It is important not to lose sight of the impact that climate change itself will have on biofuels into the future, together with determining the most appropriate place for biofuels in the long-term battle to reduce global GHG emissions. This section deals with these two issues.

4.2 Effects of Climate Change

It is uncertain whether existing current climatic conditions will prevail, with many scientists contending that anthropogenic climate change is already taking effect across the globe (Cook et al. 2013). There are a number of critical factors associ­ated with climate change that need to be taken into account. First, and as intro­duced above, there may be increased uncertainty with regard to rainfall patterns. This will problematize when to plant with annual crops (such as those used for first-generation biofuels), and will also place increased pressure on water use, with potential social repercussions outside the agricultural arena. Second, there may be increased and more severe meteorological phenomena, with floods wip­ing out entire fields, and storms damaging or destroying entire harvests (Charles et al. 2009). Uncontrolled fires resulting from drought, thunderstorm activity or human action could also have similar effects. Third, there may be an increased severity and incidence of pestilence, with changed climatic conditions making crops destined for biofuel production more susceptible to pest outbreaks (Malcolm et al. 2012). This would have the added environmental implication that there could potentially be an increased need to employ pesticides, herbicides or fungicides, with all the negative outcomes associated with the use of these materials signalled above compounded by increased chemical usage.

Taken together, these issues suggest that it will be more difficult to plan for weather — and climate-related phenomena into the future. Nations will clearly be unable to rely solely on domestic biomass cultivation for their biofuel needs (Larson 2008). It follows that increased energy security associated with biofuel production will need to be tempered with the understanding that existing agri­cultural techniques certainly do not guarantee constant and predictable harvests in the face of regular climatic uncertainty. Yet climate change, as is generally expected, will exacerbate this high level of uncertainty, regardless of whether it is anthropogenic or otherwise, or indeed a combination of both man-made and natu­ral processes. Regardless of these issues, it is essential that biofuel policy takes a path that does as much as possible to ensure that it assists with anthropogenic cli­mate change mitigation, rather than exacerbating the problem.

Microalgae have been studied for many years for production of commodities and special human foods and animal feeds. Moreover, algae can generate a wide range of biofuels, including biohydrogen, methane, oils (triglycerides and hydrocarbons, con­vertible to biodiesel, jet fuels, etc.), and, to a lesser extent, bioethanol. Meanwhile, this products’ production involves different processes such as biochemical and ther­mochemical conversions or chemical separation or a direct combustion (Huesemann et al. 2010). Like a refinery, it is still possible to obtain other non-energy products in the cultivation of microalgae, such as cosmetics, animal feed, and nutraceuticals.

Subhadra and Edwards (2011) analyzed algal biorefinery-based integrated industrial sector that produces primary biofuel (biodiesel), coproducts (algal meal—AM), and omega-3 fatty acids (O3FA and glycerin). They demonstrated that biorefineries have a clear market for AM and O3FA up to a certain level; thereafter, diversification for other coproducts is desirable. However, coproduct market analysis and water footprint (WFP) of algal biorefineries need to be studied before large-scale deployment and adoption. In addition, Benemann (2012) argued that saying that “animal feeds could be readily coproduced with algae biofuels are incorrect”; because there are significant differences in the processes focus, quanti­ties production, volume and market values, comparing coproducts with biofuels. However, algal biofuel can be integrated with aquaculture to treat the wastes.

Oil products account for approximately 95 % of the energy used for transportation in the world in their various modes. The technological standards for the use of this energy source, which has been strongly disseminated in the world, developed over more than a century.

However, several liabilities accompany its hegemonic use, since the reduction of available stocks of this essential non-renewable resource (petroleum), pollution, and GHG emissions (Seabra 2008: 83).

Therefore, the continuation of fossil fuel energy resources use provides strate­gic and environmental drawbacks, seeing that the use of non-renewable sources is revealed as a way of releasing elements captured in a remote past, which expose the modern lifestyle to a not properly dimensioned future risk.

On the other hand, the production and the consumption of biofuel obtained from agricultural biomass (renewable resources) entails a GHG balance (CO2 eq.) close to neutrality. Thus, unlike fossil fuels, the biomass has sustainable features, since human systems capitalize on energy use with little interference in the GHG balance (ANEEL 2008; Macedo et al. 2008; Garcia 2011).

