cellulases production by solid-state fermentation Recycling of cellulases Improvement of acid hydrolysis of MSW Increase in conversion of glucose and pentoses to ethanol Recombinant strains with increased stability and efficiency for assimilating hexoses and pentoses, and for working at higher temperatures Development of strains more tolerant to the inhibitors Increase of ethanol tolerance in pentose-fermenting microorganisms
Fermentation Continuous fermentation with high cell density and increased yields and productivity
Reduction of inhibition by ethanol Microorganisms with increased osmotolerance or flocculating properties	Recombinant strains of yeasts with increased productivity and ethanol tolerance High cell-density fermentation (e. g., immobilized cells, flocculating yeasts, membrane reactors) Very high gravity fermentations
Refers to the three analyzed groups of feedstock: sucrose-containing materials, starchy materials, and lignocellulosic biomass.

utilization of the feedstocks should lead to the development of large biorefineries that allow the production of large amounts of fuel ethanol and many other valu­able co-products at smaller volumes, improving the overall economical effective­ness of the conversion of a given raw material. Integration opportunities may provide the way for qualitative and quantitative improvement of the process so that not only technoeconomical, but also environmental criteria can be met.

The increasing energy requirements of the world’s population will augment the pressure on R&D centers, both public and private, for finding new renew­able sources of energy and for optimizing their production and utilization. The use of bioethanol as an energy source requires that the technology for its pro­duction from lignocellulosic biomass be fully developed by the middle of this century. This need is much more urgent for those countries that do not have the agroecological conditions for the cultivation of energy-rich crops like sugarcane, as is true with North American and European countries. Even from governmen­tal biofuel programs in the United States, the retrofitting of the ethanol industry from corn starch to lignocellulosic residues (corn stover, woody materials, and municipal solid waste) has been recommended. Some countries, such as Brazil and Colombia, are in an excellent situation in this field considering the great availability of the three types of analyzed feedstocks. Although the more logical option is sugarcane, social benefits for rural communities when other alternative feedstocks, such as cassava or typical agricultural and tropical residues, are taken into account. Here, the process engineering strategy for assessing the real possi­bilities of tropical countries to develop fuel ethanol production is a real and rapid approach to be used by governments, investors, and decision makers.

Current development of the ethanol industry shows that complex technical problems affecting the indicators of global process have not been properly solved. The growing cost of energy, the design of more intensive and compact processes, and the concern of the populace about the environment have forced the necessity of employing totally new and integrated approaches for the design and opera­tion of bioethanol production processes, quite different from those utilized for the operation of the old refineries. The spectrum of objectives and constraints that should be taken into account for the development of technologies for bio­fuels production grows wider and more diverse. The socioeconomic component involved during the production of biofuels in the global context should be noted. Practically every country can produce its own biofuel. In this way, the feedstock supply for ethanol production is “decentralized” and does not coincide with the supply centers of fossil fuels. This would make it possible for countries that are now dependent on oil to use biofuels at a high scale and, thus decrease their dependency on fossil fuels. In addition, human development indexes could be improved in two ways: the creation of new rural jobs and the reduction of gas emissions that produce a greenhouse effect. However, ethanol production costs are higher than those of the fossil fuels, especially in the case of biomass ethanol. Nevertheless, during the past two years, oil prices have persistently increased. There is no doubt that the price of gasoline and other oil-derived fuels have a sub­sidy paid by all taxpayers of the world and that is not necessarily made effective in gas stations. This “subsidy” is intended to compensate the inversions made for maintaining the status quo of international relationships. Logically, we also pay the consequences of the measures taken to “offset” this state of affairs: social instability and, unfortunately, to a certain degree, terrorism.

Therefore, the relatively higher production cost of ethanol is the main obstacle to be overcome. To undertake this, process engineering plays a central role for the generation, design, analysis, and implementation of technologies improving the indexes of the global process, or for the retrofitting of employed bioprocesses. Undoubtedly, process intensification through integration of different phenomena and unit operations, as well as the implementation of consolidated bioprocess­ing of different feedstocks into ethanol (that requires the development of tailored recombinant microorganisms), will offer the most significant outcomes during the search for efficiency in fuel ethanol production. Great efforts should be focused on the development of consolidated bioprocessing (CBP) of biomass, as ligno — cellulosics is the most promising feedstock for ethanol production. Additionally, the intensification of biological processes indicates a better utilization of the feedstocks and the reduction of process effluents improving the environmental performance of the proposed configurations. Attaining this set of goals is a colos­sal challenge to be faced through the fruitful interaction between biotechnology and chemical engineering. The most important and promising research priorities linked to process engineering for improving the global process are briefly sum­marized in Table 13.2.

Finally, regarding process engineering, this approach has more importance today when oil prices can change drastically depending on not-easy-to-predict factors. Then bioethanol “fashion” should be supported by numbers and projec­tions based on serious studies.

The mathematical complexity inherent to this synthesis strategy is related to the nondifferentiable, discontinuous, and nonconvex nature of the resulting MINLP problems. However, new optimization techniques have been developed in the last decade that, along with the great advances in computational resources, have become more feasible in the utilization of this approach for process synthesis (Barnicki and Siirola, 2004), although the difficulty in the case of very large and complex synthesis problems persists.

