Although a plethora of pretreatment methods have been developed in the recent years, very few could be applied in pre-commercial stage. It is therefore difficult to ascertain which method is the best in terms of its efficiency. The term ‘effi­ciency’ includes several criteria, and for a pretreatment method to be an efficient one, it should suffice all or partly. The key criteria for an efficient pretreatment technology and process are as follows (IEA 2008; Kumar et al. 2009):

• must reduce the crystallinity of cellulose and increase porosity of the material;

• increase the yields of both hexoses and pentoses in downstream processing;

• avoid the loss/degradation of sugars;

• recover lignin for further combustion;

• minimize the inhibitors of enzymatic action and fermentation;

• fungibility with different feedstocks;

• avoid expensive capital cost on biomass comminution;

• minimize waste products and use low-cost chemicals; and

• should have low overall capital cost with low energy requirement.

Currently, none of the pretreatment method is suitable for a range of biomass feed­stocks owing to their different degree of action and varying strengths and weak­nesses. Different feedstocks respond to each pretreatment method in a varying way, and it is difficult to find a single method for all feedstocks type. Presently, the dilute and concentrated acid, and steam explosion are very near to commercializa­tion, in spite of their high capital cost. As described in Table 3, each of the methods has its own limitations, which are inherent to the process and difficult to overcome. Therefore, a single pretreatment method cannot have the potential to commerciali­zation unless integrated/combined with other methods. It is imperative to conduct research on combined pretreatment methods to minimize the limitations and overall reduce the capital cost and improve the efficiency of hydrolysis. In fact, all the pre­treatment methods described in Table 3 are in the varying stages of R&D and require extensive trials before any of them reach the commercial viability.

The combined effects of microporous HZSM-5 (Si/Al = 80) catalyst and mesoporous acidic AlSBA-15 on the degradation of HDPE are presented by data in Table 6. It was observed that a conversion of about 100 % yield toward liquid, gaseous, and residue products without any formation of solid waxy compound was achieved. According to literature, these two catalysts constitute principally Lewis and Bronsted acid sites with variable surface areas and pore sizes (Ooi and Bhatia 2007).

However, results shown here are in accordance with the shape selectivity effect in microporous and mesoporous materials. It was associated with the narrow pores to access active sites. It was noted that for acidic enhancement SBA-15 catalyst, the larger pore size and channels allowed the formation of higher hydrocarbon products such as light liquid products. The high yield of gaseous products shown by HZSM-5 catalyst was mainly due to the slow diffusion of cracked products within the internal pores. The findings made in this work were consistent with those reported by pre­vious researchers. It has been reported that the diffusion rate of hydrocarbon mol­ecules could be retarded using microporous catalysts (Urquieta et al. 2002).

Overall, the combined effects of HZSM-5 (80) and AlSBA-15 catalysts gave rise to positive improvements as compared to the performance of the individual cata­lyst. For all three combinations of catalyst ratio, light liquid degradation product yields were higher than 20 wt. %. The optimum liquid yield was demonstrated by the HZSM-5 (80) to AlSBA-15 catalyst ratio of 1:2 (which was 26.5 wt. %). Although the increment of both catalysts increased the liquid yield as compared to the catalyst ratio of 1:1, further addition of AlSBA-15 could provide higher liquid yield while simultaneously suppressing the yield of gaseous product more effec­tively. Another advantage of applying the 1:2 catalyst loading ratio was that it also inhibited the solid coke formation on the catalyst. This could be explained from the theory that larger composition of HZSM-5 catalyst will contribute to higher coke

Fig. 14 Composition of gas products at 673 K using composite HZSM-5(80) and AlSBA-15 catalysts at different ratios

Fig. 15 Liquid degradation products at 673 K using composite HZSM-5 (80) andAlSBA-15 catalysts at different catalyst ratios

formation and more rapid deactivation. In this respect, the mesoporous catalyst is generally more effective than the microporous catalyst in suppressing the formation of large molecules and thus causing less carbon deposits (Urquieta et al. 2002).

As can be seen in Fig. 14, the gas products distribution was quite uniform. For the catalyst ratio of 1:1, the smallest compositions were the carbon chain of C1 (12.4 %) and C5 (12.7 %) while the highest were the carbon chain of C3 (28.9 %) and C4 (28.6 %). However, all of them were marginally different. For unequal cat­alyst mixing ratios such as 2:1 and 1:2, the uniformity of product distribution was not able to be maintained. For HZSM-5(80) to AlSBA-15 catalyst ratio of 2:1, the smallest compositions were the carbon chain of C1 and C5 (about 13.0 %) while the highest proportion was the carbon chain of C4 (31.7 %). On the other hand, for catalyst ratio of 1:2, increasing amount of AlSBA-15 composition resulted in lower production of C5 carbon chain (8.9 %) while showing higher composition in the carbon chain of C3 (31.4 %).