According to Table 10, the sugarcane has the best energy efficiency (9.3) among the different sources of biomass available in Brazil and it has the highest reduction percentage of GHG emissions (89 %). These indicators are much higher than those obtained by corn (US option) or beet (an option used in Europe).

When the Life Cycle Assessment (LCA) of some biofuels was performed, ethanol was highlighted due to the high percentage of GHG reduction, as depicted in Fig. 9.

Even though the options of energy production are within the renewable status, they are not free of interfering negatively on the environment. One of the most important liabilities is the interference in the soil and the formation of monocul­tures over large areas. However, these problems can be mitigated by techniques and processes that increase biomass productivity per area. An example of this is that Brazil produces 6,800 l of ethanol per hectare of sugarcane, while the USA produces 3,100 l/ha of maize (ANEEL 2008).

In Brazil, several crops have the potential to produce bioenergy, among them soy, sugarcane, castor bean, and palm oil. The cultivation of sugarcane has been highlighted in the production of ethanol. With a focus on increasing productivity, the mills have opted for mechanical harvesting, including suitability for the cur­rent legislation which restricts fires of sugarcane straw for the crop.

Another element of this sustainable supply chain is the use of bagasse to pro­duce electricity through thermal power plants (ANEEL 2008).

The techniques and processes evolution and R&D also contribute to the increased efficiency in the various stages of the production process, such as har­vesting sugarcane in Brazil, which is abandoning the straw burning for the harvest and better studies about the emission levels in the various stages of production and processing of this biomass (Table 11).

The governance structure is related to the transaction characteristics of a given production chain. Table 1 illustrates the characteristics of each transaction accord­ing to the aforementioned theoretical attributes.

Table 1 Main transaction attributes of palm oil processing plants with family farmers

Transaction Transaction attributes Predominant

Buyer o Business Frequency Degree of governance

Seller specificity uncertainty

Biofuels that are produced from non-food feedstocks (second generation) are commonly known as advanced biofuels. They can be classified into four groups (generations) based on their production process and type of feedstock (Table 1). As summarized in Table 1, it is evident that each generation has its own advantages and disadvantages and it is difficult to choose one among them for large-scale produc­tion and application. For example, fourth-generation biofuels seem to be very attrac­tive and carry several advantages from carbon emission and environmental pollution point of view; however, its process of production is cumbersome and technically not proved and established as of now to make it commercially viable.

Catalysts were degassed under vacuum at 160 °C for 5 h, and then, the sur­face area of catalysts was measured using a surface analyzer. The pore size distributions and pore volume were determined. Scanning electron microscope (SEM) was used for determination of surface morphology and shape of catalyst particles.

Collector

Furnace

I hermocouplc

Fig. 1 Schematic diagram of experimental apparatus

1.3 Preparation of ZSM-5 and SBA-15 Catalysts

The catalysts were activated by calcination at 420 °C in nitrogen for 1 h, cooled to 350 °C, and then calcined further in air at 540 °C for 12 h. After this, the catalysts were crushed into powder. Both polyethylene and catalyst powder were sieved to ensure that particle sizes remained around 60-150 mesh and then blended by grind­ing the desired amount of catalyst and polyethylene according to a certain ratio.

The Industrial Organization (that was also called Industrial Economy, in Great Britain and Europe) is not recent, where the central focuses of this study are as follows: (1) competition, as the engine of most modern markets, and (2) the power of monopolies that interfere with the good results of competition (De Jong and Shepherd 2007). The Industrial Organization also focuses on the study of public policies, where the first studies analyzed the governmental policies, in order to prevent the existence of monopolies, to eliminate, or at least restrict, the effects of the existing monopolies. The public policies studies mainly include as follows: antitrust policies, in order to prevent or reduce the power of monopoly; regulation, so as to contain the natural monopolies; deregulation, which removes restrictions, hoping that competition will grow, and the creation of estates that seek to support the public interest when competition does not work.

However, a growing research area, within the Industrial Organization, is iden­tifying the industrial concentration level, where one seeks understanding the rela­tionship between the concentration level and this industry’s price/profitability ratio, where much evidence point to a positive relationship between market con­centration and the sector’s profitability (Peltzman 1977). The basic assumption for this purpose is that high concentration enables collusion and, as a consequence, the manipulation of market prices.

