Some European companies (Telschow, 2006; Chollet,

2011) advertise the application of special enzyme combi­nations in biogas digesters. A 30% faster digestion or a 10% higher biogas yield is reported.

Water cleanup secondary sludge is a source of enzymes. The secondary sludge consists mainly of bac­teria and the intracellular liquid of these bacteria con­tains lyses enzymes.

PRETREATMENTS

Biological Pretreatment with Enzymes

Shredded straws, bagasse and husks are seasonal products and need to be stored before being used as sub­strate in a digester. Storage with silage can be used to improve the biodigestability of the substrate. Methane yield for maize silage increased from 290l/kgVS to 330l/kgVS using the enzyme mixture Microfern (Bossuwe, 2011).

Methane yield increased from 145l/kgVS (fresh reed) to 200l/kgVS (reed silage prepared with the enzyme mixture Methaplus; Helbig, 2009). Komatsu et al. (2007) report an increase in methane yield from 280l/kgVS to 310l/kgVS for rice straw soaked in a solution of an unspecified enzyme codigested with sewage sludge.

Chemical Pretreatment

Lime (calcium hydroxide) is a relatively cheap chem­ical and calcium improves the fertility of the soil. In its production about 0.8kWh/kg high-temperature ther­mal energy is used. Gunnerson et al.; (1987) advise to compost straw with lime, water and dung. In this method a fraction of VS is lost. Raju et al., 2010 demon­strated an increase of 60% in biogas production using a pretreatment at 0.015 kg Ca(OH)2 per kilogram VS. The pretreatment with 1.5% CaOH is equivalent to an increase in retention time from 32 to 100 days (Moeller et al., 2006). Klopfenstein (1978) found for hemicellulose and cellulose an increase of 80% and 20%, respectively, for sodium hydroxide using corn­cobs as substrate. The yield increase was only 25% us­ing calcium hydroxide both for hemicellulose and cellulose.

Pretreatment with a minimum amount of dilute acids at 50—100 °C dissolves the hemicellulose and leaves a solid residue that is highly porous (Tsao,

1987) . German biogas tanks have an acid pretreatment (Sauter, 2012). Lebuhn et al. (2010) report technical dif­ficulties with the acid pretreatment and no increase in methane yield.

Schober et al. (2006) and Busch et al. (2006) describe an aerated percolation reactor followed by a methano — genese reactor. They report shorter retention times for kitchen and garden waste and maize silage compared to wet systems.

Hot Water Treatment

Raju et al. (2010) obtained a 40% increase in methane yield using a 15 min pretreatment of wheat and rape- seed straw at 75 °C.

Mechanical Pretreatment

Jerger et al., 1983 found an increase in the methane yield from 270l/kgVS for particles of hybrid poplar <8 mm to 310 l/kg VS for particles <0.8 mm. The dura­tion of the tests was 90 days. Slotyuk (Oechsner, 2012) found an increase from 230 l/kg VS for 10 mm wheat straw particles to 300 l/kg VS for 1 mm particles. The duration of the tests was 35 days (Table 13.4).

LONGER RETENTION TIMES

Doubling of the retention times increases the gas yield with 30—50% (Table 13.5). It is unfortunate that the tests were not done at optimum nutrient concentra­tions. Calculated yields for shorter retention times using Eqns (1) and (2) are compared with measured yields in Table 13.6. The standard deviation between measure­ment and calculation is 35 l/kg VS (similar to the corre­lation for longer retention times) (Table 13.7).

ENERGY CROPS

About 7% of the land used for agriculture in Germany is planted with maize destined for methane production. There are a number of other energy crops with higher production costs (Boese, 2010). Some of these crops have a higher methane yield per hectare (Table 13.8). The humus content of the soils will decrease when only maize is planted as crop (Willms et al., 2009).

Lignocellulosic biomass is the most abundant organic compound on Earth and represents the major portion of the world’s annual production of renewable biomass. The global biomass production is about 150 billion tons annually (Balat and Ayar, 2005). Carbohydrates are by far the most omnipresent compo­nent of lignocellulosic biomass and are therefore often the preferred feedstock for the biobased economy. In fermentative processes there is sometimes more room for feedstock flexibility (proteins, triglycerides /fatty acids) but in the case of catalytic conversions such as the transformation of biomass into furan molecules you are restricted to carbohydrates. Sources of carbohy­drates include conventional forestry, wood processing by-products (e. g. wood chips, sawdust, bark, pulp and paper industrial residue as black liquor), agricul­tural crops and surpluses (e. g. corn stover, wheat and rice straw), and so-called energy crops (e. g. switch — grass, Miscanthus, willow) grown on degraded soils and aquatic biomass (algae, seaweeds). In this chapter we will focus on lignocellulosic biomass. Typical carbohydrate compositions are shown in Table 17.1. The majority of lignocellulosic biomass consists of carbohydrates (60—80%); the other main compo­nent is lignin (20—25%); proteins are mainly found in fresh (green) plant material. Amounts of triglycerides, extractives and inorganic materials are very much species as well as harvest time dependent. The bulk of the carbohydrates present in biomass are composed of poly/oligosaccharides, such as

hemicelluloses, cellulose, starch, and inulin. Sucrose is an omnipresent disaccharide consisting of a glucose and fructose moiety, whereas monosaccharides such as glucose and fructose are present in far lesser amounts. In particular, lignocellulosic plant matter is available in large quantities and is relatively cheap while aquatic biomass is given great potential for the future.