Meanwhile, the effect of varying catalyst mixing ratio on the liquid phase degradation products is illustrated in Fig. 15. Mixture of HZSM-5(80) and AlSBA-15
catalysts with catalyst loading ratio of 1:1 produced nearly 89 % of carbon chain range C8-C12, C13-C16, and C17-C20 from the overall liquid composition. Increasing the ratio of either HZSM-5(80) or AlSBA-15 amount in the catalyst mixture appar­ently shifted the product distribution toward heavier carbon chain. The effect of adding HZSM-5(80) was more dominant as it increased the carbon chain range of C17-C20 to 35.7 % while adding AlSBA-15 only increased the carbon chain range of C13—C16 to 28.4 %.

4 Conclusions

The results reported and discussed in the present work demonstrate that micro — and mesoporous materials show promising properties to be used as catalysts in the degradation of HDPE into gaseous and liquid hydrocarbon fuels at 350-500 °C. HZSM-5 (14), HZSM-5 (80), SBA-15, and AlSBA-15 were used under various operating conditions to obtained liquid biofuels. Mixture of HZSM-5 (80) and AlSBA-15 with ratio of 1:2 exhibits higher degradation activity to yield higher liquid biofuels with valuable gas product at 400 °C. In addition, significant HDPE conversions into liquid fuel with lower coke contents were achieved in a batch reactor over the HZSM-5 catalyst as compared to mesoporous silica catalyst SBA-15. The pore shape of zeolites was very important for deter­mining their activities and product selectivity in the degradation of polymeric materials because it influenced the degradation and deactivation rates simulta­neously. SBA-15 containing aluminum catalyst was of a potential interest in the cracking of heavier feedstock such as palm oil waste into biofuels. The weaker acid properties exhibited by the mesostructured catalysts, i. e., SBA-15 and Al-SBA-15 were responsible for their reduced gaseous product production capaci­ties. However, their presence coupled with the combination of HZSM-5 catalysts in the conversion of polyethylene materials promoted a substantial conversion of the original long-chain hydrocarbons into lighter liquid hydrocarbon products (up to 26.5 wt. %). Furthermore, larger pore dimensions exhibited by these sol­ids did not allow for any product selectivity, resulting in the possible formation of a wide range of branched hydrocarbon and alkyl derivative aromatic products. These results suggest that catalytic degradation of HDPE leads into higher liq­uid hydrocarbons yield at lower temperature. It is stated that such type of chemi­cal recycling, i. e., conversion of waste HDPE into hydrocarbon feedstock used as resource for biofuel has been recognized as an ideal approach and could sig­nificantly reduce the net cost of disposal. It is concluded that under appropriate reaction conditions, suitable catalysts such as HZSM-5 (80) and AlSBA-15 have the ability to control both the product yield and product distribution from HDPE degradation, potentially leading to a cheaper process with more valuable products such as biofuel.

Pery Francisco Assis Shikida, Adele Finco, Barbara Fran^oise Cardoso, Valdir Antonio Galante, Daliane Rahmeier, Deborah Bentivoglio and Michele Rasetti

Abstract Industrialized countries’ dependence on fossil fuels has been distressing for a long time for countries that do not have self-sufficiency, whether for environ­mental, economic, geopolitical, or other reasons. In this context, it is understood that the burning of fossil fuels contributes to greenhouse gas emissions (GHG) increasing the risk of intensifying climatic disturbances that can deteriorate the processes of production, consumption, and welfare in the world. Therefore, the development of alternative energy sources can provide solutions for the gaps, since reducing exposure to the vulnerability of supply and price volatility, environmental issues, and even the development of new investment opportunities in these coun­tries. This is due to the possibility of developing innovations in the production and processing industry, which would contribute to the economic activity. Thus, increasing the use of bioenergy is one of the existing ways to reconcile the need to [7] [8]

expand the supply of energy with the slowdown in global warming, i. e., the most important and disseminated use would be the biomass power generated by the consumption of biofuels, once it reduces GGE emissions.

1 Introduction

Global ethanol and biodiesel production are projected to expand at a slower pace than in the past. Ethanol markets are dominated by the USA, Brazil, and, to a smaller extent, the European Union. Biodiesel markets will likely remain domi­nated by the European Union and followed by the USA, Argentina, and Brazil.