Peltzman (1977) said that the relationship between the market structure and productions costs is long known, where a technological breakthrough in a not con­centrated industry can produce a natural monopoly, since there will be an increase of the operational efficiency through time, generating competitive advantages for a specific organization. On the other hand, according to the author, the pro­cess through which old technology becomes economically obsolete also implies a reduction (or at least no increase) of the offered goods. Whatever force is operat­ing this system, it is crucial to understand what the concentration level is, so as to control the excessive power of some firms within its industry.

Industrial structure and industrial concentration issues have concerned econo­mists and politicians for at least a century (Jacquemin and Slade 1986), while the industrial concentration level is tightly connected to the margins firms keep in the market, since competitiveness drops according to the increase of concentration level, creating opportunities for firms to price in a differentiated manner. The mar­ket concentration analysis, on the other hand, of a specific industry stems from the idea of how it is distributed in terms of production and participation of their firms, in a determined market. In this context, Bain and Qualls (1968) define industrial concentration according to property, considering the control of a great proportion of aggregates of economic resources or activities, by a small companies’ proportion.

George and Joll (1983) states that the industrial concentration regards the size distribution of firms that sell a specific product, with a significant dimension of the market structure, for having an important role regarding a company’s behavior and performance. Besides, the number and size distribution of these firms influence the expectations regarding the competitors’ behavior. In this context, Possas (1985) comments that the industrial concentration is closely linked to the internal profit accumulation and corporate technical progress.

According to Bain and Qualls (1968), the market structure regards the organiza­tional features that determine the relationships with the agents, being an important part of the competitive environment of firms, in order to influence the competitors’ pattern. For the author, this means that the market structure features have a strategic influence on the nature of competition and on determining prices in the market.

These are fuels that are produced from edible crops. Bioethanol is generally derived from commonly grown food crops such as sugar cane, sugar beet, maize (corn), sorghum and wheat. First-generation processes for bioethanol production, in the case of plants such as corn and wheat, rely on starch from plant kernels or, with respect to sugar cane and sugar beet, on the sucrose contained within parts of the plant (McCormick-Brennan et al. 2007). These starches and sugars are fer­mented and are then distilled, in much the same way as the production of alcohol destined for other purposes. The types of crops employed for first-generation bio­fuel production also have lower energy content than conventional petroleum prod­ucts per volume, something which exacerbates the issues surrounding the use of this technology (McCormick-Brennan et al. 2007). With regard to first-generation biodiesel, crops such as rapeseed, palm oil, Jatropha and soya beans are gener­ally used. The oil from these crops is then converted to biodiesel, together with a co-product called glycerol, which can be used for a variety of non-energy-related purposes. Waste vegetable oil (WVO), if cleaned up sufficiently, can also be used to produce biodiesel (Parida et al. 2011).

David E. Leiva-Candia and M. P. Dorado

Abstract The biodiesel industry is gaining interest in the past years due to the depletion of the easily extracted petroleum, the increasing demand to the automotive market, and the environmental damage. It is acknowledged that the main obstacle to biodiesel marketing is the cost of production, which is mostly due to the price of the raw material (usually vegetable oils). In this way, the goal is to provide low-cost raw materials. This may be achieved by feedstocks that do not require arable land, do not depend on growing seasons, and that give added value to waste, helping also to its recycling. In this way, oleaginous organisms may be considered an alternative feedstock for the biodiesel industry, as they meet all the previous requirements. This chapter presents the state of the art and the main characteristics of the oil and bio­diesel provided by macroorganism (insects) and microorganism (bacteria, filamen­tous fungi, and yeasts).

1 Introduction

It is worldwide accepted that biodiesel is an attractive alternative to fossil diesel fuel in terms of exhaust emissions besides its renewable nature (Demirbas 2009). However, the market inclusion of first-generation biodiesel is controversial due to the “food versus fuel” discussion (Pinzi et al. 2009). Moreover, it is not economi­cally viable in the absence of both tax exemption and high petroleum-derived

D. E. Leiva-Candia • M. P. Dorado (H)

Department of Physical Chemistry and Applied Thermodynamics, University of Cordoba, Cordoba, Spain e-mail: pilar. dorado@uco. es