The 13C NMR analysis of lignin has been considered as a very informative, but not very affordable method due to the very long experimental time originally required for quantitative lignin analysis (Chen and Robert, 1988). Recently, we have optimized the experimental time required for 13C NMR analysis and reduced it from 70 to 15—20 h (Capanema et al., 2004, 2005a). Thus, a large amount of valuable structural information (20—30 results on structural moieties per analyzed lignin sample) can be obtained in a reasonably short experimental time which permits considering the 13C NMR method as the most productive one in lignin analysis. Furthermore, we demonstrated that the use of a CryoProbe NMR (Bruker BioSpin MRI GmbH, Germany) allows for 1 h total quan­titative 13C NMR experimental time (Balakshin, Berlin et al., 2013). Therefore, 13C NMR cannot be considered a time consuming lignin analytical technique anymore. In addition, the CryoProbe yields much better signal res­olution, both for 13C and Heteronuclear Single Quantum Coherence (HSQC) NMR methods. However, currently the use of CryoProbe does require tedious and profes­sional optimization of acquisition and processing param­eters to adjust the baseline, specifically for 13C NMR spectra of lignin samples. Therefore, we cannot recom­mend yet the use of this method on a routine basis. Further development in the use of CryoProbe technology and its use for lignin analytical chemistry is expected to mitigate this limitation.

A careful optimization of the acquisition parameters for lignin analysis using a traditional probe yields 13C NMR spectra with a good and reproducible baseline, which can be easily and reliably corrected during spectra processing. This careful adjustment of the acqui­sition and processing parameters has enabled the recording of reproducible results even in different NMR spectrometers with a relative error of ca. 2—3% for the major lignin peaks. Unfortunately, it is not feasible for now to evaluate interlab variability of the 13C NMR method as it has been used much less often than the 31P NMR method and data for the same lignin preparations are very limited. It is also very important to consider some issues when calculating the amount of various lignin moieties using the original 13C NMR spectra as it has been discussed earlier (Capanema et al., 2005b). However, our team has been acquiring over the past few years significant information on different technical lignins, which is hereby summarized in Table 18.6. Since the data were produced and inter­preted by the same analytical methodology, their com­parison is more accurate than the comparisons based on data obtained from various literature reports. The analysis of technical lignins (Table 18.6) clearly showed dramatic changes in lignin structure resulting from the delignification process. In addition to between-process variations, certain within-process lignin structural changes could be documented. One of the most impor­tant factors in these variations is the feedstock origin. Significant differences in the structure of technical lig­nins from different tree species were obvious and they were significantly larger than the differences in the

Phenolic

structure observed for native lignins in these tree spe­cies. For instance, it was shown that various hardwood lignins degraded differently during kraft pulping result­ing in variations of hydroxyl and carboxyl groups, b-O — 4, Ь-Ь, and b-5 linkages as well as in S/G ratio and degree of condensation (Capanema et al., 2005b; Balak­shin et al., 2008). In fact, species-originated variations are similar or even larger than the variations in major lignin functionalities caused by different delignification technologies, such as kraft and OS processes. The only significant differences observed between kraft and ethanol OS lignins (as analyzed by 1-D NMR) are the incorporation of ethoxyl groups and the significantly higher amounts of carbonyl groups in the latter.

Most of the wet chemistry and 31P NMR methods originally yield results in mmol/g (or mass %) units. The 13C NMR method reports results in number of functional groups per aromatic ring (Ar). A conversion factor based on the C9-formulae is typically used to correlate these values (mmol/g and units/Ar), but the ratio is not obvious. The C9-formulae might not be ac­curate (even for high-purity lignins; contaminations would also contribute to this NMR signal) as the lignin side chain is degraded, to a certain extent, during biomass processing. The 13C NMR lignin analysis with Internal Standard (IS) allows for both types of data presentation. A very good correlation between 13C NMR with IS and 31P NMR data for the hydroxyl group content has been reported (Xia et al., 2001). How­ever, in that publication, the authors did not specify if a correction for lignin acetylation was applied to the 13C NMR data or not. The proportion between the values expressed "per 100 Ar" and those in mmol/g (for the same lignin) allow us to calculate the weight of an average C9-unit (or Ar). The numbers obtained are 271 and 243 for Aspen SE and Poplar SE lignin, corre­spondingly (Table 18.3). These values are much higher than those calculated based on the C9-formulae (195 and 193, correspondingly; Table 18.3) and indicate that the amounts of OH groups in the 13C-IS NMR ex­periments have been calculated based on acetylated lignin. Therefore, recalculation as per the original (non-derivatized) lignin would give numbers of ca. 25-40% higher for the 13C NMR (with IS) vs. 31P NMR-II. It should be mentioned that a very good corre­lation between 13C-NMR-IS data (for a non-acetylated lignin) and the methoxyl group wet chemistry analysis, one of the most reliable analytical methods in lignin chemistry, has been reported (Xia et al., 2001). This in­dicates that 13C NMR data should be considered as more realistic and that the 31P-II NMR method pro­duced significantly underestimated numbers (probably due to incomplete derivatization), in agreement with the earlier discussed results.

In summary, 13C NMR with IS is probably the best analytical approach to obtain the most comprehensive and reliable lignin structural information expressed, both in mol% (units/Ar) and in mmol/g. Unfortu­nately, very little has been reported so far on the meth­odology development and its validation of the 13C NMR lignin analysis with IS vs other analytical techniques.