The world biofuels production reached almost 124 billion liters in 2011; 80 % of that global production of liquid biofuels consists of ethanol and 20 % consists of biodiesel. The European Union produced in 2011 about 9.5 million metric tons of biodiesel, but in 2011, the production decreased about 10 % compared to 2010. However, the share of biodiesel is rapidly increasing due to emergence of new pro­ducing countries in Southeast Asia. The USA and Brazil are the largest ethanol producers, with 54 and 34 % of global ethanol output in 2009, respectively; while the European Union accounts for 57 % of global biodiesel production.

Brazil is the world’s second biggest producer of fuel ethanol (about 23 billion liters in 2011) and the world’s biggest exporter of fuel ethanol. The production started in the early 1970s by a program which led to the development caused by local automobile companies with flex-fuel engine technology. Presently, around half of all Brazilian cars use these hybrid engines, which can run with any mixture of pure ethanol and gasohol (around 80 % gasoline and 20 % ethanol). In 2010, cars used nearly equal volumes of gasoline and ethanol.

The chapter aims at revisiting the recent developments in biofuels markets and their economic and environmental impacts. The analysis compares the perfor­mance of ethanol versus biodiesel produced in Brazil and Europe, respectively.

This chapter is organized as it follows: Sects. 2 and 3 discuss the scenario of Brazilian ethanol and European biodiesel in terms of policies, production, sup­ply, and demand. Section 4 examines the environmental impacts of both biofuels. Finally, we draw key conclusion.

Gunter Festel, Martin Bellof, Martin Wurmseher, Christian Rammer and Eckhard Boles

Abstract The current taxation benefits for biofuels are only temporary. Therefore, biofuel production costs need to be able to compete with those of conventional fuels in order to gain market share in the future. However, highly complex influ­encing factors make a comparison of biofuel production costs with those of fossil fuels challenging. This chapter has three major goals: (1) a projection of future feedstock prices for biofuels based on the development of the price for crude oil, (2) a simulation of the effects of likely economies of scale from scaling-up produc­tion size and technological learning on production costs and (3) a scenario analysis comparing different biofuels and fossil fuels. European biofuel production costs for 2015 as well as 2020 are projected based on a calculation model for biofuel production. Our scenarios assume prices for crude oil between Euro 50 and Euro 200 per barrel for both reference years. Our results indicate that mid — to long-term, second-generation biofuels are very likely to achieve competitive production costs, if technological learning and economies of scale are factored in. Bioethanol made

G. Festel (*)

Festel Capital, Mettlenstrasse 14, 6363 Fuerigen, Switzerland e-mail: gunter. festel@festel. com

G. Festel • M. Bellof

Autodisplay Biotech GmbH, Merowinger Platz 1a, 40225 Dusseldorf, Germany G. Festel • M. Wurmseher

Butalco GmbH, Mettlenstrasse 14, 6363 Fuerigen, Switzerland G. Festel • M. Wurmseher

Department of Management, Technology, and Economics, Swiss Federal Institute of Technology Zurich, Zurich, Switzerland

C. Rammer

Centre for European Economic Research (ZEW), Mannheim, Germany E. Boles

Institute of Molecular Biosciences, Goethe-University Frankfurt,

Frankfurt am Main, Germany

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_5, © Springer-Verlag London 2014

from lignocellulosic biomass and biodiesel from waste oil promise the highest cost-saving potential in all crude price scenarios and are capable of outperforming fossil fuels and first-generation biofuels in the future.

Keywords Biofuels ■ Production costs ■ Scale effects ■ Learning curve effects

1 Introduction

The economic dependence on fossil fuels and the potential replacement of crude oil by biomass have been investigated in research chapters. Biomass use for bio­fuels competes with residential applications, heat/power generation and the pro­duction of food, animal feed and other industrial products. This is the reason for negative influences of first-generation biofuel production on global food prices. Whenever crude oil prices rise, the positive correlation between the production scale of biofuels and food prices becomes clearly visible, due to arbitrage effects (Chen et al. 2010). For example, a rising oil price has significantly influenced production volumes, and prices of agricultural grain on a global basis, as the pro­duction of biodiesel and bioethanol from soybeans and corn, respectively, have grown accordingly. The fact that biomass can serve as raw material for chemicals and numerous other applications is not solely the fuel industry that drives prices (Swinnen and Tollens 1991; Hermann and Patel 2007). Due to substitution effects, the price for raw materials is not only dependent on the development of biomass markets but also on the cost of fossil raw materials, such as crude oil. Conversion costs are driven by scale effects as well as time-dependent learning effects.