D. E. Leiva-Candia e-mail: z82lecad@uco. es

A. Domingos Padula et al. (eds.), Liquid Biofuels: Emergence, Development and Prospects, Lecture Notes in Energy 27, DOI: 10.1007/978-1-4471-6482-1_11, © Springer-Verlag London 2014

fuel prices (Janda et al. 2012), as a result of the high cost of the raw materials (60-75 % of the total cost of biodiesel) (Dorado et al. 2006; Gui et al. 2008). In this sense, research is focused on new renewable non-edible low-cost raw materi­als that do not need arable land. Second-generation biodiesel, mainly constituted by non-edible oil, waste oil, and animal fat-based biodiesel, partially complies with the above requirements, as in some cases, it requires land to produce the raw materi­als. Third-generation biodiesel uses non-edible oleaginous alternative sources fully independent of climate or availability of land. Among the possibilities, there is a novel source of raw materials composed by macro — and microorganisms that are able to produce oil.

In the category of macroorganisms, insects show a great potential in terms of fat accumulation, in some cases above 25-30 %, especially during the immature stages (larva, pupa, and nymph) (Manzano-Agugliaro et al. 2012). The fat contents of oleaginous insects vary according to the species and location, being Coleoptera and Lepidoptera species the ones that provide the highest amount of fat (Ramos — Elorduy 2008). Insects have shown a high potential to replace oleaginous seeds as raw material for biodiesel production, due to their high food efficiency, high repro­duction rate, and short life cycle (Li et al. 2012). Furthermore, biodiesel derived from insect oil fulfills both ASTM D6751 and EN 14214 standards (Leung et al. 2012; Li et al. 2012).

Microbial oil or single-cell oil proceeds from different oleaginous microorgan­isms, i. e., bacteria, fungi, and microalgae (Li et al. 2008). These microorganisms are able to accumulate intracellular lipids above 20 % of their dry cell weight. Besides, they do not require arable land and allow the recycling of residual bio­mass, as it can be used as a carbon source (Azocar et al. 2010). The accumu­lation of lipids depends on the kind of microorganism, culture conditions, and the relation C/N, as under nitrogen limitation, the accumulation of oil increases. The oleaginous microorganisms are able to consume a variety of carbon sub­strates following different metabolic pathways (Xu et al. 2013). Currently, tech­nologies for the production of microbial oil are still in pilot scale, i. e., Nestea Oil Company uses waste as medium and expects commercial production after 2015 (Neste oil 2012).

The potential use of microbial oil as a feedstock for the biodiesel industry is surrounded by a great expectation, as oleaginous microorganisms can be grown in conventional microbial bioreactors, improving the biomass yield and reduc­ing the cost of produced biomass and oil (Vicente et al. 2009). For the reasons mentioned above, this chapter includes the main characteristics and properties of microbial oil, with special focus on the use of waste as substrate and the sub­sequent biodiesel. Microalgae have been removed from this chapter as the sole explanation of the cultivation technology requires a fully dedicated chapter.

In our model, feedstock prices are exogenous variables and therefore independ­ent from production scale. This assumption is based on the fact that transportation costs are the main driver for raw material prices and the costs per unit increase with the scale of a plant as transport routes become longer. The rationale behind this assumption is that each company aims to operate at the optimal production scale in the light of (a) the tension between scale benefits and (b) increasing cost of capital associated with transportation costs. Cost advantages driven by learning

and scale are significant endogenous parameters and therefore main determinants in our calculation model.

For each type of biofuel, we assume learning-based cost-reduction potentials which diminish over time applied to all process steps as defined in Fig. 3. In regard to second-generation biofuels, we estimate learning curve effects of 40, 30 and 20 % for the corresponding time frames 2005-2010, 2010-2015 and 2015-2020. This in return leads to progress coefficients of 60, 70 and 80 %, respectively. For first-generation bioethanol, progress coefficients of 70, 80 and 85 % were estimated. In autoregressive time series models, these progress coefficients are sequentially multiplied with previous values to derive operational and total production costs for specific points in time. Based on our scale size estimates for the different types of biofuel (see Fig. 2), scale effects were incorporated into biofuel conversion costs depending on the output of biofuel product (10, 50, 100, 250 and 250 kilotonnes per year). Table 4 is one example for the application of our assumptions in order to calculate conversion costs. It represents the results for first-generation bioethanol.