Overall, the lignin scientific community believes, based on a few publications, that there is a good cor­relation between the different methods used for the analysis of the technical lignin chemical structures. However, a comprehensive review of the existing database (especially of independent publications) clearly shows that this is not the case. In fact, the de­viation between reported data using even the same analytical method (such as 31P NMR) for the same lignin preparation is often very significant. Moreover, the deviation between different analytical methods is in the range of the differences observed between different lignin types. This conclusion indicates that significant efforts still should be made to address these deviations and to standardize the analytical methodology for technical lignins analysis. Mean­while, we should remember the general principle that it is naturally more accurate to compare struc­tural data obtained with the same analytical method in the same lab. The use of "reference" lignin samples (well-investigated lignins, such as Alcell and Indulin

AT) would be also very beneficial to ensure at least a reliable relative comparison.

Oxygenic Photosynthesis

Cyanobacteria and green algae are photosynthetic microorganisms widespread in nature that survive even in extreme climatic conditions. They are able to harness solar energy and convert it into chemical energy by simul­taneous splitting of water to molecular oxygen and protons with following fixation of CO2, according to the general equation of photosynthesis:

6CO2 + 6H2O / 6(CH2O)+6O2 (21.1)

The photosynthetic electron transfer reactions are usually divided into two stages—the "light reactions", where light energy is converted into the chemical energy of strong reductants and the "dark reactions", where CO2 is reduced into organic compounds by using chemical energy obtained from the light reactions. The simplified scheme of photosynthetic light reactions is presented in Figure 21.1. Photosynthetic light reactions involve electron flow through three major protein com­plexes: photosystem II (PSII), Cytochrome b6f (Cyt bef), and photosystem I (PSI) embedded into the thylakoid membrane. The light reactions start with capture of pho­tons by the pigment molecules in the antenna complexes and subsequent transfer of light energy to PSI and PSII reaction centers, where primary charge separation occurs and photosynthetic electron transport reactions are initiated.

The two reaction centers, PSII and PSI, function simul­taneously, but in series. PSII is the only known biocatalyst that can oxidize water, which is energetically a poor elec­tron donor. The oxidation—reduction midpoint potential of water is +0.82 V at pH 7. In PSII the photolysis of water is driven by the oxidized reaction center, P680+ (the midpoint potential of P680/P680+ is +1.2 V at pH 7).

The electrons extracted from water on the lumenal side of PSII are transferred via the PSII reaction center, plasto — quinone (PQ), the Cyt bf complex and plastocyanin to PSI, which after excitation directs electrons to ferredoxin (Fd), ferredoxin-NADP+ reductase (FNR) and, finally, to generate reduced nicotinamide adenine dinucleotide phosphate (NADPH). This process is known as the linear electron transport (LET). Concomitantly with electron transfer reactions, protons are transferred inside of the thylakoid lumen creating a proton gradient across the thylakoid membrane, which in turn drives adenosine triphosphate (ATP) production via the ATP synthase complex. Sometimes, the electrons are recycled from NADPH or Fd to PQ in the process known as the cyclic electron transport, whereby DpH is generated without production of NADPH. NADPH produced by LET is further used by carbon metabolism, and many other metabolic pathways. The excess of reduced carbon is stored in cells as carbohydrates or lipids. An unique feature of photosynthetic microorganisms is that under specific conditions, most of them are able to redirect the flow of electrons originated from water splitting to the enzymes that mediate H2 production (Figure 21.1).

Biophotolysis of water by microalgae has been under investigation for over 70 years. H2 production by the anaerobically adapted and CO2-depleted suspension of Scenedesmus obliquus in light was reported for the first time by Gaffron and Rubin (1942). Three decades later, it was revealed that filamentous cyanobacteria, Anabaena cylindrica is also able to evolve H2 and O2 simulta­neously under Ar atmosphere (Benemann and Weare, 1974). Despite intensive research on the structure and function of photosynthetic protein complexes, we are still lacking a fundamental understanding of the molec­ular factors regulating the entire electron transfer chain from water to H2 in oxygenic photosynthetic organisms.

In this chapter, we mainly focus on H2 production by oxygenic photosynthetic microorganisms via the

light-dependent direct and indirect biophotolysis pathways. During direct biophotolysis H2 is derived from the electrons originated from water splitting at PSII, whereas for indirect biophotolysis electrons are mainly supplied by degradation of intracellular carbon compounds produced in photosynthetic carbon reduc­tion reactions.

As we have seen in the previous section, assessments of the global bioenergy potential are based on land use and land availability consideration subject to several sustainability criteria. These assessments thus tend to disregard agronomic boundary conditions. WBGU (2009) is one exception and also explicitly includes such aspects on a very aggregate level in their model, by assuming that only 60% of residues can be used for energy production technically (and only 30% economi­cally), given that part of the residue biomass needs to be left on the fields in order to avoid soil degradation.

In contrast to such global or regional assessments, farm or farming system-based assessments are in princi­ple able to account for such agronomic boundaries. Rossi (2012) reviews a range of sustainable farming sys­tems as options for sustainable biomass production. He points out the role of biomass as a fertilizer and for soil fertility, but does not provide quantitative assessments of how much biomass may be exported from these sys­tems for bioenergy use. Even more, the case studies pre­sented in Rossi (2012) often do not address bioenergy production at all but only illustrate the advantageous performance of the respective farming system along a range of sustainability criteria.