One of the concepts related to the solution of MINLP-type problems for pro­cess synthesis is shown in Figure 2.1. For instance, this strategy has been used by Floudas (1995). After defining the superstructure containing all the possible process units for transformation of a given feedstock into a specific end prod­uct (and that includes all the possible connections among these units), an opti­mization loop that employs mixed-integer linear programming (MILP) tools is started. During the calculation of this optimization loop, the streams connecting the process units of the first configuration to be evaluated are chosen. This is done by specifying the value of the integer (discrete) variables (for example, 1 if the stream connects two units, 0 if this stream does not exist). The selected technological configuration represents the model of the process, which is involved in a nonlinear programming (NLP) loop. In this loop, the optimal values of the continuous process variables (temperatures, pressures, flowrates, compositions, etc.) are determined completing the optimization of the evaluated process model. Once completing the optimization of this first configuration by NLP, a second configuration of the technological scheme is defined by varying the values of the discrete variables, i. e., by choosing new connection streams among the process units, and the procedure is repeated. In this way, the different configurations of the process flowsheets are selected in an outer loop represented by the MILP

algorithm, whereas the optimal values of the operating parameters are found in the inner loop based on NLP. The best technological configuration of the process that maximizes or minimizes the employed objective function is identified by repeating and executing these two loops. For this configuration, the optimal val­ues of its operating parameters are defined as well.

The solution of NLP problems is usually based either on successive quadratic programming (SQP) or on reduced gradient methods. Among the main solu­tion methods of MINLP problems are the branch and bound method (Gupta and Ravindran, 1985), generalized Benders decomposition (Geoffrion 1972), and outer approximation (Duran and Grossmann, 1986). A new trend for solving MINLP problems is the generalized disjunctive programming whose formulation includes the condition that one of a set of three types of constraints should be exactly satis­fied (Lee and Grossmann, 2005; Raman and Grossmann, 1991). The three types of constraints comprise global independent inequalities concerning discrete deci­sions, disjunctions that are conditional constraints involving the operator OR, and logic pure constraints involving Boolean variables (Grossmann et al., 2000).

The formulation and solution of the main types of mathematical program­ming problems can be accomplished in an effective way using specialized com­puter programs, such as GAMS (Generic Algebraic Modeling System; GAMS Development Corporation, 2007). This system requires that the models and the formulation of the optimization problem be explicitly introduced in algebraic form. It automatically creates an interface with the solution codes for various types of problems (solvers). This offers a great advantage because it makes its
main efforts to formulate the problem itself and not to develop methods for solv­ing it. GAMS has a powerful set of solvers for different optimization problems (LP, NLP, MILP, and MINLP, among others). Moreover, it makes possible the input of indexed equations that is very useful in the case of large-sized models.

The NLP methods ensure that the global optimum can be found if and only if both the objective function and the constraints are convex (Floudas, 1995). If this is not the case, the location of the global solution cannot be guaranteed. The rigor­ous or deterministic methods for global optimization ensure an arbitrarily close approximation to the global optimum and, in addition, carry out the verification if this approximation has been attained. These methods include the branch and bound method, methods based on interval arithmetic (Byrne and Bogle, 1996; Stadherr, 1997) and generalized disjunctive programming (Lee and Grossmann, 2005), and procedures with multiple starting points in which a local optimizer is invoked from these points (Edgar et al., 2001). In the past few years, signifi­cant advances in the methods of rigorous global optimization have been achieved. These methods assume that special structures in these types of problems are pres­ent, such as bilinear, linear, fractioned, and separable concave functions. It has been demonstrated that the algebraic models always can be reduced to these sim­pler structures if they do not involve trigonometric functions (Grossmann et al.,

2000) . Floudas (2005) points out that the most important advances in determinis­tic global optimization belong to the following categories: convex envelopes and convex underestimators, twice continuously differentiable constrained nonlinear optimization problems, mixed-integer nonlinear optimization problems, bilevel nonlinear optimization problems, optimization problems with differential-alge­braic equations, grey-box and factorable models, and enclosure of all solutions.

Other types of techniques for global optimization correspond to nonrigorous or heuristic search methods. These methods can find the global optima, but do not guarantee and generally are not able to prove that the global solution is found even if they do so. However, these procedures often find good solutions and can be successfully applied to MINLP problems. In such cases, the heuristic method starts with a starting solution and explores all the solutions in a certain vicinity to that starting point, looking for a better solution. The method repeats the pro­cedure every time a better solution is found. The metaheuristic methods direct and improve the search with a heuristic algorithm. Tabu search, sparse search, simulated annealing, and genetic algorithms belong to this category (Edgar et al.,

2001) . In particular, stochastic methods, such as simulated annealing (Kirkpatrick et al., 1983) and the genetic algorithms (Goldberg, 1989), have gained more and more popularity, do not make any assumptions on the form of the functions, and require some type of discretization, and the violation of the constraints are tack­led through penalization functions (Grossmann et al., 2000).