In order to better understand complex energy production systems under various policy objectives, numerous different calculation models have been developed. Both technical bottom-up approaches as well as macroeconomic top-down approaches have been utilised to describe the entire energy system (de Wit et al. 2010). Other authors evaluate whole supply chains for bio-based products (Stephen et al. 2010; Kim et al. 2011), biorefinery concepts (Fernando et al. 2006; Clark 2007; Francesco 2010) as well as the potential of biofuels for individual countries (Martinsen et al. 2010). For example, a mixed integer linear programming model that allows the selection of fuel conversion technologies, capacities, biomass locations, as well as the logistics of transportation from the raw material locations to the conversion sites and then to the final markets has been established by Kim et al. (2011).

Numerous research chapters have evaluated biofuels, such as biodiesel (Zhang et al. 2003; van Kasteren and Nisworo 2007; Araujo et al. 2010), or simulated biofuel processes with specialised software, such as Aspen HYSYS (West et al. 2008). By contrast, comparisons of one biofuel production process with other production processes (biofuels or fossil fuels) that take scale and learning curve effects into account are rare, even though production costs are imperative to the demand of biofuels. Some studies focus on a single process step, such as enzymes (Tufvesson et al. 2011; Klein-Marcuschamer et al. 2012), while others compare biofuel types through production cost analysis (Bridgewater and Double 1994; Giampietro and Ulgiati 2005; de Wit et al. 2010; NREL 2011). de Wit et al. (2010), for example, show that biodiesel is the most cost competitive type of fuel, dominating the early market of first-generation biofuels. Lower oil crop feedstock prices compared to those of sugar — or starch-containing crops are one of the rea­sons for biodiesel’s better cost performance compared with first-generation bioeth­anol. In addition, capital and operating expenditures for the transesterification of oil to biodiesel are below those for hydrolysis and fermentation of starch to bioeth­anol (de Wit et al. 2010). This chapter intends to calculate the production costs for various types of biofuel in Europe. It will also compare them with production costs of fossil fuels. Raw material, conversion and capital costs are taken into account as well as different scenarios of price development for raw materials and crude oil.

Four steps are central to the developed calculation model in order to analyse and compare biofuel production costs: (1) the definition of biofuel production scenarios in 2015 and 2020, (2) an estimation of future raw material prices based on assump­tions for crude oil price development and the relation between crude oil price and prices for biofuel raw materials which has been observed in the past, (3) the model­ling of scale — and time-dependent costs for capital expenditures and the conversion of biomass and (4) a calculation of the total production costs as a total of raw material, capital and conversion costs. Our model is based on publicly available data for single production process steps and the whole production process. The input data have been collected in expert interviews and intensive literature research during the past 5 years (Festel 2007, 2008). As model output, we have chosen production costs in Euro-Cent per litre, as this is a measure which end users, such as car drivers, can refer to.

Within the next 5-10 years, estimates do not see biofuels gaining a market share larger than 15 % globally (Gnansounou et al. 2009; Bagheri 2011). European Union (EU) targets support this estimation. The EU has set a target market share of 10 % in terms of all petrol and diesel transport fuels by 2020 (EU Commission 2003). That is why future fuel markets prices will still be driven by fossil fuels. Today, it is government regulations and subsidies that enable biofuels to compete with fossil fuels. However, our hypothesis is that government incentives will have a decreasing influence on biofuel demand medium to long term and that demand will be more and more driven by cost competitiveness with fossil fuels through, e. g., new tech­nologies, reduced costs in the production process, improved logistics. In the case that production costs of biofuels will be lower than those of fossil fuels, we expect demand to be high enough to absorb all produced volumes of biofuel. In our model, we do not take the connection between biofuel demand and biofuel prices into account, as the market share of biofuels is determined by its production costs.

In our model, we neglect the option of biofuel import from outside Europe but rather assume that all demand for biofuels within Europe will be met by European biofuel producers. The more developed production infrastructure, economies of scope to other production activities and a close proximity to end users may be a benefit for European production sites. Our input data for production costs are solely focused on Europe. However, our model could easily be adapted to other regions if input is changed accordingly.

Smith and Searchinger (2012) argue that existing life cycle assessments (LCAs) pertaining to biofuels seriously overestimate carbon absorption on the part of bio­energy crops and do not take sufficient account of GHG emissions resulting from the cultivation and harvesting of these crops.

The type of land, i. e. unfertilized grassland, forest land or traditional cropland, used for biomass feedstock is an important determinant of GHGs emitted from the soil (EPA 2006). Preparing fallow or underutilized land for agricultural production usually requires clearing off the majority of the animal and plant species. This can destabilize the soil by releasing significant amounts of stored carbon (EPA 2006). Some studies conclude that conversion of native land such as forest, grassland and abandoned land for biofuel crops leads to carbon debts[15] ranging from one to sev­eral 100 years (Fargione et al. 2008; Gibbs et al. 2008; Fritsche 2008). For exam­ple, Fargione et al. (2008) estimated the carbon debt of producing palm oil on forest land (releasing 3452 tCO2/ha) to be approximately 423 years. Table 5 below provides an overview of the estimated payback periods for a range of biofuels.