There is however other research that provides detailed quantitative analysis. Meyer and Priefer (2012) for example discuss the potential of biogas production in organic agriculture, based on case-study farms in Ger­many. Biogas fits neatly into organic production systems, as in organic farms, much biomass that can be used as feedstock for biogas plants is around (from grass — clover leys in the crop rotations, for example) and the biogas slurry can be used as a fertilizer. Meyer and Prie — fer (2012) provides also some forecast on the potential for such bioenergy production in Germany, assuming that the biogas is used for electricity production and also uti­lizing the heat generated in the power plants. Assuming 20% of agricultural production being organic (political goals for 2020 are 20% in Germany) and equipped with biogas facilities, 7TWh/a electricity could be provided plus 50% of this energy in heat. Assuming a total elec­tricity demand of 535 TWh/a in Germany in 2030 BMU 2011), similar biogas production on all farms would provide 6—7% of this (35 TWh/a). Also, Anspach (2009) finds that biogas production fits well into organic production systems. Using biogas slurry as fertilizer has also some additional advantages regarding yields, envi­ronmental impacts and weed control (as seeds of weeds e. g. in manure are killed in the biogas digester). The po­tential of biogas production is also recognized by au­thors of more aggregate studies, e. g. Bindraban et al. (2009). This biogas production is assumed to work largely without bioenergy cropping and only uses resi­dues and manure. Thus, it does not lead to competition with food production. Currently, the reality in Germany is different, though, as co-substrates are imported to a significant part in biogas digesters and part of those are specifically grown for biogas production (e. g. maize).

Another body of literature focuses on energy self­sufficiency of organic farms, motivated by the unsus­tainable use of fossil fuels also in organic production systems (Carter et al., 2012; Christen and Dalgaard, 2013; Halberg et al., 2008; Oleskowicz-Popiel et al., 2012; Pugesgaard et al., 2013). Those studies are from Denmark and serve as further illustration for the bioenergy production in sustainable agricultural pro­duction systems. They generally find that energy self-sufficiency of organic farms is possible and that sometimes even some small energy surplus can be generated. Carter et al. (2012) are somewhat different, as they focus on a GHG life-cycle analysis and do not address nutrient recycling aspects at all. Pugesgaard et al. (2013) find that energy self-sufficiency is also possible with nitrogen self-sufficiency. The energy self­sufficiency described in these studies comes at the expense of increased land demand or lower yields, though a fact that is not emphasized in these studies but that is crucial for our more encompassing assess­ment of sustainable bioenergy production. Fredriksson et al. (2006) find 4—10% increased land demand for en­ergy self-sufficiency of the farm. We emphasize that self-sufficiency means that such a farm does not produce any energy for the wider society. In Fredriksson et al.

(2006) , this is achieved with utilization of first- generation bioenergy, thus the agronomy is similar to or­dinary food production and biomass exports are also similar. Halberg et al. (2008) achieve energy self­sufficiency and improved nutrient availability by using land that has been set-aside in the baseline (8.5% of total farmland) for energy production. It is not discussed which environmental effects this has. Pugesgaard et al.

(2013) use 10—20% of the farm area for biogas feedstock production and report lower food yields. Either are milk yields reduced by more than 50% due to lower cattle numbers (while cash crop yields are increased by 60—120% due to improved N fertilization of cash crops), or cash crop yields are reduced by 10—30%. The scenario with 120% increased cash crop utilizes additional 20% farmland of meadows and is thus not fully comparable to the baseline. Also in this case, energy production thus comes at the expense of lower yields or higher land use. A clear assessment of what this means regarding food security is however not possible, as the differences should be translated in total calorie and pro­tein provision for human nutrition. Interesting though is the fact that part of this energy provision is possible in scenarios that go along with some dietary change only, as animal products are reduced.

CONCLUSIONS

Our analysis shows that bioenergy without land competition is difficult. While general land use models exhibit quite some potential for bioenergy production also under several sustainability constraints, they lack a due assessment of nutrient use, supply and demand in the agricultural production phase. On-farm studies reveal that increased land use or reduced yields cannot be avoided even for moderate bioenergy generation (e. g. to make a farm energy self-sufficient) unless only biogas is produced.

We draw several conclusions from this assessment of sustainable farming of bioenergy crops. First, for a thor­ough assessment of the sustainability of bioenergy, sys­temic views have to be adopted. It is not enough to assess the GHG balance on a life-cycle basis. Bioenergy as a climate change mitigation strategy needs to be analyzed in the context of the whole food system including agricultural production. Much work has been done in this direction. Land use modeling and also sustainability criteria for bioenergy account for a wide range of aspects, such as the competition for land. However, as a second point, we want to emphasize that fertilization and nutrient cycles play a minor role in the assessment of bioenergy and its sustainable produc­tion only. This is a significant lack in analysis, as biomass plays a key role as fertilizer in sustainable agricultural production systems and as feedstock for bioenergy production. Agronomic aspects of crop fertilization and nitrogen use need to play a significant role in sustainability assessments of bioenergy.

Third, we may point out biogas production as one viable option, where biomass can in principle be used for both ends at the same time—as feedstock for biogas plants and as fertilizer in the form of biogas slurry, after having passed through the biogas digester. Biogas pro­duction can be designed in such a way that it fits into agricultural production systems without additional land demand. However, as promising as it is for local energy generation, the aggregate potential remains small. In addition, it is no option for producing liquid biofuels.

Fourth, land competition is a key challenge, in partic­ular for liquid biofuel production. Many models to assess the bioenergy potential globally or regionally exist, but they should be improved by adding much more detailed interaction with the energy markets. Such models need to be able to capture land use alloca­tion based on the relative profitability of energy or food production. Most models focus on assessing physical potentials which is a key basis for this, and they mention economic constraints for developing the technical bio­energy potential, but how strong a land competition will emerge hinges on such relative profitability, resp. prices and on demand and supply elasticities, i. e. how much demand and supply changes with prices. In addi­tion, these land use models need to incorporate agro­nomic aspects. Nitrogen demand of energy crops, corresponding fertilizer demand, its environmental ef­fects and linkages between yields and nutrient inputs need to be captured in much more detail to arrive at reli­able conclusions. If it comes to assessing bioenergy po­tentials in the context of sustainable agricultural production systems, the need to capture fertilizer and nutrient dynamics in more detail is directly linked to biomass flows that must be captured adequately be­tween energy and fertilizer use.