One of the main problems during the pretreatment and hydrolysis of lignocel — lulosic biomass lies in the big differences found in its content of both lignin and hemicellulose. This content depends not only on the plant species from which the lignocellulosic materials were obtained, but also on crop age, method of harvest­ing, etc. This means that none of the pretreatment methods could be applied in a generic way for the great amount of potential feedstocks (Claassen et al., 1999). This justifies the need for a detailed analysis of all pretreatment options for dif­ferent materials, conditions, and regions. With this aim, process synthesis can provide the necessary tools for discarding, in a preliminary way, the less promis­ing pretreatment options and considering new procedures, schemes, and alterna­tives proposed during the conceptual design involving all the steps of the biomass processing as well.

The significant variety of pretreatment methods of biomass has led to the devel­opment of many flowsheet options for ethanol production (Cardona and Sanchez, 2007). Von Sivers and Zacchi (1995) analyze three pretreatment processes for etha­nol production from pine: concentrated acid hydrolysis, two-stage hydrolysis by steam explosion using SO2, and dilute acid and steam explosion using SO2 followed by the enzymatic hydrolysis. Through sensitivity analysis, these authors show that

TABLE 4.5 Biological Methods for Pretreatment of Lignocellulosic Biomass for Ethanol Production
examples of Pretreated Materials Corn stover, wheat straw
Remarks Fungi produces cellulases, hemicellulases, and lignin-degrading enzymes: ligninases, lignin, peroxidases, polyphenoloxidases, laccase and quinone-reducing enzymes Very slow process: Pleurotus ostreatus converts 35% of wheat straw into reducing sugars in 5 weeks Brown-rot fungi degrades cellulose White- and soft-rot fungi degrade cellulose and lignin Fungi decompose the lignin network Ethanol action allows hemicellulose hydrolysis Biological pretreatment can save 15% of the electricity needed for ethanolysis Ethanol can be reused; environmentally friendly process	References Sun and Cheng (2002); Tengerdy and Szakacs (2003)
Methods ProcedureMgents Fungal pretreatment Brown-, white — and soft-rot fungi Cellulase and hemicellulase production by solid-state fermentation of biomass
Ceriporiopsis subvermispora for 2-8 weeks followed by ethanolysis at 140-200°C for 2 h
Beechwood	Itoh et al (2003)
Bioorganosolv pretreatment
Source: Adapted from Sanchez, O. J., and C. A. Cardona. 2008. Bioresource Technology 99:5270-5295. Elsevier Ltd.

none of the processes can be discarded as less reliable. Milling has been suggested as a sole pretreatment method before the cellulose hydrolysis since the required equipment is less expensive than the equipment needed for other pretreatment methods such as steam explosion or ammonia fiber explosion (AFEX) process, which can account for 6 to 20% of the capital costs of the process. In contrast, milling equipment accounts for about 1% of these costs. However, it is considered that milling has elevated energy costs. Alvo and Belkacemi (1997) point out that milling of perennial grasses requires much less energy that milling of wood. These authors consider that milling as a unique pretreatment method should not be dis­carded as an option taking into account the advantages of this configuration: toxic products of degradation are not formed, soluble carbohydrates of the initial biomass are not destroyed, and many rural communities can acquire an easier way to mill in comparison to other expensive pretreatment equipment. This alternative should be evaluated in depth, utilizing simulation and optimization tools in the design step.

Dilute-acid pretreatment is the most studied method in the world along with steam explosion since they have a major probability of being implemented at an industrial scale in the near future. In fact, the utilization of dilute acids is con­sidered one of the most mature technologies compared to the rest of biomass pretreatment methods. The NREL (National Renewable Energy Laboratory) of the U. S. Department of Energy, which is one of the institutions leading the research and industrial development of technologies for fuel ethanol production from lignocellulosic materials in the United States, has chosen the dilute-acid pretreatment as the best model process to have been developed in the past years and offered to the industry (Aden et al., 2002; Wooley et al., 1999). The main advantage of this method compared to steam explosion is the higher recovery of sugars derived from the hydrolyzed hemicellulose. In the case of hardwood, about 80% of sugars can be recovered using dilute sulfuric acid while this recovery does not reach 65% when steam explosion is used (Lynd, 1996). The higher the sugars recovery, the greater the monosaccharide content in the liquid fraction resulting from the pretreatment. This liquid fraction can be employed as a culture medium for pentose-assimilating yeasts, or can be added to the bioreactor where the fer­mentation of cellulose hydrolyzates is accomplished as an additional sugar source (see the following chapters).