1.1.2 Total Production Costs

Depending on the type of raw material, different numbers of litres of biofuel can be produced from one tonne of feedstock. A conversion factor was implemented in order to translate prices for one tonne of raw material into the prices per litre of produced biofuel. Production costs were calculated as the sum of raw material costs and conversion costs. For a better comparison, energy density factors (in Millijoule per litre, MJ/L) were taken into account and normalised to the average energy den­sity of fossil fuel. The results were adjusted production costs, based on the specific density of biofuels. Reference scenarios were calculated for 2015 and 2020. This model enables the calculation for different production scales in place and planned or hypothetical scales (e. g. simulation of not yet realised production scales).

As previously mentioned, the price of fossil fuel is the decisive factor for biofuel market success. Therefore, it is essential that biofuel production costs can compete

Table 4 Modelling of conversion costs for first-generation ethanol Scale Investment Depreciation Operational costs Total costs = operational expenses plus depreciation 2005 2010 2015 2020 2005 2010 2015 2020 (kt> (ml) (m Euro) (m (Cent/1) (m Euro) (Cent/1) (m Euro) (Cent/1) (m (Cent/1) (m Euro) (Cent/1) (m Euro) (Cent/1) (m Euro) (Cent/1) (m Euro) (Cent/1) (m (Cent/1) Euro/ Euro) Euro) year) Process step 1: enzymatic hydrolysis Learning curve 0.70 effect 2005-2010 Learning curve 0.80 effect 2010-2015 Learning curve 0.85 effect 2015-2010 10 13 10 0.50 3.95 6.00 47.40 4.20 33.18 3.36 26.54 2.86 22.56 6.50 51.35 4.70 37.13 3.86 30.49 3.36 26.51 50 63 35 1.75 2.77 12.00 18.96 8.40 13.27 6.72 10.62 5.71 9.02 13.75 21.73 10.15 16.04 8.47 13.38 7.46 11.79 100 127 50 2.50 1.98 18.00 14.22 12.60 9.95 10.08 7.96 8.57 6.77 20.50 16.20 15.10 11.93 12.58 9.94 11.07 8.74 250 316 75 3.75 1.19 24.00 7.58 16.80 5.31 13.44 4.25 11.42 3.61 27.75 8.77 20.55 6.49 17.19 5.43 15.17 4.79 500 633 100 5.00 0.79 30.00 4.74 21.00 3.32 16.80 2.65 14.28 2.26 35.00 5.53 26.00 4.11 21.80 3.44 19.28 3.05 Process step 2: fermentation Learning curve 0.70 effect 2005-2010 Learning curve 0.80 effect 2010-2015 Learning curve 0.85 effect 2015-2010 10 13 15 0.75 5.93 9.00 71.10 6.30 49.77 5.04 39.82 4.28 33.84 9.75 77.03 7.05 55.70 5.79 45.74 5.03 39.77 50 63 53 2.63 4.15 18.00 28.44 12.60 19.91 10.08 15.93 8.57 13.54 20.63 32.59 15.23 24.06 12.71 20.07 11.19 17.68 100 127 75 3.75 2.96 27.00 21.33 18.90 14.93 15.12 11.94 12.85 10.15 30.75 24.29 22.65 17.89 18.87 14.91 16.60 13.12 250 316 113 5.63 1.78 36.00 11.38 25.20 7.96 20.16 6.37 17.14 5.41 41.63 13.15 30.83 9.74 25.79 8.15 22.76 7.19 500 633 150 7.50 1.19 45.00 7.11 31.50 4.98 25.20 3.98 21.42 3.38 52.50 8.30 39.00 6.16 32.70 5.17 28.92 4.57

effect

2005-2010

Learning curve 0.80

effect 2010-2015

Learning curve 0.85

effect 2015-2010

with those of fossil fuels. This was the main focus for our comparative analysis. To compare production costs, historical prices for raw materials were extrapolated in the course of reference scenarios of the fossil fuel price. The identification of eco­nomically promising biofuel technologies was then enabled through modelling of projections for technological advancements in respect to production scale and learn­ing effects. In other words, our approach enables the comparison of different biofu­els’ production costs while considering the specific development state, economies of scale in context of realistic scenarios for the market prices for biomass. Plausibility checks based on current data as well as consistency of the results across production technologies enhanced the accuracy of the results. At the same time, we assessed the comparability of data and performed corresponding adjustments if necessary.