Biorefinery scale-up is a common approach to reducing costs by capital intensification. This approach has been proven in the fossil fuel industry where the average US coal plant generates over 227 gigawatts (in compari­son, the energy production rate of a 100 million gallon (379 million liter) per year ethanol plant is equivalent to about 280 megawatts). Although biorefineries can benefit from economies of scale, they also suffer from diseconomies of scale that limits their ability to increase capacity (Wright and Brown, 2007b). Here we discuss the impact of scale on biorefinery costs and strategies to mitigate diseconomies of scale.

Product costs consist of three major categories: capital, operating, and feedstock costs. Capital costs include equipment depreciation, taxes, and the return on investment required to recoup the initial capital with a desired profit. Operating costs are expenses required to operate the facility such as
labor and maintenance. Finally, feedstock costs are spent to acquire requisite raw materials. These costs are difficult to estimate, which has led to the development of sophisticated tools that depend on prior knowledge to determine costs for novel processes.

Product costs are a function of a plant’s capacity. The relationship between a plant’s capacity and the various cost components can be approximated with power law equations (Wright and Brown, 2007b):

[2.3]

where C, O, and F stand for capital, operating, and feedstock costs. Variables with subscript 0 correspond to known costs and capacities for a baseline facility. The scale factors n, m, and p relate to the scaling behavior of each cost component. Scale factor values vary between technologies, but there are commonly accepted values employed in industry for major facility categories. In the thermochemical industry, n is commonly assumed as 0.63 or 0.7, m is approximately linear (between 0.9 and 1), and p can be either smaller or greater than 1. In general, scale factors that are less than unity represent product costs that decrease with plant capacity. For example, a 0.7 scale factor suggests that every 1% increase in capacity incurs a smaller 0.7% increase in capital costs. Biorefineries are unique in the fuel industry for having large p scale factors (in the order of 1.5), meaning that feedstock costs increase with plant capacity. Figure 2.4 shows unit capital costs of corn ethanol biorefineries versus plant capacity.

Economies of scale are strong incentive to build large biorefineries. However, beyond an optimal capacity, feedstock transportation costs increase at a faster rate than reductions in capital costs. Engineers are evaluating distributed processing strategies to alleviate transport costs.

Distributed processing is the notion that small-scale facilities pretreat biomass prior to shipping to a large, upgrading facility. This concept yields several economic benefits: reduced storage space requirement, slower biomass degradation rates, and improved energy densities. Pretreatment intensity varies from simple drying and grinding to torrefaction or even pyrolysis.

Biomass drying and grinding removes moisture, which can otherwise increase biomass degradation rates by encouraging microbial activity. Grinding increases the feedstock bulk density and allows for pelletization, which is the mechanical compression of loose material into dense pellets. Torrefaction increases the energy density by further lowering the moisture content, releasing low energy value compounds, and slightly modifying biomass structure. Torrefied biomass is hydrophobic, which makes it

ideal for long-term storage. Torrefaction allows for efficient biomass pulverization, which increases the energy density. Finally, biomass pyrolysis, or liquefaction techniques, convert biomass into a liquid form with high material density. Pyrolysis oil, combined with biomass char materials, is an energy dense material that could be shipped at low cost, but corrosion remains a challenge.

Distributed processing in specialized, small-scale facilities or depots will play an important role in a large-scale biorefinery industry (Wright and Brown, 2007b). Without pretreatment, biomass transportation will be expensive and increase the number of trucks delivering material to central facilities. Unfortunately, distributed processing faces the classical ‘chicken or egg’ problem: without large-scale facilities there is a limited market for pretreated biomass feedstock, and without distributed processing facilities may find it difficult to achieve large-scale capacities. Pretreatment technologies may initially feed into the existing fossil fuel infrastructure by delivering torrefied biomass to coal plants or bio-oils to oil refineries. However, some technical and economic hurdles for this alternative remain unsolved.