Another prospective method is the pretreatment by LHW. In particular, steam explosion and LHW processes have been compared in the case of poplar biomass obtaining better results for the latter method (Negro et al., 2003). In general, it is considered that the most efficient and promising methods are dilute-acid pretreatment, steam explosion with the addition of acid catalysts, and the LHW method (Ogier et al., 1999). To this regard, the evaluation of the global process for fuel ethanol production from lignocellulosic materials has been performed by Hamelinck et al. (2005) and the above-mentioned promising pretreatment methods should be noted. These authors selected one of the three pretreatment methods and diverse biological conversion technologies according to three differ­ent stages of technological maturity and development of the operations involved. For this, they employed spreadsheets and commercial simulators to study the

Process Synthesis for Fuel Ethanol Production

configuration of the process flowsheet corresponding to each one of three ana­lyzed scenarios (short-, mid-, and long-term variants). For the pretreatment step and short-term scenario (five years), the dilute-acid pretreatment was selected considering that this method is the technology offering the highest efficiency and reliability at the moment, while for the mid-term scenario (10 to 15 years), the steam explosion was chosen. For the long-term scenario, the LHW method was analyzed due to its comparative advantages and considering that this technology, for a period of time greater than 15 years, will be completely developed and that the current drawbacks will be overcome. Undoubtedly, the evaluation through the simulation of these alternatives will provide more insight for the selection of the best configuration of the overall process flowsheet. This will allow, in turn, the definition of the main research and development directions for the design of more effective pretreatment methods.

Process simulation requires suitable models for describing the studied pro­cesses. Considering process synthesis procedures, the mathematical modeling can allow a deeper insight into the pretreatment methods and make possible the defi­nition of operating parameters for which the system attains a better performance. In particular, mathematical modeling is a valuable tool for planning and execut­ing different trials at pilot and industrial scales (Cardona and Sanchez, 2007). For instance, through a kinetic model of cane bagasse pretreatment using nitric acid, the best conditions for increasing sugar yields were predicted. In this spe­cific case, the model considered the formation of inhibitory compounds (furfural and acetic acid). The results obtained were better than when sulfuric acid was used (Rodriguez-Chong et al., 2004) or when no acid was used (Jacobsen and Wyman, 2002). This type of kinetic study also has been done for poplar wood, switchgrass, and corn stover treated with sulfuric acid (Esteghlalian et al., 1997), as well as for wheat straw using hydrochloric acid (Jimenez and Ferrer, 1991). The kinetic model of corn stover pretreatment at pilot scale was also used to determine the process conditions leading to the maximization of xylose yield (Schell et al., 2003). Malester et al. (1988) studied the kinetics of dilute-acid pretreatment of municipal solid waste (MSW) since the cellulosic materials are the main com­ponent of this potential feedstock for ethanol production. For this case, the major difficulty lies in the resistance of cellulose to be converted into fermentable sug­ars in the presence of acids. This is explained by the neutralizing capacity of the MSW over the acid that imposes an additional difficulty to the measurement of kinetic parameters of the process. Therefore, these authors proposed to measure this effect based on pH and not on the concentration of the employed acid. This capacity is inherent to other lignocellulosic materials like corn stover and saw­dust, so the acid concentration should be adjusted slightly in order to maintain the efficiency of this type of pretreatment (Esteghlalian et al., 1997).

The co-fermentation of lignocellulosic hydrolyzates represents another techno­logical option for utilizing all the sugars released during biomass pretreatment and cellulose hydrolysis. This kind of cultivation process is aimed at the com­plete assimilation of all the sugars resulting from lignocellulosic degradation by the microbial cells and consists of the employment of a mixture of two or more compatible microorganisms that assimilate both the hexoses and pentoses present in the medium. This means that the fermentation is carried out by a mixed culture. Some examples of mixed culture are summarized in Table 7.3. However, the use of mixed cultures presents the problem that microorganisms

utilizing only hexoses grow faster than pentose-utilizing microorganisms lead­ing to a more elevated conversion of hexoses into ethanol (Cardona and Sanchez, 2007). To solve this problem, the utilization of respiratory-deficient mutants of the hexose-fermenting microorganisms has been proposed. In this way, the fer­mentation and growth activities of the pentose-fermenting microorganisms are increased as they grow very slowly when cultivated along with rapid hexose-fer — menting yeasts. In addition, the presence of hexose-assimilating microorganisms allows the reduction of the catabolic repression exerted by glucose on the pen­tose consumption in pentose-assimilating microorganisms (Laplace et al., 1993). Considering the indicators for the process using only the glucose-assimilating bacterium Z. mobilis grown on the biomass hydrolyzate, the productivities of the mixed culture are less than those of the bacterium, but the yields are com­parable, which offers a space for further research (Delgenes et al., 1996). One of the additional problems in this kind of configuration is that pentose-fermenting yeasts present a greater inhibition by ethanol, which limits the use of concen­trated substrates in the system.

Another variant of co-fermentation consists of the utilization of a single micro­organism capable of assimilating both hexoses and pentoses in an optimal way allowing high conversion and ethanol yield. Although these microorganisms exist in nature (see previous section), their efficiency and ethanol conversion rates are reduced for the implementation of an industrial process. Hence, the addition to the culture medium of an enzyme transforming the xylose into xylulose (xylose- isomerase) has been proposed (see Table 7.3.). In this way, microorganisms exhib­iting high rates of conversion to ethanol and elevated yields (like S. cerevisiae) can assimilate the xylulose, involving it in the metabolic pathways leading to the ethanol biosynthesis (see Chapter 6, Figure 6.2). On the other hand, a high effi­ciency in the conversion to ethanol can be reached through the genetic modifica­tion of yeasts or bacteria already adapted to the ethanolic fermentation—a topic that was discussed in Chapter 6. The microorganisms most commonly modified for this purpose are S. cerevisiae and Z. mobilis to which genes encoding the assimilation of pentoses have been introduced (see Chapter 6, Table 6.3). The other approach for genetic modification is the introduction of genes encoding the metabolic pathways for ethanol production to microorganisms that are capable of fermenting both hexoses and pentoses in their native form. The “design” of etha — nologenic bacteria like E. coli or Klebsiella oxytoca is an example of such type of modification (see Chapter 6, Table 6.3). Using these recombinant microorganisms allows implementing the co-fermentation process intended to the more complete utilization of the sugars contained in the hydrolyzates of lignocellulosic biomass (Cardona and Sanchez, 2007).