Fifth, some improved standard for sustainable bio­energy could help in this. We thus suggest to combine the RSB (2011) and GEF et al. (2013) standards and to enhance them with agronomic aspects related to nutrient and biomass use and recycling.

Suitable conversion technologies are needed in order to effectively breakdown or deconstruct biomass into simple sugars, carbohydrate derivatives or bio-oils that are more easily converted into fuels in combination with downstream conversion technologies to subse­quently upgrade these intermediates into bioenergy, biofuels and value-added bioproducts.

At present, the bioconversion of lignocellulose is carried out in four major steps viz. pretreatment, hydro­lysis, fermentation and separation/purification to recover bioenergy/biofuels and residues (more recently, the recovery of coproducts has becoming increasingly important). The pretreatment of lignocellulose materials is considered a key step in bioenergy production and indeed in biorefining as it accelerates the hydrolysis pro­cedure, by enhancing cellulose accessibility and increasing pore size, which, in theory, leads to higher sugar yields for fermentation. An ideal pretreatment should remove lignin and thus reduce the crystallinity of cellulose (Lynd et al., 2002), increase porosity and accessibility of the cellulose (and hemicellulose) to enzymatic hydrolysis, release/generate low levels of inhibitory compounds, be low cost and have low energy requirements. The overall result should be a reduction in the recalcitrance of lignocellulose and an increase in accessibility to enzymes.

In general, accessibility of cellulose is achieved through the removal of lignin and hemicellulose polymers through various pretreatment methods, which can be defined as chemical, physical or biological (O’Donovan et al., 2013; Dashtban et al., 2009; Taherzadeh and Karimi, 2008; Ong, 2004; Howard et al., 2003).

Microbial pretreatment makes use of microorganisms and their enzyme systems to breakdown lignin and/or hemicellulose present in lignocellulosic biomass. So far, the isolated and identified lignocellulolytic microor­ganisms mainly include fungi and a few bacterial strains. Fungi including brown-, white-, and soft-rot fungi are the predominant organisms responsible for lignocellulose degradation, and among the fungi, the Basidiomycetes that cause both white and brown rots are the most rapid degraders (Bennet et al., 2002; Loguercio-Leite et al., 2008; Rabinovich et al., 2004; Sanchez, 2009; ten Have and Teunissen, 2001). Several Basidiomycetes such as P. chrysosporium, C. subvermispora, Phlebia subserialis, Pleurotus ostreatus, and Irpex lacteus have been shown to efficiently degrade lignin in different lignocellulosic materials (Hatakka and Usi-Rauva, 1983; Keller et al., 2003; Sawada et al., 1995; Taniguchi et al., 2005; Zeng et al., 2011).

Natural Microorganisms and Practical Applications in Bioconversion

Application of White-Rot Fungus in Treatment of Different Biomasses

CORN STOVER

When corn stover is pretreated with C. subvermispora for downstream bioethanol production, lignin is selec­tively degraded up to 31.59% with a limited cellulose loss of less than 6% during an 18-day pretreatment. Longer pretreatment time was found to increase lignin removal, resulting in correspondingly higher glucose yields from enzymatic hydrolysis. The highest overall ethanol yield of 57.80% was obtained with 35-day — pretreated corn stover (Wan and Li, 2010).

In a later study, the effectiveness of C. subvermispora pretreatment on different types of feedstocks, including corn stover, wheat straw, soybean straw, switchgrass, and hardwood was tested. After an 18-day pretreat­ment, corn stover, switchgrass, and hardwood were effectively delignified, leading to a two — to threefold in­crease in glucose yield over those of the untreated raw materials. In contrast, wheat straw and soybean straw did not show glucose yield increase after undergoing the same pretreatment, suggesting the importance of us­ing a specific strain for pretreatment of specific biomass (Wan and Li, 2011).

Pretreatments of corn stover with the white-rot fungus

I. lacteus CD2 also resulted in significant lignin degrada­tion with limited cellulose loss (Zeng et al., 2011). Pre­treatment of corn stover with Cyathus stercoreus led to a three — to fivefold improvement in enzymatic cellulose digestibility (Keller et al., 2003). Pretreatment of corn sto­ver with a newly isolated white-rot fungus, Trametes hirsuta yj9, led to selective lignin degradation up to 71.49% and a significant increase in enzymatic digestibil­ity of 73.99% after a 42-day pretreatment (Sun et al., 2011). Pretreatment of corn stover fractions (leaves, cobs, and stalks) with the white-rot fungus C. subvermispora showed that the leaves were the least recalcitrant to fungal pretreatment with a 45% lignin degradation as well as higher carbohydrate degradation after 30 days of pretreatment. However, corn cobs produced the high­est sugar yield after fungal pretreatment (Cui et al., 2012).

SOFTWOOD

The effect of pretreatment on the softwood Pinus den- siflora by three white-rot fungi, Ceriporia lacerata, Stereum hirsutum, and Polyporus brumalis, has been investigated. Among the three white-rot fungi tested, S. hirsutum selectively degraded the lignin rather than the holocellu — lose component. Consistently, extracellular enzymes from S. hirsutum showed higher activity of ligninase and lower activity of cellulase than those from the other white-rot fungi. In addition, the available pore size and surface area in the pretreated wood were increased, possibly due to degradation of lignin and a small portion of hemicellulose by the secreted enzymes. Sugar yield of the S. hirsutum pretreated wood also greatly increased compared to a nonpretreated sample, indi­cating S. hirsutum might be a potentially effective fungus for use in biological pretreatment of woody biomass (Lee et al., 2007).