The use of renewable feedstock is one of the cornerstones of modern green chemistry. Non-renewable fossil resources supply 86% of our energy and 96% of organic chemicals (Binder and Raines, 2009). But fossil resources are not renewed in a time interval relevant to our resource consumption: according to our actual consumption, the future petroleum production is unlikely to meet our society’s growing needs: by 2025, our energy demands are expected to increase by 50% (Ragauskas et al, 2006). Other drivers are pushing for the substitution of chemicals used daily in consumer products: safety concerns for both humans and the natural environment. Volatile chlorinated compounds used in dry cleaning, sulphonated surfactants, and polybrominated compounds in flame retardants are compounds used in formulations and processes for which replacement molecules would be preferred. Research on the production of cost-effective alternatives derived from renewable resources is an area of primary importance if we want to satisfy the requirement for green and sustainable chemicals and products. Green chemistry now embraces the whole life cycle of a product (see Fig. 1.2), rather than just focusing on the production stage. Upstream and downstream stages of the production, including the raw material employed, its use, end-use and disposal, are included, guaranteeing the true sustainability of a product (Anastas and Lankey, 2000).

Improvements in today’s modern formulating-based industries at the production stage of the life cycle, are restricted (although moving towards renewable energy and zero waste is important and not trivial). The use of renewable feedstocks could offer an important margin for progress,

Re-use

Use

Product

especially for companies, such as consumer goods manufacturers, keen to dramatically improve the environmental performance (decrease the CO2 emissions) of their products.

Researchers have developed detailed TEAs for several biofuels including hydrogen, methanol, ethanol, mixed alcohols, Fischer-Tropsch liquids, and naphtha and diesel range blend stock fuels (Wright and Brown, 2007a). Biofuel synthesis pathways can be categorized by the feedstock intermediate, subsequent upgrading process, and type of biofuel output. Feedstock intermediates are classified here as the primary products from biomass torrefaction, pyrolysis, and gasification. Upgrading processes are associated with specific intermediate products, although torrefied biomass and bio-oil can be converted into syngas and upgraded through alternative pathways. An overview of the main thermochemical biomass-to-liquid fuel pathways is shown in Fig. 2.1.

The syngas pathway leads to several types of biofuel products depending on the upgrading process: alcohol synthesis can output mostly methanol,

ethanol, or mixed alcohols; methanol from alcohol synthesis can be further upgraded to gasoline and liquefied petroleum gas via the methanol-to — gasoline (MTG) process; steam reforming results in high hydrogen yields, and Fischer-Tropsch synthesis generates a mixture of hydrocarbons ranging from light gases to waxes (C1 ^ C120). Fischer-Tropsch liquids can substitute for diesel, but they have slightly different properties than conventional diesel. Therefore, some studies include a hydroprocessing unit to increase the output of naphtha — and diesel-range biofuels.

Capital costs vary significantly between different biofuel synthesis routes and plant configurations. Table 2.1 shows capital costs for biomass conversion to hydrogen, methanol, mixed alcohols, and Fischer-Tropsch liquids via syngas production and conversion. These capital costs have been re-categorized from the original analyses according to major process steps or sections. In general, pretreatment includes feedstock drying and grinding; gasification consists of the gasifier and auxiliary equipment; oxygen separation refers to the air separation island; gas cleaning includes particulate, tar, and impurity removal; syngas conditioning refers to water gas shift or reforming processes required for synthesis; synthesis/production is the main step to produce the desired fuel based on given specifications; steam and power generation, and utilities/miscellaneous group all auxiliary units required for the overall operation of the facility.

The selection of processes shown in Table 2.1 includes high and low temperature gasifiers; oxygen and air blown gasification systems; and steam and power export configurations. High temperature gasifiers tend to be more expensive due to strict metallurgy and operating constraints, but they deliver a higher quality syngas, which reduces gas cleaning and conditioning costs. Air blown gasifiers do not require air separation equipment, which is expensive, but they dilute syngas with nitrogen, which increases the size of downstream equipment. Excess heat and fuel gas are typically converted into steam and/or power depending on the quantity and quality available.

Biofuel costs include feedstock and operating costs in addition to annualized capital costs. Feedstock costs typically contribute between a quarter and more than half of the final biofuel cost because of high biomass costs. By-products, mostly heat and power, can sometimes contribute significant revenue.

Table 2.2 shows the annualized costs for selected biofuels via the syngas pathway. These costs illustrate some of the differences in assumptions found in the literature. Although the biorefinery capacities are similar, the annualized capital, operation and management, and biomass costs vary widely. Annual capital expenditures, including depreciation and capital charges, depend on the estimates described in Table 2.1 and several financial assumptions. Operation and management costs are typically based on local labor rates and factors for equipment maintenance. The differences in

aMFSP: Minimum fuel selling price.

bAssumes 34.4MJ/L Fischer-Tropsch liquid energy density.