The application of membrane technology has been aimed at the design of mem­branes that allow the recovery of either ethanol from water (as in the case of

technology Continuous fermentation coupled with stripping	Bioagent/unit operation Saccharomyces cerevisiael ethanol stripping with CO2			references Taylor et al (1996, 1998, 2000)
S. uvarumlethanol stripping with CO2 Kluyveromyces marxianus/ Pichia stipitis/ethanol stripping with CO2 S. cerevisiae or Zymomonas mobilislethanol stripping with CO2
ALSA MSCRS	Glucose Lignocellulosic biomass (oat hulls)	Gong et al. (1999) Dale and Moelhman (2001)
MSCRS	Gelatinized starch	Dale (1992)
Source: Modified from Cardona, C. A., and O. J. Sanchez. 2007. Bioresource Technology 98:2415-2457. Elsevier Ltd. Note: ALSA = Airlift reactor with a side arm, MSCRS = Multistage continuous reactor-separator.

membrane modules coupled to fermenters) or water from ethanol (as in the opera­tion of pervaporation during ethanol dehydration). Membranes make possible the removal of ethanol from a culture broth, which neutralizes the inhibitory effect of ethanol on microorganisms. Most of integrated schemes of this kind correspond to membrane modules coupled to fermenters. The use of ceramic membranes located inside the fermenter has been proposed, although most of these systems have been studied only on a laboratory scale. These laboratory configurations have shown interesting results, but their implementation at an industrial scale can be very dif­ficult. The utilization of ceramic membranes has been proposed for the filtration of cell biomass and the removal of ethanol during the fermentation (Ohashi et al., 1998). The removed ethanol is distilled and the obtained bottoms are recycled back to the culture broth resulting in a drastic reduction of generated wastewater. This configuration uses a stirred ceramic membrane reactor (SCMR). In the same way, immobilized cells can be used in order to allow an easier separation of etha­nol and the recirculation of distillation bottoms to the reactor (Kishimoto et al., 1997). Kobayashi et al. (1995) developed a mathematical model for optimization of temperature profiling during the batch operation of a fermenter coupled with a hollow-fiber module. The temperature was kept initially at 30°C descending later to 20°C and attaining higher ethanol concentration and productivity. However, it is necessary to analyze the scalability of these configurations due to their complexi­ties (immobilization, presence of membranes, recirculation, repeated batches) and taking into account that no mathematical description has been presented (Cardona and Sanchez, 2007). The utilization of liquid membranes (porous material with an organic liquid) in schemes involving the extraction of ethanol by the organic phase and the reextraction with a liquid stripping phase used as an extractant (perstrac- tion or membrane-aided solvent extraction) or gaseous stripping phase have been also coupled to the fermentation process showing the increased effectiveness of the latter configuration (Cardona and Sanchez, 2007; Christen et al., 1990). Some of these configurations are summarized in Table 9.8.

Pervaporation has offered new possibilities for integration, as evidenced in Table 9.8. The coupling of fermentation with the pervaporation allows the remov­ing of produced ethanol (Figure 9.10), reducing the natural inhibition of the cell growth caused by high concentrations of ethyl alcohol (Cardona and Sanchez, 2007). Nomura et al. (2002) observed that the separation factor of silicalite zeolite membranes used for continuous pervaporation of fermentation broth was higher than the corresponding value for ethanol-water mixtures due to the presence of salts that enhance the ethanol selectivity. Ikegami et al. (2003, 2004) employed this same kind of membrane coated with two types of silicone rubber or covered with a silicone rubber sheet as a hydrophobic material for obtaining concentrated solutions of ethanol. The coupling of T. thermohydrosulfuricum that directly converts uncooked starch into ethanol with pervaporation has also been tested obtaining ethanol concentrations in the permeate of 27 to 32% w/w (Mori and Inaba, 1990).