In contrast, biofuel crops grown on traditional croplands are less threatening to the environment since they have less embedded soil organic carbon (SOC) (Englund et al. 2011). However, intensive biofuel cultivation, especially if using annual crops, could lead to a substantial release of SOC. This is due to frequent disturbance to the soil (i. e. via tillage), which exposes protected organic matter and increases the rate of mineral decomposition, thereby resulting in lower SOC storage (Grandy and Robertson 2007).

Aside from tillage, farming and irrigation practices could also affect the net carbon balance of biofuels. Mechanized farming or the use of fossil-fuel-powered machinery for soil preparation, sowing, planting, weeding and harvesting activi­ties releases GHGs. Likewise, water for irrigation of biofuel crops is often sourced from rivers, lakes, canals, dams and groundwaters. While this reduces water avail­ability for other uses, it also leads to soil salinization when the irrigation process is poorly managed (Englund et al. 2011). These impacts can be mitigated where rain harvesting systems such as terraces, bunds and small dams are available.

Another issue related to harvesting is mono-cropping, or the planting of only a single species or cultivar. While harvesting a particular biofuel crop on a large-scale over several years makes the process more economical, it can also increase the environmental footprint. Repetitive harvesting of a single variety of crop results in a

lack of biodiversity and a decline in soil fertility. To control pests and maintain yields in such environments, more chemical input and fertilizers are generally applied (Englund et al. 2011), which can lead to serious ecological impacts (more in Sect. 5 in A Comparison Between Ethanol and Biodiesel Production: The Brazilian and European Experiences). However, as Dale et al. (2010) report, such impacts can be minimized by adopting sustainable land management practices.[16]

Studies of LCAs have shown that GHG emissions can vary substantially between biofuels, but are mostly lower than those associated with conventional fossil fuels. Through a meta-analysis of LCA literature, Davis et al. (2008) found that the results range between -89 MgCO2 per hectare per year for corn-based biofuel (Farrell et al. 2006) to 9.6 MgCO2 per hectare per year[17] for biofuel pro­duced from switchgrass (Searchinger et al. 2008). Results also varied between authors for biofuels produced from the same crop. For example, Shapouri et al. (2002) found that corn ethanol reduces CO2 emissions by 1.2 Mg per hectare per year, while Delucchi (2006) determined that it increased CO2 emissions by 5.14 Mg per hectare over the same period. Some studies reported the results in terms of change in GHG emissions compared to fossil fuels. The variation in this case was once again large and ranged between -114 % for switchgrass (Adler et al. 2007) to 93 % for corn (Searchinger et al. 2008). LCAs, however, have often overlooked the impacts of LUC on overall GHG emissions. When Bailis and Baka (2010) compared biodiesel from Jatropha in Brazil with conventional biodiesel without considering LUC, they noted a 55 % reduction in GHGs. In contrast, when they included LUC, the net emissions were estimated to increase by 59 %.

Despite providing a cradle-to-grave assessment, LCAs therefore reach varying conclusions on any biofuel depending on the methodological approach adopted. While using an LCA should ideally be an ongoing process for handling and prior­itizing information as new data comes to hand, it is worth noting the “seven grand challenges” that McKone et al. (2011) identified for undertaking a comprehensive LCA of biofuels. These are

• Understanding farmers, feedstock options and practices.

• Predicting biofuel production technologies and practices.

• Characterizing tailpipe emissions and their health consequences.

• Incorporating spatial heterogeneity in inventories and assessments.

• Accounting for time in impact assessments.

• Assessing transitions as well as end states.

• Confronting uncertainty and variability.

A proper understanding of these issues will have profound implications with respect to what feedstocks should be used for biofuel production, together with what lands are most suitable for environmentally sustainable feedstock produc­tion. Any conclusion reached from an LCA must consequently be tempered by the knowledge that the same assessment could provide a different result at another point in time.

Commercial algae production facilities employ both open and closed cultivation systems. Each of these has advantages and disadvantages, but both require high capital input (Pienkos and Damns 2009). Open ponds are much more cheaper than closed systems because it demands relatively high capital and O&M costs associated with installation and operation of PBRs (Benemann 2012; Buford et al. 2012).