CGGE: gallon of gasoline equivalent (32.3 MJ/L gasoline energy density).

biomass costs are due to the wide range of assumed feedstock prices ($30—$75 per metric ton). Finally, we should note that these cost assessments involve assumptions for process maturity and projections for technology improvement that significantly impact the final estimates.

There are fewer techno-economic analyses for alternatives to the syngas thermochemical pathway, such as bio-oil upgrading and hydroprocessing of lipids. This is due in part to the commercial maturity of these processes. There has been little commercial adoption in the biomass industry of alternative processes such as biomass pyrolysis to bio-oil and hydrothermal processing to bio-crude despite being under development since the 1970s. However, with rising petroleum costs, there is renewed interest in processes that replace petroleum products beyond transportation fuels.

The following processes adopt alternative routes to producing transportation fuels with valuable co-products. Biomass pyrolysis produces primarily bio-oil, which can be upgraded to fuels. Pyrolysis co-products include biochar — a soil amendment and potential carbon sequestration agent, and chemicals. Extraction and hydrolysis of bio-oil recovers sugars (pentose and hexose) that can subsequently be fermented to ethanol. Biorefineries could obtain high valued chemicals benzene, toluene, ethylene, and propylene among other hydrocarbons from bio-oil via integrated catalytic processing (ICP) using a modified HZSM-5 catalyst. Syngas fermentation could produce polyhydroxyalkonate (PHA), a biodegradable polymer, while yielding excess hydrogen. Table 2.3 shows capital and operating costs for these alternative biorefineries.

A small number of ventures have commercialized the hydroprocessing of lipids to renewable diesel and jet fuels. Commercial lipid hydroprocessing employs by-products from the food industry such as vegetable oils, waste oils, and fats. Algal biomass has the potential to become the main feedstock

for lipid hydroprocessing if its production costs are drastically reduced (Roesijadi et al, 2010).

Lack of industrial data to support TEA has prompted government initiatives to sponsor the construction of pilot — and demonstration-scale biorefineries such as the National Advanced Biofuels Consortium (NABC). These initiatives help gather process data that guides the direction of future public funding in biofuels research and development.

Industry has yet to meet the RFS2 mandates for advanced cellulosic biofuel production. In fact, the Environmental Protection Agency (EPA) retroactively reduced the 2011 mandate from 250 million gallons (946 million liters) to 6.5 million gallons (25 million liters). The United States Department of Agriculture attributes the lack of advanced biofuel supply to several factors including the high cost of first-of-a-kind biorefineries (Coyle, 2010). Lack of financial investment in these technologies is exacerbated by the lack of industrial data to support them. This can be alleviated with the construction of pilot — and demonstration-scale facilities.

Pilot — and demonstration-scale projects are key milestones for the commercialization of novel technologies. These facilities are not intended to generate revenue, but they sometimes require significant financial commitments. Pilot-scale biorefineries have capacities of less than 5 MT per day, and demonstration-scale facilities can operate at commercial produc­tion rates but are discontinued once operators obtain the necessary data. Industry has so far been reluctant to invest in these capital-intensive demonstration facilities. Unfortunately, there are important processing considerations that can only be tested at commercial scale.

Data from privately funded demonstration projects are almost never released to the general public. However, this data is crucial to help guide policy decisions that may affect the development of biorefinery technologies. Demonstration-plant data will drastically improve the quality of TEA studies, and conversely, TEA will improve in their ability to clarify the path from nascent technology to commercial product.

It is important to ensure that both the resource and the process technology used as well as the products made are environmentally acceptable. The twentieth century saw the development of processes designed for the production of energy and organic chemicals based on the oil refinery. The twenty-first century must see the development of similar processes based on the biorefinery. The aim is to design an integrated process capable of generating a cost-effective source of energy and chemical feedstocks using biomass as a raw material. The key is to find alternative sources of carbon to oil, available in high quantities and process them using green chemical technologies, ensuring products obtained are truly green as well as sustainable. Technologies used should ideally be flexible enough to accommodate the natural variation of biomass associated with seasonal or variety change (Clark et al, 2009). The efficiency of the process needs to be maximal: ideally every output has to have a use and a value/market. We can no longer afford the luxury of waste. Practices based on industrial symbiosis looking at re-using the waste produced by one process to feed another, or converting waste into a useful by-product with a marketable value need to be developed. The aim would be to achieve a zero waste biorefinery able to compete economically with existing systems used to produce energy and chemicals, an objective increasingly pushed by EU regulations (see Section 1.3.1).