O’Brien et al. (2000) employed process simulation tools (Aspen Plus) for evaluating the costs of the global process involving fermentation-pervaporation

Reaction-Separation Integration for Alcoholic Fermentation Processes through Ethanol Removal by Using Membranes

Free or immobilized S. cerevisiae/distillation

Glucose-containing medium

Batch fermentation coupled with continuous pervaporation

Batch co-fermentation coupled with continuous pervaporation

Fed-batch fermentation coupled with pervaporation

Continuous fermentation coupled with pervaporation

S. cerevisiae/silicalite zeolite membrane

Pichia stipitisl polytetrafluoro-ethylen membrane

Immobilized S. cerevisiae in Ca alginate/microporous polypropylene membrane S. cerevisiae! commercial polydimetylsiloxane membranes

Immobilized S. cerevisiae on beads of PAAH gel coated with Ca alginate/ membrane of silicone composite on a polysulfone support

High cellular retention by ceramic membrane; recycling of distillation bottoms; 100 h of cultivation; without wastewater; productivity 13.1-14.5 g/ (L. h); EtOH cone. 20-50 g/L For 4.6 wt.% EtOH in the broth, EtOH in permeate reaches 81.7 wt.%; separation factor of membrane 88; up to 48 h of operation For 10 g/L EtOH in the broth, EtOH in permeate reaches 50 g/L; yield 0.43 g/g; 100 h of operation 72 h cultivation; EtOH cone. 50 g/L; yield 0.49 g/g; productivity 2.9 g/(L. h); 61.5% reduction in wastewater Aspen Plus simulation based on fermentation-pervaporation lab experiments; EtOH cone, in permeate 420 g/L; recycling of retentate to fermenter, reduction of cost associated with fermentation by 75%

For 4 wt.% EtOH cone, in the broth, EtOH in permeate reaches 12-20% wt.%; yield 0.36-0.41 g/g; productivity 20-30 g/(L. h); over 40 d of operation

Reaction-Separation Integration for Alcoholic Fermentation Processes through Ethanol Removal by Using Membranes

Source: Modified from Cardona, C. A., and O. J. Sanchez. 2007. Bioresource Technology 98:2415-2457. Elsevier Ltd.

Note: CMFS = continuous membrane fermentor-separator, HFMEF = hollow-fiber membrane extractive fermentor, PAAH = polyacrylamide hydrazide, SCMR = stirred ceramic membrane reactor.

Liquid

retentate PERVAPORATION

UNIT

Conc.

Gaseous EtOH permeate

To distillation
step

in comparison with the conventional batch process from starch. Fermentation — pervaporation was simulated based on experimental data from tests carried out during more than 200 h using commercial membranes of polydimethylsiloxane. Performed simulations revealed costs slightly higher for the coupled fermenta — tion-pervaporation process due to the capital and membrane costs. Nevertheless, fermentation costs were reduced 75% and distillation costs decreased signifi­cantly. Sensitivity analysis indicated that few improvements in membrane flux or selectivity could make this integrated process competitive (Cardona and Sanchez, 2007). Wu et al. (2005) have investigated the mass transfer coefficients for this type of membrane in the case of pervaporation of fermentation broths showing that active yeast cells were favorable for ethanol recovery. Kargupta et al. (1998) carried out the simulation of continuous membrane fermenter separator (CMFS) removing ethanol by pervaporation in a membrane reactor, which is coupled with a cell separator in order to increase the concentration of cells inside the reactor by recycling them. The models predicted an increase in productivity because this system could be operated at high dilution rates as a consequence of in situ product removal and higher cell concentrations.

Besides pervaporation, membrane distillation has been studied (see Table 9.8). In this type of distillation, aqueous solution is heated for the formation of vapors, which go through a hydrophobic porous membrane favoring the pass of vapors of ethanol (which is more volatile) over the vapors of water. The process’s driv­ing force is the gradient of partial pressures mainly caused by the difference of temperatures across the membrane (Cardona and Sanchez, 2007). Gryta et al. (2000) implemented a batch fermenter coupled with a membrane distillation
module leading to the ethanol removal from culture broth diminishing the inhibi­tion effect and obtaining an increase in ethanol yield and productivities. Gryta (2001) points out that when a tubular fermenter working in a continuous regime is coupled with the membrane distillation module, higher increases in ethanol productivity can be achieved (up to 5.5 g/(L x h)). This author determined that the number of yeast cells that are deposited on the membrane is practically zero dur­ing the operation of these modules (Gryta, 2002). Calibo et al. (1989) also dem­onstrated the possibility of coupling the continuous fermentation with membrane distillation. They used a column fermenter, a cell settler, and a membrane mod­ule. This system operated during almost 700 h with a feed of molasses. Garcia — Payo et al. (2000) studied the influence of different parameters for the case of air gap membrane distillation based on the model of temperature polarization. It was observed that permeate flux increases in a quadratic way when ethanol con­centration increases in the membrane distillation module. Similarly, Banat and Simandl (1999) indicate that the effects of concentration and temperature polar­ization should be accounted for during the modeling of this process and highlight the need for optimizing it with respect to feed stream temperature. Banat et al. (1999) also analyzed different models based on Fick’s law and on the solution of Maxwell-Stefan equations for this type of distillation. Likewise, the characteris­tics of the vacuum membrane distillation (Izquierdo-Gil and Jonsson, 2003) and direct contact membrane distillation have been studied for the concentration of aqueous solutions of ethanol (Fujii et al., 1992a, 1992b). Without a doubt, these studies are of great interest considering the simulation of these integrated con­figurations (Cardona and Sanchez, 2007).