Lower costs and the possibility to scale up to several hectares make open ponds the main choice for algae commercial production (Benemann 2012). However, open pond cultures suffer from many limitations that can disrupt algal productiv­ity during unexpected environmental events. Another challenge for this system includes having access to an adequate supply of water for growth due to continu­ing loss of water through evaporation. Therefore, open ponds must be in a geo­graphic setting that has a fairly near source of water and a relatively flat terrain to avoid costly earthworks (Buford et al. 2012). Moreover, the open systems are susceptible to wind-born biological agents that can affect the cultivation, such as grazers, infectious fungi, lytic bacteria, viruses, other algae, and also lower tem­peratures in colder climates (Benemann 2012).

These open pond limitations stimulate PBRs development; however, only a few commercial plants use closed PBRs systems, mainly due to high costs as men­tioned before. Nowadays, according to Benemann (2012), microalgae cannot be grown in PBRs for commodities and are not even successful for high-value prod­ucts. However, PBRs can be used for seed culture production, though only for ~0.1 % of the biomass. Closed PBRs are significantly more expensive to construct, but have not been engineered to the extent of other reactors in commercial prac­tice, and so there may be opportunities for significant cost reductions.

Neither open ponds nor closed PBRs are mature technologies. Therefore, until large-scale systems are built and operated over a number of years, many uncer­tainties will remain. Cultivation issues for both open and closed systems, such as reactor construction materials, mixing, optimal cultivation scale, heating/cool — ing, evaporation, O2 build-up, and CO2 administration, have been considered and explored to some degree, but more definitive answers await detailed and expansive scale-up evaluations (Pienkos and Darzins 2009).

Concerning the various algal species and strains, they vary from study to study, depending on location and culture techniques. For that reason, it is not yet possible to predict what species or strain will be the best suited for commercial biofuel pro­duction, but it is most likely that it will differ from case to case, depending on the location, cultivation techniques chosen, processing technologies available, nutri­ents source, local climacteric conditions, and among other potential factors.

The cost of producing biodiesel depends on a number of factors, including the feedstock used in the process (i. e., the production cost of biomass), the capi­tal and operating costs of the production plant, the current value and sale of by­products, and the yield and quality of the fuel and by-products. Table 8 provides total and unit production costs of a representative European biodiesel plant (Italy) using rapeseed oil as feedstock (2010), which is a good example that includes the average characteristics of Italian plants, on the base of the information collected through firm survey (Finco 2012). The plan has capacity for 150,000 tons and pro­duces 150,000 tons of biodiesel.

Table 8 shows that the major economic factor to consider for input costs of bio­diesel production is the feedstock, which is about 80 % of the total production cost. This means that the market trend commodities prices highly influence the result of the biodiesel industry. In particular, feedstock costs can vary significantly from region to region due to their availability and market fluctuations, which can also make biodiesel production costs vary over time. Vegetable oils prices have changed significantly in the last 5 years. The prices have been rather stable until end of 2006, while from 2007 to 2008, they are more than doubled, declining again in 2009 reaching the 2006 level. In the second semester of 2010, the price registered another increase followed by a slight fall in 2012 (OECD-FAO 2012).

Table 9 shows the net margin of our representative plant. Nowadays, our plant perceives a negative economic result because revenues do not cover production costs. This result is mainly driven by the biodiesel price that is fixed by the refiner­ies and it is not connected with the production costs.

There are two components that influence the value of biodiesel: the diesel price on Platts and a premium price. The premium is determined by the refinery indus­try, and it depends on the vegetable oils price and the contractual power of the biodiesel plant. Technically, the premium price should correspond to the difference between the production costs and the diesel price on Platts, which biodiesel pro­ducers widely call the ‘business margin.’

However, according to the data from biodiesel plants, the premium price per­ceived corresponds to approximately 65 % of the ‘business margin.’ Moreover, this percentage depends on the policies adopted by the Governments, such as tax excise reductions or subsidies.

It is important to underline that biodiesel plants use a blend of vegetable oils and, consequently, the price can probably be lower than the rapeseed oil price that was used in the Table 9. Taking this into account, the results present an accurate representation of the Italian biodiesel industry.

However, the increased price of vegetables oil, the economic crisis, and policy changes at European level had negative impact on biodiesel production. For exam­ple, in Italy, the reduced tax exemption in 2009 and the subsequent abolition has diminished the profitability of the biodiesel plant realizing losses.

Lastly, the third attribute refers to the specificity of the assets involved in the trans­action. Assets are specific if their return depends on the continuity of a specific transaction. The more specific the asset, the greater the agents’ dependence on achieving the negotiation and therefore the greater the loss from an opportunistic behavior by one of the parties.