1.2

Scheme describing an integrated biorefinery as a mixed feedstock source of chemicals, energy, fuels and materials.

Adding value to every output of the biorefinery can be achieved by combining several technologies together, using a sequential approach to extract chemicals before biomass is converted to energy. The main green extraction processes used to extract valuable compounds from biomass include liquid and supercritical CO2, ultrasonic or microwave-assisted extraction and accelerated extraction. Microwave-assisted extraction is a commercial reality with Crodarom using this technique to extract purer and more degradation stable plant materials (Crodarom, n. d.). The extraction can be followed by biochemical or thermochemical processes and internal recycling of energy and waste gases. This approach ideally constitutes the basis of an economically sound starting point for the design of a biorefinery and is illustrated in Fig. 1.3. The integration of technologies for the biorefinery takes into account the complex nature of lignocellulosic biomass, in order to produce several products and render the biorefinery concept cost-effective.

Biomass contains an array of functionalized molecules, with many of them having a market value. Compounds such as natural dyes or colorants (e. g., carotenoids), polyphenols, sterols, waxes, nonacosanol or flavonoids (e. g., hesperidin), amino acids, and fatty acid derivatives can be extracted selectively using clean extraction techniques prior to the treatment of biomass by biochemical and thermochemical processes. These compounds have uses in cosmetics, as nutraceutical or semiochemicals (Clark et al., 2006; Deswarte et al., 2006). Often, secondary metabolites are extracted using volatile organic solvents, but clean extraction techniques such as liquid and supercritical CO2 are very selective, allowing fractionation of extracted mixtures and have the advantage of being allowed for processing raw materials, foodstuffs, food components and food ingredients (together with ethanol and water) according to Directive 2009/32/EC of the European Parliament on extraction solvents used in the production of foodstuffs
and food ingredients. This technique also does not leave any residues (Budarin et al., 2011), allowing it to be used for pharmaceutical, food and cosmetic applications and compensating for both high technology capital cost and energy consumption. The polarity of CO2 can also be fine-tuned using co-solvents such as methanol or ethanol (Sahena et al., 2009). As a matter of comparison, the polarity of supercritical CO2 can be compared to that of hexane (Deye et al., 1990). Although energy requirements of supercritical CO2 are high, the technology has been commercially used for hop extraction, decaffeination of coffee and dry cleaning (Arshadi et al., 2012).

Biochemical and thermochemical processes complement each other well, the former being very selective but slow compared to the latter. Biochemical processes require low temperatures but pre-treatments are often required (e. g., ammonia fibre expansion or AFEX, dilute acid hydrolysis) to open up biomass’s fibre structure and yield fuels and chemical intermediates used for further downstream processing (Eggeman and Elander, 2005; Tao et al., 2011). Processing times and space-time yields are high compared to thermochemical processes, but they are less energy intensive (Kamm and Kamm, 2004). Thermochemical processes, which include gasification, pyrolysis and direct combustion (see Table 1.2), usually operate above 500°C and are much less selective, yielding oils, gas, chars and ash (Fernandez et al., 2011).

Biomass with a high acid, alkali metal and water content can be difficult to use in conventional thermal treatments: the high water content can render pyrolysis or gasification processes very difficult and the acidity of the feedstock can limit the applications of the pyrolysis oil obtained, for example.

Microwave technology has been studied for the pyrolysis of straw. This technology was proven to improve the quality of bio-oils obtained at lower temperatures (typically under 200°C), yielding oils with properties outperforming commercial fuel additives: bio-oils produced have a lower oxygen, alkali, acid and sulphur content (Budarin et al., 2009).

The properties of the oil obtained could also be modified by using additives during the heating phase, showing how microwave technology is versatile and can offer an alternative to conventional thermal processes. Microwave technology has an added advantage compared to conventional thermal heating: it activates cellulose at a temperature of 180°C, helping the conversion process (Budarin et al., 2010). It has been reported that at this precise point and under microwave heating conditions, the rate of decomposition of the amorphous part of cellulose increases due to in-situ pseudo acid catalysis, yielding a char and bio-oil of superior properties compared to those produced when using conventional heating methods.

