Access to this full-text is provided by Springer Nature.
Content available from Clean Technologies and Environmental Policy
This content is subject to copyright. Terms and conditions apply.
Vol.:(0123456789)
1 3
Clean Technologies and Environmental Policy (2021) 23:1475–1492
https://doi.org/10.1007/s10098-021-02042-x
ORIGINAL PAPER
Techno‑economic assessment ofmicroalgae cultivation inatubular
photobioreactor forfood inahumid continental climate
S.Schade1 · T.Meier1
Received: 19 May 2020 / Accepted: 1 February 2021 / Published online: 23 February 2021
© The Author(s) 2021, corrected publication
Abstract
Fish as the primary source for the essential n−3 fatty acids eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA)
cannot cover the global demand for these important nutrients resulting in a supply gap of currently 1.1 million tons of
EPA + DHA annually. A further exploitation of natural fish stocks is linked to great damage to ecosystems. Oleaginous
microalgae are a natural source for EPA and DHA and could possibly contribute to closing this gap. The cultivation in
photobioreactors (PBR) in a ‘cold-weather’ climate showed that microalgae compare favorably to aquaculture fish. The pre-
sent study assesses the economic potential of microalgae for food in such system model. Techno-economic assessment was
conducted on the basis of a dynamic system model for the cultivation of Nannochloropsis sp. in industrial scale in Central
Germany over a time span of 30years. The net present value (NPV) and return-on-investment (ROI) were obtained for a
number of scenarios in which technic and economic parameters were altered. Taking the size of the PBR considered into
account, the cultivation of Nannochloropsis sp. yielded a positive NPV of EUR 4.5million after 30years which translates
to an annualized ROI of 1.87%. The sensitivity analysis overall resulted in annualized ROIs between 1.12 and 2.47%. Major
expenditures comprised the PBR infrastructure, maintenance and labor cost. An extended cultivation season by four weeks
was responsible for an NPV surplus of almost one third (32%). An increase in the selling price by 15% was responsible for
a 47% higher NPV. In comparison with Atlantic salmon (Salmo salar) raised in aquaculture, EPA from Nannochloropsis sp.
resulted in about halved cultivation costs (−44 to −60%). In this study we could show that microalgae from photoautotrophic
cultivation not only have the potential to supply humans with essential nutrients, but they are also a lucrative investment,
even in a ‘cold-weather’ climate where cultivation cannot take place year round.
* S. Schade
susann.schade@landw.uni-halle.de
1 Institute forAgricultural andNutritional Sciences,
Martin Luther University Halle-Wittenberg,
Von-Danckelmann-Platz 2, 06120Halle(Saale), Germany
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1476 S.Schade, T.Meier
1 3
Graphic abstract
Keywords Techno-economic assessment· Cost analysis· Nannochloropsis sp.· Microalgae· Phaeodactylum tricornutum
Introduction
Fish is by far the biggest source of n−3 long-chain poly-
unsaturated fatty acids (PUFA), more specifically the fatty
acids eicosapentaenoic acid (EPA) and docosahexaenoic
acid (DHA) (de Oliveira Finco etal. 2016). The consump-
tion of n−3 long-chain PUFAs has been linked to cardio-
vascular benefits, a support of the nervous system and a
regulatory effect on inflammatory conditions (Adarme-
Vega etal. 2014; García etal. 2017). These nutrients are
otherwise only found in human milk, cultivated marine
algae, marine mammals and krill [EFSA Panel on Dietetic
Products Nutrition and Allergies (NDA) 2012] and can-
not be converted by the human body in sufficient amounts
(Galán etal. 2019). The question is not if alternative
sources can substitute fish as the primary source for EPA
and DHA, but which sources could possibly complement
fish already today. Exploitation of wild-caught fish can-
not be expanded without depleting natural resources and
ecosystems (Adarme-Vega etal. 2014; de Oliveira Finco
etal. 2016). Aquaculture fish on the other side has to rely
on terrestrial fodder plants and thus competes with other
food groups for arable land. Besides, the diet of aqua-
culture fish is enriched with wild-caught fish and fish oil
to enhance its PUFA content (Adarme-Vega etal. 2014)
leading to over 70% of the available fish oil being used
for fish feed production (Tocher 2015; Galán etal. 2019).
As a result, it has been found that fish currently only con-
tributes around 15% of the global demand for EPA and
DHA, and krill as an alternative source adds 0.3% (Salem
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1477
Techno-economic assessment ofmicroalgae cultivation inatubular photobioreactor forfood…
1 3
and Eggersdorfer 2015). The annual global gap of EPA
and DHA supply thus presently reaches 1.1 million tons
(Schade etal. 2020), and the world population keeps on
growing.