Williamson (1985) also proposes classifying the different ways a given transac­tion is performed, starting with the spot market, continuing with long-term con­tracts and concluding with the hierarchy (a single firm securing the transaction in question). If the asset specificity is null, the TCs are negligible, requiring no con­trol over the transaction; therefore, the spot market would be more efficient than other organizational forms. If, instead, the asset specificity is high, the costs asso­ciated with breaching the contract will be high, which would imply greater control over the transactions.

Also according to Williamson (1981), asset specificity is the most important critical dimension, as it is related to the type of investment. Thus, after perform­ing the specific investment, the seller and the buyer will operate in a bilateral exchange relationship for a considerable period of time (irreversibility cost). Williamson (1991a, b) discriminates six types of asset specificity:

a. locational: those whose application in a given transaction generates cost sav­ings in transport and storage, meaning specific returns to these productive units;

b. physical: those more suitable for a specific purpose (e. g., specific inputs for the production of a specific product);

c. human: related to the use of specialized human capital for an activity. This type of specificity is related to accumulated knowledge by the continuous execution of a particular activity;

d. dedicated: specific assets for a given transaction (e. g., to service a specific customer);

e. brand: refers to capital—not physical or human—manifested in a company’s brand, which is particularly relevant in the franchising world; and

f. temporal: refers to the value of the assets related to the period when the trans­action is processed. Thus, this asset becomes especially relevant in the case of negotiating perishable products.

According to Azevedo (2000), as it is not possible to determine a relationship that contains all eventualities, in some cases, renegotiation is inevitable. However, as an opportunistic behavior is a possibility, this renegotiation is subject to one of the parties taking advantage of the gains, which in turn results in losses to the other party. Thus, in economic transactions, based on the issue of opportunism, one side could try to take advantage of the other due to the impotence of predicting future events. Hence, agents often have to resort to safeguard contracts, which in turn contribute to increase some TCs.

There are some forms reported in the literature that enable controlling the prob­lems of post-contractual opportunism, namely increase the resources to monitor transactions, reduce information asymmetry, and adopt contractual incentives rewarding the agents’ compliance or good performance. The vertical integra­tion itself can eliminate conflict of interest, especially in transactions between an organization and its suppliers, reducing TC, though this integration could increase operating costs (Bonfim 2011).

On account of the intrinsically qualitative competitive process, the literature generally does not address the governance structures and the theory of competi­tiveness. This supposes, mistakenly, that the coordination of supply chains occurs efficiently or that more efficient structures through mechanisms associated with competitive rivalry are used (Farina 1999).

Coutinho and Ferraz (1995) pointed out that strategies are the basis of the dynamics of competitiveness, which seek to expand and renew the companies’ capacity required by the standards of competition (or “rules of the game”) in the market they are embedded.

Buainain et al. (2007) deem that competitiveness will only be achieved by including practices that encourage cooperation between the economic agents of a supply chain, including the government. According to the authors, considering that a company’s competitiveness is linked to the system it is inserted in could mean significantly changing the way such company views and manages its business. Thus, the authors emphasize the importance of vertical and horizontal manage­ment within a system to gain competitiveness. According to Buainain et al. (2007), a serious problem is the lack of works and experiences that report the problems of internal management in the family farmers’ network, as well as the relationship between them and their customers and suppliers.

Thus, competitiveness is reflected by these companies’ greater or lesser ability to adopt governance structures that reduce TC, enable greater integration with the agricultural production, and set conditions for systemic competitiveness (Batalha and Souza Filho 2009).

Quality control of the final product requires a large and varied number of chemi­cal analyses to evaluate the physical and chemical parameters in comparison with quality standards, usually established by regulatory legislation. Table 7 shows the specifications and analytical methods for the quality control of ethanol, an impor­tant Brazilian biofuel.

These data highlight the large number of techniques required to ensure ethanol quality, from classical techniques (volumetry, gravimetry, and direct potentiometry) to instrumental techniques (ion chromatography, GC-flame ionization detector, and AAS). The method for each analytical technique needs to be rigorously and systematically applied in order to enable accurate comparison between samples and to accurately assess the quality of the sample.

Figure 8 shows a flowchart for the use of AAS for quality control of ethanol. AAS is a rapid technique for the determination of the presence and concentration of several metals and some nonmetals. Nevertheless, preparation steps require attention because this step will release the analyte into the solution to be meas­ured. If not all of the species is released into the solution, inaccurate results will be obtained. The analytical result could be obtained as a concentration (mg kg-1 or mg L-1) or as a mass percentage in a certain volume (% m/v), depending on the individual’s interest or standard regulation.