The biochemical pathway in general, and fermentation to ethanol in particular, have been employed in the US and Brazil for several decades, and its economics have been thoroughly investigated. TEAs are available for the production of ethanol from sugarcane; the production of ethanol from corn (starch); and the production of ethanol from lignocellulose. More advanced pathways such as the production of ethanol from cyanobacteria and the production of hydrocarbons from sugar fermentation are also under investigation, although TEAs for these are not yet available.

The sugarcane pathway produces ethanol, electricity, and crystallized sucrose. Sugarcane is harvested and processed to separate the plant’s lignocellulose (bagasse) from the cane juice (garapa). The bagasse is combusted to provide process heat and electricity, with the latter generated in sufficient quantities to be sold onto the neighboring electricity grid. The garapa is further processed into molasses and sucrose crystals. The molasses, which are a mixture of sucrose and minerals, are sterilized and fermented with brewer’s yeast (Saccharomyces cerevisiae) to produce a beer containing 6-10 vol% ethanol. The beer is distilled to hydrous ethanol (containing 5 vol% water) via conventional distillation. Further dehydration can occur via employment of molecular sieves or other techniques to produce anhydrous ethanol (containing less than 0.3 vol% water).

The starch ethanol pathway most commonly employs corn (maize) as feedstock, although other starch crops such as wheat and cassava are also used. It is similar to the sugarcane ethanol pathway, although a saccharifica­tion step is required to depolymerize the starch into fermentable glucose monomers. The pathway employs either dry milling or wet milling, although TEAs of wet milling are very rare and it is not covered here as a result. In dry milling the corn kernels are ground, mixed with water, and cooked to gelatinize the starch content. Enzymes are added to depolymerize the starch first to oligosaccharides and then to the monosaccharide glucose (the process is also known as saccharification). The resulting fermentation broth also contains the lipid, fiber, and protein content of the kernel, which are removed and sold following fermentation as distillers’ dried grains and solubles (DDGS), a valuable livestock feed. Fermentation employs Saccharomyces cerevisiae and is followed by distillation and dehydration to anhydrous ethanol.

Table 2.4 reviews the capital costs for a Brazilian sugarcane ethanol biorefinery and a US corn ethanol dry mill biorefinery. Equipment compo­nents are not identical due to feedstock-specific differences between

the pathways: a sugarcane ethanol biorefinery requires equipment to process the garapa into molasses and sucrose crystals, whereas a corn ethanol biorefinery requires equipment to convert starch into dextrose via enzymatic hydrolysis. The corn ethanol facility is more expensive on an equal capacity basis as a result of the saccharification step and increased fermentation and distillation steps.

Table 2.5 reviews the operating costs for sugarcane to ethanol and starch ethanol dry milling biorefineries. While sugarcane ethanol biorefineries pay nearly twice as much in annualized costs for feedstock than do corn ethanol biorefineries of comparable ethanol output, they also derive significantly more revenue from by-product credits in the form of electricity and crystallized sucrose sales. These by-product credits cause the total annualized operating costs to be lower for a sugarcane ethanol biorefinery than a corn ethanol biorefinery despite the former’s higher feedstock costs, resulting in a lower biofuel production cost for sugarcane ethanol than corn ethanol.

Lignocellulosic biomass can also be converted into ethanol via fermenta­tion, although the recalcitrance of cellulose (a linear-chain polysaccharide) and the antimicrobial properties of lignin make it a significantly more challenging and expensive pathway than first generation ethanol pathways. The biomass is first milled to increase the surface area of the lignocellulosic material and increase hydrolysis efficiency. A pretreatment step is also commonly employed to maximize hydrolysis efficiency and dilute acid, steam explosion, and ammonia fiber explosion are considered to be the most feasible (Kazi et al., 2010). The choice of pretreatment step affects both biorefinery operating costs and ethanol yields.