In the case of the bioethanol production from sugarcane, the integration of fermentation with pervaporation or vacuum membrane distillation can allow the recovery of a valuable product: the fructose. For this, mutant strains of yeasts without the capacity of assimilating this monosaccharide should be used. Thus, continuous ethanol removal through the membranes coupled to the fermenter makes possible the accumulation of fructose in the culture medium that can be recovered in an extraction column (Cardona and Sanchez, 2007). According to Di Luccio et al. (2002), the simulation of this process based on experimental data and semiempiric models for the evaluation of the required membranes area allowed performing of a preliminary economic analysis. This analysis showed that vari­able costs involving membrane area influence in a higher degree the viability of the process. The process is viable only if the cost of membranes is not greater than US$550/m2 for a new plant or US$800/m2 for an adapted plant considering an internal return rate of 17%.

In previous works (Cardona and Sanchez, 2004, 2006), the analysis of several inte­grated process flowsheets for production of fuel ethanol from lignocellulosic bio­mass was performed. The flowsheets were compared with a base case representing a nonintegrated configuration. The comparison criterion was the energy consump­tion defined as the thermal and electric energy demanded during the production of ethanol from biomass.

The different flowsheet configurations were simulated using Aspen Plus. Short­cut methods based on the principles of the topological thermodynamics (analysis of the statics, see Chapter 2) were employed for the synthesis of the distillation train as highlighted in Chapter 8, Case Study 8.1. The amount of feedstock (lignocellulosic biomass) was the same for every combination of process configurations (160,950 kg/h). Wood chips were analyzed as feedstock during the simulations. The analysis was made taking into account the best variants of each configuration assuming that no technological limitations were present for the proposed technologies. For example, it was assumed that the cellulases used for hydrolysis were purchased from commercial suppliers, which ensured their availability and efficiency. It was also assumed that SSF and SSCF processes were fully developed.

The considered overall process included all the steps required for ethanol pro­duction from pretreatment until effluent treatment. The defined nonintegrated base case is shown in Figure 11.12. This configuration comprised

• The pretreatment step using dilute sulfuric acid

• Detoxification step for the liquid fraction of pretreated biomass (hemicel — lulose hydrolyzate) through ionic exchange followed by alkali neutralization (not shown in Figure 11.12)

• Pentose fermentation using the xylose-assimilating yeast C. shehatae

• Enzymatic hydrolysis of cellulose contained in the solid fraction of pre­treated biomass

• Hexose (glucose) fermentation using S. cerevisiae

• Ethanol separation by distillation

• Ethanol dehydration by azeotropic distillation

• Effluent treatment step by evaporation of stillage with recovery of lignin

The alternative integrated configurations were synthesized through the combi­nation improvements in some of the steps making up the overall process. Thus, two types of pretreatment and hydrolysis schemes (with deviation of the liquid fraction

of hemicellulose hydrolyzate or without it); three types of fermentation processes (separate hexose and pentose fermentation, SSF, or SSCF); two types of separation technologies (azeotropic distillation or pervaporation); and three types of effluent treatment schemes (without recycling of water or with two alternatives for recy­cling water) were selected for the subsequent simulation. The selection procedure included the technologies that are more perspective considering the use of qualita­tive improvements of the process and the viability of their implementation. For example, all the analyzed configurations included the use of dilute sulfuric acid for the pretreatment of biomass. In this way, six alternative configurations were synthesized and analyzed (Table 11.11).

Data for the comparison of energy consumption for each configuration were obtained from the simulation results (Table 11.12). Considering the results shown, it was evident that those alternatives involving a higher degree of process integra­tion (SSCF, recirculation of water streams, coupling of distillation with pervapora- tion) presented lower energy costs. In particular, configuration 6 that included the SSCF process and ethanol dehydration by pervaporation had a 23% reduction in the energy consumption related to the nonintegrated base case. The second best

Process Configurations Considered during Process Simulation and Energy Analysis

Source: Cardona, C. A., and O. J. Sanchez. 2006. Energy 31:2447-2459. Elsevier Ltd. With permission.

Note: DA = dilute acid pretreatment; DLF = deviation of liquid fraction of hemicellulose hydrolyzate for pentose fermentation; Det = ion exchange detoxification; EH = enzymatic hydrolysis; HF = hexose fermentation; PF = pentose fermentation; SSF = simultaneous saccharification and fermentation; SSCF = simultaneous saccharification and co-fermentation; Dist = conventional distillation; Az = azeotropic distillation; Perv =- pervaporation; Ev = stillage evaporation; RW1 = recycling of water for washing hemicellulose hydrolyzate; RW2 = recycling of water for washing hemicellulose hydrolyzate and for pretreatment reactor. The symbol “V” indicates that a given step is included in the configuration.

TABLE 11.12

Comparison of Simulated Configurations according to Their Energy Consumption

Flowsheet Variant

Base Case Configuration 1 Configuration 2 Configuration 3 Configuration 4 Configuration 5 Configuration 6

Source:

unit energy Costs
(MJ/L ЕЮН)

34.84

33.12

28.56

27.83
28.37

27.84

26.84

energy Costs (% of the base case) 100.00 95.06 81.98 79.87 81.43 79.92

77.05

scheme corresponded to configuration 5, which had a higher degree of integration as well (Figure 11.13) and offered a 20% reduction in energy costs.

