Biomedical Engineering Reference
In-Depth Information
change) were not estimated (Searchinger, 2008). Fargione (2008) determined that
the greenhouse gases (GHGs) released from changing natural habitats to biofuel
cropland were several-fold greater than the offset from displacing fossil fuels, and
hence a “carbon payback time” was defined to determine the time required before a
true reduction in GHG resulted. This example drives home the need for an integrated
assessment of environmental impacts.
9.3
ENVIRONMENTAL SUSTAINABILITY
OF MICROALGAL PROCESSES
9.3.1 o
verview
oF
e
nvironMental
a
ssessMent
oF
a
lGal
b
iodiesel
While prior studies on biofuels were largely limited to feedstocks of terrestrial plant
origin, over the past 4 years, a number of studies have been published on the envi-
ronmental analysis of algal energy processes (Lardon et al., 2009; Batan et al., 2010;
Clarens et al., 2010; Jorquera et al., 2010; Sander and Murthy, 2010; Stephenson
et al., 2010; Razon and Tan, 2011; Richardson et al., 2012a). Prior to this, environ-
mental analyses of algal energy processes were limited to the work of Kadam (2002)
and Sazdanoff (2006). The former considered the benefit of co-combustion of coal
and algae in electricity generation, while the latter presented a model for the algal-to-
biodiesel fuel cycle, including climatic data to simulate production at four locations
in the United States. In most studies, analyses of the energy and global warming
potential (GWP) have formed the key assessment criteria, with net energy recovery
(NER) and LCA being the most frequently used approaches. In all cases, the absence
of commercial-scale inventory data implies that scale-up estimates from laboratory-
and pilot-scale data inform these analyses, requiring that a range of assumptions
must be made on large-scale performance within these systems. In Table 9.3,
the systems analyzed in each of the studies reported are summarized. These can be
positioned within the context of Figure 9.1, which provides a block flowsheet of the
integrated process for the production of biodiesel from microalgae and demonstrates
the manner in which different studies focus on different components of the process.
The findings in these studies are discussed in the following sections.
It must be emphasized that the immature nature of the microalgal biofuels
process implies that the data have mostly been sourced from laboratory studies,
modeling, and some pilot-scale research. Variability in data is a source of variation
in results presented in the literature. As an example, Collet et al. (2010) estimated
the energy for paddlewheels and pump of water at 0.2 and 0.153 kW-h per kilogram
algae, respectively, whereas Clarens et al. (2010) suggested values of 0.035 and
0.029 kW-h per kilogram algae, respectively. Substitution with the lower values
assumed by Clarens et al. (2010) leads to a 44% reduction in total energy demand.
Further, lipid productivity has been recognized as a key factor in selecting condi-
tions for biofuel production (Griffiths and Harrison, 2009; Rodolfi et al., 2009). Lipid
productivity is the product of lipid content and specific growth rate. It is recognized
that nutrient limitation results in compromised growth rates and high lipid con-
tent; hence, care must be exercised to utilize compatible data when estimating lipid
productivity. Examples are found in the literature where the high specific growth
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