Pretreatment is followed by hydrolysis. One of three hydrolysis steps is employed to convert cellulose and any hemicellulose remaining following pretreatment into fermentable monosaccharides: concentrated acid, dilute acid, or enzymatic. Concentrated acid hydrolysis is employed in multiple cellulosic ethanol biorefineries but has not been the subject of TEAs and therefore is not covered here. Dilute acid hydrolysis is faster than enzymatic hydrolysis but can generate lower yields of monosaccharides. Recent research has also called into question existing cost estimates of the enzymes employed by enzymatic hydrolysis (Klein-Marcuschamer et al., 2012), suggesting that dilute acid hydrolysis could incur lower operating costs of the two processes (Kazi et al., 2010).

Table 2.6 presents capital cost estimates from three different TEAs for lignocellulosic biorefineries employing a dilute acid pretreatment and enzymatic hydrolysis. There is some variation in the equipment costs used to calculate total project investment, although Humbird et al. (2011) and Kazi et al. (2010) are very close when adjusted for capacity. The estimate from Piccolo and Bezzo (2007) is comparatively low, although this can be attributed to low estimates for distillation and recovery equipment and exclusion of the feedstock handling area, demonstrating the importance of the assumptions used in a TEA. Total project investment from these studies is 200-300% higher than for first generation biorefineries due to the necessity of including expensive pretreatment and hydrolysis equipment.

Table 2.7 presents annual operating cost estimates from three different TEAs for lignocellulosic biorefineries employing a dilute acid pretreatment and enzymatic hydrolysis. Humbird et al. (2011) is different from the other two assessments in that it models on-site enzyme production for hydrolysis; both Kazi et al. (2010) and Piccolo and Bezzo (2009) model enzyme purchase

from external sources. On-site enzyme production generates higher capital costs (as evidenced by greater capital depreciation) and lower operation and management costs.

Researchers are combining TEA with life cycle analysis (LCA) to provide a comprehensive evaluation of biorefinery technologies (Hill et al, 2006). These techniques share a symbiotic relationship in that they enhance findings from each discipline. TEA can quantify the economic costs associated with environmental impacts, and LCA determines the environmental effects related to TEA assumptions.

LCA researchers estimate the environmental impacts associated with biorefinery operations. Sometimes, the environmental impacts can be readily quantified in economic terms — water treatment of process effluent, for example. It is much harder to determine the economic cost of other types of environmental impacts like those associated with global climate change. TEAs can help determine the proper incentives or penalties required to encourage or mitigate these environmental impacts.

System boundaries are an important consideration for both TEA and LCA. TEAs are typically confined to the boundaries of a specific process, but can extend to include global economic activity. LCA research on the other hand encourages the expansion of system boundaries to properly account for environmental impacts. Therefore there are important tradeoffs involved in combining both techniques. In general, data availability weighs heavily on the choice of system boundaries.

There is an increasing awareness of the environmental impacts of industrial activity. The long-term implications of commissioning biorefinery projects require careful study of both economic and environmental risks. Knowledge gained from future biorefinery projects will enhance our understanding of both risks if they are investigated in a concerted fashion.

1.2.1 Defining biorefineries and bio-processing

In addition to the definition of green chemistry given previously in Section 1.1.1, two additional definitions need to be highlighted in this chapter: the term ‘biorefinery’ and the term ‘bioprocessing’.

A biorefinery is an analogue to the current petro-refinery, in the sense it produces energy and chemicals. The major difference lies in the raw material it will use, ranging from biomass to waste. The use of clean technology is another imperative for the biorefinery, ensuring its output(s) are truly sustainable. The IEA Bioenergy Task 42 defines biorefining as ‘the sustainable processing of biomass into a spectrum of bio-based products (food, feed, chemicals and/or materials) and bioenergy (biofuels, power and/or heat)’ (IEA, 2009). Various biorefinery designs of varying size and output number will emerge commercially in the future (Cherubini, 2010), taking advantage of flexible technology, helping the concept of a biorefinery to process locally available biomass to its fullest in an integrated fuel — chemical-material-power cycle, improving cost-efficiency, the quality of life of the local population and lowering the environmental impact governed by the three dimensions of sustainability (environmental protection, social progress and economic development; see Fig. 1.4). Networks of biorefineries are to be considered too, for maximum resource efficiency.