Water content plays a key role in enzymatic transes­terification as it is vital to sustain the three­dimensional conformations of enzyme catalytic site. Presence of an oil—water interface creates a favorable environment for the conformation of active site (Al — Zuhair et al., 2006, 2003). Water interacts with the enzyme hydrophilic groups located on surface, and changes the conformation of hydrogen bond interac­tions inside enzyme, leading to transformation of lipase active (Gao et al., 2006). Generally lipase activity in­creases with increase in water content up to 15% (w/w of oils). Beyond 15%, the conversion rate decreased slightly. But 20% of water content also efficiently cata­lyzed alcoholysis using lipases from Rhizopus delemar and Rhizomucor miehei (Tweddell et al., 1998). About 5% of initial water content was suggested as optimum for biodiesel production from jatropha oil using various lipases (Shah and Gupta, 2007). Thus, the optimum level of initial water (moisture) is based on the type of biocat­alyst and reaction conditions.

ACYL ACCEPTORS

Generally transesterification reactions are conducted using straight — and branched-chain alcohols. Because of abundant availability and low cost, methanol is the widely used short-chain alcohol acyl acceptor for bio­diesel production (Fan, 2012). The negative effect of methanol on enzyme activity alleviates by stepwise addition of alcohols. Ethanol, n-butanol and i-butanol, n-amylalcohol and i-amylalcohol, and n-propanol were also used during transesterification. But increase in C number of the alcohols has not significantly influenced fatty acid ester contents and shown the negative effect (Soumanou and Bornscheuer, 2003a, b). Also, it is gener­ally believed that primary alcohols are more suitable than secondary alcohols and alcohols with less than eight carbon atoms can be used under the conditions that gave the highest conversion of the oils to FAME. Methyl acetate had no negative effect on enzymatic ac­tivity. No changes were detected in lipase activity even after being continuously used for 100 batches (Sulaiman,

2007) . Recently, ethyl acetate, methyl acetate, butyl acetate, vinyl acetate and dimethyl carbonate (DMC) are considered as novel acyl acceptors. The work revealed by Er-Zheng et al. (2007) proved that DMC gives two — to threefold higher conversion than those of conventional acyl acceptors (methanol and methyl ace­tate) and is also ecofriendly, neutral, odorless, cheap, noncorrosive, nontoxic, and exhibits good solvent properties.

SOLVENTS

In the above section (molar ratio) discussed that excess amount of alcohols increases FAME yield. In order to in­crease the solubility of alcohol (not the enzyme), solvents are used and they alleviate negative effect of methanol on the catalyst and precede the transesterification. Enzyme should be insoluble in solvent; otherwise, it will not be active (Kanerva et al., 1990; Antczak et al., 2009). Various hydrophilic and hydrophobic organic solvents such as cyclohexane, n-hexane, tert-butanol, petroleum ether, isooctane and 1,4-dioxane are mainly studied organic sol­vents in enzymatic biodiesel production. If organic sol­vent is used as medium, overall alcohol is added at the beginning of the reaction. In solvent-free reaction me­dium, alcohol is added stepwise to prevent enzyme activ­ity with high alcohol concentration (Sevil et al., 2012).

Nowadays, waste disposal is a worldwide problem. In agricultural countries like India, waste discharges from agriculture, agrobased industries and city sewages are the main sources of water pollution. Conventional waste­water treatment systems do not seem to be the definitive solution to pollution and eutrophication problems. The major drawbacks are cost and lack of nutrient recycling (Eisenberg et al., 1981). Secondary sewage treatment plants are specifically designed to control the quantity of organic compounds in wastewaters. Other pollutants including nitrogen and phosphorus are only slightly affected by this type of treatment (Gates and Borchardt, 1964). Owing to the ability to use nitrogen and phosphorus for growth, algae can successfully be cultivated in such type of wastewaters (Mallick, 2002). This has been evolved from the early work of Oswald (Oswald et al., 1953) using microalgae in tertiary treatment of municipal wastewa­ters. The widely used microalgae cultures for nutrient removal are Chlorella (Gonzalez et al., 1997; Lee and Lee,

2001) , Scenedesmus (Martinez et al., 1999, 2000) and Spiru — lina (Olguin et al., 2003). Nutrient removal efficiency of Nannochloris sp. (Jimenez-Perez et al., 2004), B. brauinii (An et al., 2003), and Phormidium sp. (Dumas et al., 1998; Laliberte et al., 1997) has also been investigated. One of the well-known algae-based bioprocesses for wastewater treatment is high-rate algal ponds (Cromar et al., 1996; Deviller et al., 2004). Recently, corrugated raceways (Craggs et al., 1997; Olguin et al., 2003), triangular photobioreactors (Dumas et al., 1998), and tubular photobioreactors (Briassoulis et al., 2010; Molina et al.,

2000) have been developed for nutrient removal.

Among agroindustries, a large quantity of waste­water is generated from intensified aquaculture prac­tices. The main source of potentially polluting waste in fish culture is feed derived, mainly unconsumed and undigested feed and fish excreta. Discharging these
effluents directly into water resources causes eutrophi­cation of the receiving waters. Qian et al. (1996) reported the collapse of a prawn industry in China due to outbreak of pathogenic bacteria caused by high nutrient load. A few studies have shown the efficiency of algae biofilters in removing nitrogen from fish effluents (Cohen and Neori, 1991; Jimenez del Rio et al., 1996; Schuenhoff et al., 2003). These works are based on the use of seaweeds of the genera Ulva and Gracilaria to treat effluent water from aquaculture.