The effect of water recycling on the energy costs of the entire process should be noted. From the multiple recycling configurations, two basic schemes were selected. In the first case, the bottoms of the rectification column were mixed with a fraction of the liquid stream from centrifuge to utilize this combined stream for washing the hemicellulose hydrolyzate (configuration 4). This stream contains water and very small amounts of soluble compounds such as glucose, xylose, and acetic acid. The second case considered, besides the above-mentioned recycled water, the additional use of the evaporated water obtained in the effluent treatment step as process water for the pretreatment reactor (configurations 5 and 6), as sug­gested by Wooley et al. (1999).

The recycling of water has two main goals: (1) reduction of the amount of fresh water utilized in the process and (2) the increase of ethanol yield through more complete utilization of remaining fermentable sugars contained in the recycled wastewater. Increased yields lead to reduced energy consumption for producing the same amount of final product. In addition, the main effluent, the stillage from the first distillation column, resulted in more concentrates (10.1% solids) than the stillage corresponding to the base case (5.8% solids) as a consequence of the higher amounts of fresh water utilized throughout the latter process. Therefore, the recy­cling of water reduces the amount of water to be evaporated in the effluent and the cost of treatment of wastewater by subsequent treatment processes like anaero­bic digestion. Thus, the simulation shows that the energy consumption during the partial evaporation of wastewater can be reduced from 11.60 MJ/L EtOH for the base case to 7.58 MJ/L EtOH for configuration 5 (34.72% reduction) (Cardona and Sanchez, 2006).

This case study illustrates the advantages and possibilities that process simula­tion offers during the synthesis of technological schemes with a high energy perfor­mance. In addition, the information obtained through simulation can be the base for life-cycle analysis of processes for bioethanol production as exemplified below.

Bioethanol (ethyl alcohol, fuel ethanol) is the most-used liquid biofuel in the world. It is obtained from energy-rich crops, such as sugarcane and corn. Ethanol can be directly employed as a sole fuel in vehicles or as gasoline oxygenate increasing its oxygen content and allowing a better hydrocarbon oxidation that reduces the amount of aromatic compounds and carbon monoxide released into the atmo­sphere. For this reason, fuel grade ethanol (FGE) is the market with the most rapid growth rate in America and Europe.

The fuel ethanol can be obtained from lignocellulosic biomass as well, but its production is much more complex. Nowadays, great efforts are being made to diminish the production costs of lignocellulosic ethanol. It is expected that the evolution of biomass conversion technologies will allow the massive oxygenation of gasoline with fuel ethanol and make possible the substitution of a significant portion of fossil fuels considering the huge availability of lignocellulosic world­wide (Bull, 1994).

Sugarcane molasses is a by-product of sugar processing. It is generated during sugar crystallization by evaporation and represents the mother liquor from which the crystals are formed. Most cane molasses used for ethanol production corre­sponds to the C-molasses. Due to the repeated evaporation-centrifugation process, the molasses obtained is a viscous, thick brown liquid with a higher concentra­tion of impurities and mineral salts compared to cane juice. An example of the
average composition of cane molasses is presented in Table 3.6. The data in this table correspond to cane molasses from the United States, Colombia, and Costa Rica (Curtin, 1983; Fajardo and Sarmiento, 2007; Vega-Baudrit et al., 2007). This composition is quite variable because it depends on factors such as the type of soil, temperature, humidity, cane production season, cane variety, type of cane processing in the sugar mill, and storage conditions (Curtin, 1983). This implies great variations in the content of sugar and remaining nutrients in molasses as well as its color, viscosity, and flavor.

The molasses is commercialized based on its degrees Brix, which is an indi­cator of the specific gravity and represents an approximation to its total solid content. The degrees Brix were originally defined for solutions of pure sucrose indicating the percentage (by weight) of this sugar in the solution. However, and besides sucrose, molasses contains other sugars, such as glucose, fructose, and raffinose, as well as a variety of nonsugar organic compounds. In this way, the value of degrees Brix for molasses does not correspond to the content of sugars or total solids. Nevertheless, the degrees Brix are still being used for trading molasses. Cane molasses contains some vitamins that are crucial if considering the subsequent fermentation. For this reason, in general, it is not necessary to add vitamins to the culture medium based on cane molasses for fermentation with the yeasts of the Saccharomyces cerevisiae species. Biotin, pantothenic acid (in the form of pantothenate), and inositol are probably the three vitamins essential for the normal yeast growth. To this regard, the biotin is in excess in cane molasses, while the pantothenate is found in minimal amounts required for fermentation. The inositol is present in adequate amounts in cane molasses (Murtagh, 1995).

Microorganisms are the key component for conversion of different raw materi­als into ethyl alcohol during the fermentation step. The success of the overall fuel ethanol production process depends on the selected microbial strains. In this chapter, some metabolic features of microorganisms used for ethanol biosynthe­sis are discussed. The development of efficient, low-cost, and environmentally friendly processes for fuel ethanol production requires the selection of suitable microorganisms that contribute to achieving such goals. In addition, the devel­opment of microbial strains allowing innovative designs and making use of the wide availability of feedstock resources, especially lignocellulosic materials, is required. For this reason, the main strategies for genetic modification of microor­ganisms with better performance during ethanol production from different feed­stocks are discussed in this chapter.