Bio-refining should not be confused with bioprocessing. A bioprocess is any process which uses biological organisms (e. g., enzymes) to carry out targeted chemical or physical transformations. A bioprocess can be used as part of the conversion process in a biorefinery along with other low environmental impact technologies such as microwave chemistry or aqueous phase catalysis processing. This illustrates how important it is to interconnect different disciplines such as chemistry, chemical engineering, biotechnology and biology together with techno-economic and sustainability assessment, as they are crucial for the development of a successful fully integrated biorefinery. Biomass is a term applied which includes a great variety of different and often complex plant components embedded in a matrix that differs according to the origin of the biomass used. A multi-disciplinary approach is therefore necessary to maximize the value of the products obtained while using green chemistry technology.

Biorefineries can choose to generate electricity from biomass or byproducts into electricity. Although biomass electricity is typically more expensive than fossil fuel power, there are two scenarios where biomass power is an obvious choice: remote or stranded biomass supply, and production of excess byproducts.

There are large quantities of biomass in remote or stranded locations that are classified as wastes. A significant amount of this waste decomposes without yielding economic value. This loss occurs, in part, because waste biomass is difficult to gather reliably in sufficient quantities, and waste is a heterogeneous material which makes conversion difficult. Small-scale power generation is one way to capitalize on potentially low-cost feedstock.

Biomass power generation can be accomplished in several ways: biomass combustion can provide steam to drive a steam turbine; biomass gasification yields syngas that could be fed into a gas turbine; biomass pyrolysis or torrefaction yield intermediate materials that can be combusted or gasified to produce power. Representative costs for these three scenarios are given in Table 2.8.

The low capital costs ($600/kW) for biomass combustion to power are indicative of the technology’s simplicity (Dornburg and Faaij, 2001; Jenkins et al., 2011). However, biomass combustion is less efficient than the alter­natives even at large scale. Biomass gasification for power generation requires additional capital investment for the more expensive gas turbines and auxiliary equipment to ensure that gas conditions meet strict particulate matter requirements. The higher costs are compensated by higher efficiencies and ability to scale resulting in lower operating costs. The gasification scenario allows for the use of an integrated gas combined-cycle (IGCC) design with both steam and gas turbines to further improve the process

efficiency. Finally, the power industry has shown interest in pyrolysis and torrefaction products as a means to overcome some of the challenges faced by biomass, e. g. storage, heating value, feeding. Pyrolysis and torrefaction processes yield products that can be stored with less degradation and a lower footprint, that have a higher heating value, and are easier to feed into existing equipment than raw biomass. These benefits come at a cost. Capital costs are expected to be much higher than conventional alternatives ($2400/kW) with higher operating costs as well ($0.08/kWh) (Bridgwater et al, 2002).

A major challenge for TEA and LCA studies is risk and uncertainty quantification. Industry employs economic risk, measured by indicators such as rate of return and net present value, to identify investment opportunities. Policy makers rely on LCA to estimate greenhouse gas emissions and resource use. Varying degrees of uncertainty underlie these measures. Therefore, research requires additional tools to understand the implications of these uncertainties.

Researchers employ an increasing number of techniques such as case studies, sensitivity analysis, and Monte Carlo simulations to improve uncertainty quantification in TEA and LCA studies. The need for uncer­tainty quantification is driven by uncertainties in model parameters, their interactions, and the outputs generated by these analyses.

Case studies are the most trivial approach to quantifying uncertainty and are not always recognized as such. However, careful selection of system scenarios can provide more than enough data to understand project risks. For example, case studies based on the extreme values of historical market prices for a given commodity could be enough to rule out a potential project. The drawback of case studies is that they provide minimal insight into the interactions between different model parameters.

Sensitivity analyses improve upon case studies by evaluating several points within a range of parameter values. Their key insight is the extent to which system outputs change based on different input assumptions. Sensitivity analyses that involve a large number of randomized model evaluations are known as Monte Carlo simulations. Monte Carlo simulations benefit from inexpensive computational resources that allow rapid model evaluations. Researchers employ Monte Carlo extensively in a wide range of fields to develop model probability distributions. Increasing model complexity has limited the use of this brute-force method because it would consume significant computational time and resources. Researchers continue to adopt powerful techniques to model, collect, and assess TEA data that are beyond the scope of this chapter.

These uncertainty quantification techniques help reduce risks from assumption bias and failure to consider adverse scenarios. However, they are not a substitute for robust models with sensible built-in assumptions.