Recently, we intend to explore an integrated approach to produce biodiesel with simultaneous waste recycling by a green microalga S. obliquus with three types of wastes, viz. poultry litter (PL), fish pond discharge (FPD), and municipal secondary settling tank discharge. Our initial trial under laboratory batch culture condi­tions (Mandal and Mallick, 2011) encouraged us to conduct a small-scale field experiment in a recirculatory aquaculture system (RAS) using FPD and PL with the same microalga (Mandal and Mallick, 2012). Figure 11.1 presents a schematic diagram of RAS, developed at Agricultural and Food Engineering Department, Indian Institute of Technology Kharagpur, West Bengal, India. The effluent from a fish pond was pumped into a settling tank for removal of large solids. After 24 h, the superna­tant was siphoned to an inclined plate settler for removal of fine solids. To have a clear picture of the
inclined plate settler readers are requested to refer Sarkar et al. (2007). The effluent was then entered into fiber-reinforced plastic tanks (length 125 cm, breadth 60 cm, depth 45 cm) for culturing the test microalga.

FPD has a very high load of solid particles in suspen­sion, which contributes to increase in turbidity. Experi­ments carried on with sedimented and nonsedimented FPD showed that the nutrient removal efficiency of S. obliquus was higher in the sedimented one. Further ex­periments with sedimented FPD demonstrated that biomass and lipid yield was maximum at 15 cm culture depth with stirring. In seasonal variation study, the maximum algal biomass and lipid productivity was recorded during summer when sunshine hour was rela­tively large. During the summer season, when S. obliquus cultures pregrown in FPD supplemented with 5 g PL/l were transferred to the optimized conditions to maxi­mize the lipid accumulation (to have details on opti­mized condition readers are requested to refer Mandal and Mallick, 2009), lipid yield was raised by more than sixfold (up to 780 mg/l, Mandal and Mallick, 2012). Dur­ing rainy and winter seasons, comparable lipid yield was recorded by providing artificial lights for few hours. Thus an areal lipid productivity of 14,000 l/ha year (approximately) has been projected assuming 11 cultiva­tion cycles per year, leaving the rest of the period for cleaning and maintenance of the system

The industrial conversion of renewable resources has a quite long history, lasting since 6000 BC, in particular on the utilization of sugar cane (Demirbas, 2010; Kamm et al., 2006). However, proofs on the production of ethanol by distillation were found in China, in the form of dried residues of 9000 years old. Also, the ancient Egyptians used to produce alcohol by fermenta­tions from vegetal materials (Demirbas, 2010).

An analysis of biorefineries history should entail various aspects of wood saccharification, sugar produc­tion, synthesis of various bio-based products (furfural, lipids, lactic acid and many others), energy sources, and integrated processes (Kamm et al., 2006; Demirbas, 2010; de Jong and Marcotullio, 2010; Martin and Grossmann, 2012). Therefore, the topic branches of bio­refineries which process renewable materials became well known and applied worldwide. These develop­ments were more evident since the nineteenth and the beginning of the twentieth century, distinctively in the pulp and paper industry, where wood is the main raw material and the derived wastes gave rise to various so­lutions for the exploitation of valuable components they include (Rodsrud et al., 2012). Also, the food industry was a sector with high potential of waste valorization and recovery. Moreover, the increase in environmental concerns, especially related to the use of fossil fuels, has asked for sustainable solutions to limit the green­house gas effects and resources depletion. Table 14.1 provides a short outline of biorefinery evolution, based on data existent in various sources (Demirbas, 2010; Kamm et al., 2006; Rodsrud et al., 2012).

However, some voices claim that the concept of "bio­refinery" appeared in the 1990s as reaction to some trends of industry such as the need to use biomass re­sources in a more balanced way from both economic and environmental perspectives; an emergent concern in the promotion of low-quality lignocellulosic biomass to valuable products; an increased attention to the pro­duction of starch for energy applications; a need to develop extra high-value products and expand product combinations to face global competition; and to exploit an excess of biomass (especially in the pulp and paper industry) (Alakangas and Makinen, 2008; Berntsson et al., 2012).

BIOMASS FEEDSTOCK

Biorefineries process a bio-based feedstock input, analogous to the petroleum refineries, where a variety of different products may result, such as fuel, power, or chemicals (WEF, 2013). Although biorefineries use a large variety of different raw materials and conversion technologies, a clear alternative to fossil-based products does not exist still today (WEF, 2013). However, four classes of feedstocks are established (Demirbas, 2010):

• First generation which entails edible biomass (starch — rich, oily plants) to produce bioalcohols, vegetable oil, biodiesel, biosyngas, and biogas.

• Second generation which uses biomass in the form of nonfood sources and crops (residual nonfood parts of crops, solid waste, wheat straw, etc.) to produce bioalcohols, biooil, biohydrogen, bio-Fischer— Tropsch diesel.

• Third generation which includes algae to produce vegetable oil and biodiesel.

• Fourth generation which uses vegetable oil and biodiesel to produce biogasoline.

A more detailed presentation is done in Table 14.2. The option to choose one or more of the four different

(Continued)

alternatives to replace the fossil fuels-based products with biomass-based products depends on, among others, the costs involved (Sanders et al., 2005; van Ree and Annavelink, 2007).

There are different paths for biomass utilization (Table 14.3) (Wagemann, 2012):

• integral unmodified or modified biomass, without component separation;

• various individual components of biomass;

• biomass components in a complete way/form at various location;

• the whole biomass in its complete forms.