Ahmad Zuhairi Abdullah, Shazia Sultana, Steven Lim and Mushtaq Ahmad

Abstract The potential application of acidic HZSM-5 and AlSBA-15 materials for catalytic degradation of high-density polyethylene (HDPE) into liquid hydrocarbon fuels was investigated using a tubular batch reactor. The reaction was carried out at various catalyst loadings between 5 and 15 % with 1:1, 2:1, and 3:1 HZSM-5 to AlSBA-15 ratios. The catalysts exhibited remarkable catalytic activity with conver­sions into liquid light hydrocarbons of up to 25 %. The gaseous product distribution showed a wide spectrum of hydrocarbons (Ci-C5) while the most predominant prod­ucts were C3 and C4 (47 and 40 %, respectively). Meanwhile, the liquid products were mostly in the range of C8-C25 depending on the reaction parameters and the amount produced decreased with increasing carbon number. Thus, catalytic degrada­tion of HDPE was a promising route for obtaining valuable fuels and petrochemicals from waste polymers. It required relatively low degradation temperatures to obtain liquid hydrocarbons with boiling points within that of gasoline range. At the same time, it could reduce the environmental problems caused by waste polymers.

Keywords High-density polyethylene • HZSM-5 • ALSBA-15 • Catalytic degradation • Hydrocarbon fuel

1 Introduction

In recent decades, plastic materials consumption has undergone a significant growth. According to an estimate, the global production and consumption of plas­tics have increased by about 10 % annually. At the same time, synthetic plastics

A. Z. Abdullah • S. Sultana (*) • S. Lim

School of Chemical Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Penang, Malaysia e-mail: shaziaflora@hotmail. com

M. Ahmad

Biofuel Laboratory, Quaid-i-Azam University, Islamabad 45320, Pakistan

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_13, © Springer-Verlag London 2014

recycling has gained considerable attention all over the world due to the serious environmental problems caused by waste plastics and resources needed for man­ufacturing of such huge quantity of plastic materials. There are several methods practiced currently for degradation and disposal of high-density polyethylene (HDPE) such as landfill, thermal degradation, and incineration. However, all of these methods have not gained social acceptance for disposal of the waste mate­rial (Lee et al. 2002). The disposal is also currently becoming legally restricted because of a decrease in landfill availability, cost increase, and strong pollution concerns such as emissions of various combustion products.

Recycling of plastics should be projected to minimize the pollution. It has lower energy demand to support the process to offer high energy conservation while at the same time enhancing the efficiency of the process. Plastics recycling technolo­gies have been historically divided into four general types. Primary and secondary recycling processes involve the processing of waste/scrap materials into a product with characteristics similar to and different from those of original product, respec­tively. Tertiary recycling involves the production of basic chemicals and fuels from plastics waste/scrap as part of the municipal waste stream or as a segregated waste. Meanwhile, quaternary recycling process retrieves the energy content of waste/ scrap plastics by burning or incineration. Although the primary and secondary recy­cling processes are the most common being applied in the society, tertiary recycling and quaternary recycling have been regarded as more sustainable (Garforth et al. 1997). In contrast to landfill, thermal, and incineration methods used for degrada­tion of HDPE, chemical recycling using catalysts has emerged as a potentially inter­esting alternative. It can achieve degradation of plastic wastes for conversion into a variety of useful products, mainly as liquid fuels and raw chemical feedstock (Lee et al. 2002). In this regards, catalytic degradation using materials such as zeolites is considered to be suitable due to their unique pore diameter. These materials seem to be especially useable as catalyst supports for waste polymer degradation.

Generally, zeolites are aluminosilicate members of the family of microporous solids known as ‘molecular sieves’ and an ‘open’ structure that can accommodate a wide variety of cations. Meanwhile, mesoporous silicates such as MCM-41 and SBA-15 are porous silicates with huge surface areas (normally >1,000 m2/g), large pore sizes (2 nm < size < 20 nm), and ordered arrays of cylindrical mesopores with very regular pore morphology (Garforth et al. 1997).

In recent years, several researchers have reported the synthesis of a new class of porous materials which are supposed to combine the properties of both zeolites and ordered mesoporous aluminosilicates (Trong et al. 2001). Some examples of mesoporous materials that have been investigated in the past include MCM-41, KFS-16, and SAPO-37 (Jalil 2002; Marcilla et al. 2002; Miskolczi et al. 2005; Sakata et al. 2002). In continuing the efforts to degrade the HDPE waste into useful products such as liquid fuel, this study has been initiated. The objective of the research is the use of a laboratory tubular batch reactor to study: (i) the potential application of HZSM-5 and AlSBA-15 as acidic microporous and mesoporous catalysts for degradation of HDPE, (ii) the activ­ity of these catalysts and their effects under various operating conditions on product dis­tribution and selectivity, and (iii) to enhance the potential benefit of catalytic polymer recycling for industrial application in the future.