However, any classification is generic only based on a too large generalization and provides little information on the intimacy of involved processes as well as of the possibility to apply various technological processes to different feedstocks (Cherubini et al., 2009). No classifi­cation criterion allows the combination of different bio­refinery systems by linking different technologies involved in both energy-driven biorefinery systems and material-driven biorefinery systems. Cherubini et al.

(2009) mentioned some examples in this regard: "if the carbohydrate fraction of a lignocellulosic feedstock is used to produce cellulose and xylose, the system is classified as a lignocellulosic feedstock biorefinery; but can also be classified as a forest-based biorefinery and, if the lignin fraction is pyro — lyzed, the same biorefinery is also suitable for classification as a two-platform concept biorefinery".

STRUCTURE OF BIOREFINERY CONCEPT

The biorefinery is more than a fixed technology since it includes a collection of unitary processes, by several different routes from feedstocks to products (Xiu et al.,

2011) . Figure 14.3 shows the structural scheme of bio­refinery concepts, including process types with the uni­tary processes and the primary products and intermediates, as well as secondary products (Hackl and Harvey, 2010).

The economic viability of bio-based products prepa­ration involves different processes and methods: phys­ical, chemical, biological, and thermal. Table 14.3 describes shortly some of these processes and methods.

However, a clear set of criteria to classify the different biorefinery concepts is still missing. van Ree and Annevelink (2007) considered a classification based on the following:

• Raw material input, resulting in some classes of biorefineries, like Green, Whole Crop, Lignocellulosic, Feedstock, and Marine Biorefineries.

• Technologies applied for biomass processing: Two Platform Concept, Thermo, Chemical Biorefineries.

• Products resulted (main, intermediate): Syngas, Sugar, Lignin Platforms.

Due to the complexity of this structure, process integration is the most sustainable approach to ensure the system efficiency and products quality. In an inte­grated configuration, biorefinery systems are struc­tured in various ways by considering the use of raw materials, the environmentally sound character, and the degree of integration as follows (van Ree and Annevelink, 2007, Martin and Grossmann, 2012, Wagemann, 2012):

• Lignocellulosic feedstock biorefinery is based on the processing of lignocellulosic-rich biomass sources in three steps (Figure 14.4): cellulose (sugar raw material); hemicelluloses (polyses); and lignin. These

TABLE 14.3 Biomass Utilization Paths (Wagemann, 2012)

Biomass Utilization Examples

Wood for wood-based raw materials or sawing products Wood used as fuel Insulating materials made of natural fibers Linseed oil as solvent

Vegetable oil from rape or as component of lacquers/dyes Starch from cereal crops for the production of bioethanol or for the production of paper starch Sugar from sugar beet used as a fermentation raw material

Biogas from corn for local generation of electricity and heat respectively for biomethane as feed-in into grid for use in different locations

Palm oil generation aboard, its transportation to Europe, and its domestic processing

Biorefinery concepts using a platform for the integrated production of a spectrum of products

Source: Adapted with the permission of the coordinator of "Biorefineries Roadmap as part of the German Federal Government action plans for the material and energetic utilization of renewable raw materials" brochure on behalf of The Federal Government, Professor Kurt Wagemann.

processing steps result in feeds, chemicals, biopolymers and other biomaterials. All residues are incinerated for the cogeneration of heat and power (van Ree and Annevelink, 2007).

• Whole crop biorefinery uses raw materials (cereals, maize, and wheat) in the form of grain, flour (meal), and straw (combination of ears, leaves, chaff and nodes), based on dry or wet milling biomass. Their processing results in feeds, chemicals and biomaterials (Figure 14.5).

• Green biorefineries use "nature wet" (fresh) biomass (green grass, clover, alfalfa, and immature cereals), resulting in a fiber-rich press cake and a nutrient-rich press juice (Figure 14.6).

• Thermochemical biorefinery (TCBR) entails the biomass refining into a large portfolio of value-added products, by applying several technologies such as pyrolysis, gasification, torrefaction, and hydrothermal upgrading. The resulting products could be introduced into the existing infrastructures and substituting fossil fuels (de Wild, 2011; Martin
and Grossmann, 2012). A particular concept derived from TCBR and developed by de Wild (2011) relies to Staged Catalytic Biorefinery Concept, which offers the possibility to process biomass in different sequential technological steps, with reducing the severity of the processing conditions using suitable catalysts, and to separate diverse products at different stages.

• Marine biorefinery (MBR) is based on marine crops, i. e. microalgae (diatoms; green, golden, and blue/green algae) and macroalgae (brown, red and green seaweeds), and their derived products (Bowles, 2007; van Ree and Annevelink, 2007; Martin and Grossmann, 2012).

Depending on the materials resulted after primary refinery steps, the leading procedures applied for further transformation and the integration degree of these above mentioned biorefinery systems could be included in various biorefinery platforms: biochem­ical, thermochemical, and microorganism platforms (Cherubini et al., 2009; Kammm et al., 2006; WEF,

2010) (Table 14.4.)

In this context, the biorefinery is "an explicitly inte­grative, multifunctional overall concept that biomass as a diverse source of raw materials for the sustainable gener­ation of a spectrum of different intermediates and prod­ucts (chemicals, materials, bioenergy/biofuels), allowing the fullest possible use of all raw material components. The coproducts can also be food and/or feed. These objec­tives necessitate the integration of a range of different methods and technologies" (Wagemann, 2012).

The integration and multifunctionality in bio­refineries can be performed at four levels raw material, process, product, and industry (Martin and Grossmann, 2012; Wagemann, 2012) (Figure 14.7).