Microalgae have been studied in numerous application
contexts as a source of, e.g., biofuels, fertilizers, synthetic
materials, food and nutraceuticals. The exploration of alter-
native fuels has long driven microalgae research. However,
it has been shown that from an economic and technical point
of view, microalgae cannot compete with fossil fuels and
are most profitable for food and feed (Barsanti and Gualtieri
2018; Walsh etal. 2018). As such, microalgae are considered
the most abundant primary producers of high-value nutrients
like n−3 long-chain PUFAs and carotenoids (Adarme-Vega
etal. 2014). Moreover, microalgae contain additional valu-
able compounds for human nutrition, such as dietary fiber
which is assumed to have a positive influence on cardio-
vascular diseases and an anticancer effect (Mišurcová etal.
2012). Further health-promoting compounds in microalgae
comprise carotenoids, phycobiliproteins and sterols (García
etal. 2017). Nannochloropsis sp. is an oleaginous microalga
with an average EPA content of 4.2% under light-saturated,
non-stressed conditions (Schade and Meier 2020). It favors
temperatures between 21 and 25°C (Chini Zittelli etal.
1999; Ma etal. 2016).The microalga has moreover been
authorized by the US Food and Drug Administration and
the European Novel Food Regulation for the application as
a food (Qiu etal. 2019). Nannochloropsis sp. was selected
for its suitable nutritional profile with a comparatively high
lipid and protein levels. Likewise, Phaeodactylum tricor-
nutum shows high lipid and protein contents and contains
both EPA and DHA.
The photoautotrophic production of microalgae avoids
using limited resources for the production of nutrients. More
specifically, not only the pressure on the global fish stock is
released, but compared to heterotrophic production of micro-
algae, no arable land must be used for the cultivation of feed
for algae (Keller etal. 2017). Land as a scarce, non-renewable
resource is a key indicator for the environmental viability of
the global food system (Meier 2017; Rockström etal. 2020).
Naturally grown land is considered as the basis of biodiversity
and as such regulates the climate, adjusts the air and water
quality and provides food and room for recreation (Frischkne-
cht and Büsser Knöpfel 2013). Photobioreactors (PBR)
reportedly perform better than open raceway ponds (ORP)
concerning the production cost and productivity (Barsanti and
Gualtieri 2018). Moreover, they are possibly more suitable for
a humid continental climate than ORP because they exploit
the forthcoming solar insolation optimally without any dark
zones as they can exist in an ORP (Schade and Meier 2019).
At the same time, they might be more efficiently operated in a
humid continental climate to prevent exorbitant cooling costs
(Schade and Meier 2019). Additionally, the strongest argument
for the usage of a PBR over ORP is the much lower risk of
contamination with bacteria, protozoa or other microalgae
(García etal. 2017), which is crucial for the production of
edibles. Compared to fish as the main source for PUFAs, the
environmental impacts of microalgae cultivation are similar to
those from wild-caught fish, and often smaller than those from
aquaculture fish depending on the fish species and the exact
production system or catching method (Schade etal. 2020).
PBRs have hardly been assessed with regard to cost analy-
sis, economic risks or benefits (Zhu etal. 2018). In particular,
tubular PBR cost analyses are scarce, and such investigations
with the application of microalgae for food have barely been
conducted. If microalgae are to be used as an alternative source
for EPA and DHA globally, it is necessary to evaluate their cul-
tivation under different climatic conditions. As such, to the best
of our knowledge, cultivation cost of microalgae production
has not been assessed yet in a humid continental climate. The
humid continental climate has first been defined by Wladimir
Köppen in 1900 in his quantitative classification of the earth’s
climate zones, which is still widely applied today (Belda etal.
2014). The humid continental climate in Central Germany is
hence characterized by seasonal changes. Temperatures in the
coldest month must be below −3°C and mean temperatures
must equal or exceed 10°C in at least 4months of the year
(Peel etal. 2007). The chosen region is moreover described by
an absence of a dry season, and has a warm summer (Peel etal.
2007). The cold climate, which is the superordinate category
of the humid continental climate, is the dominant climate zone
in Europe (44.4%), North America (54.4%) and Asia (43.8%)
(Peel etal. 2007), which shows that it is rather important to
explore such locations for the production of microalgae.
In this study, a techno-economic assessment was con-
ducted to evaluate costs and benefits occurring during the
production of microalgae in a tubular photobioreactor in a
‘cold-weather’ climate. The cultivation system was based on
a previous study of the authors (Schade and Meier 2020) and
followed a ‘top–down’ approach to assess major processes
in the cultivation model. From the established net cash flow
table, the net present value (NPV) (discounted cash flow)
was calculated, along with the return-on-investment (ROI).
Upon the alteration of different technical and economic
parameters, a sensitivity analysis was conducted to analyze
critical factors in industrial scale microalgae production for
food.
Materials andmethods
General approach
The techno-economic assessment was performed in accord-
ance with Zimmermann etal. (2020) in order to construct
the methodology section as transparent and coherent as
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1478 S.Schade, T.Meier
1 3
possible. This TEA guideline allows TEA to be conducted
in parallel to life cycle assessment (LCA) which emerges
in aligned vocabulary and assessment steps, and it applies
many concepts from ISO 14,044 (ISO Organisation 2006).
TEA should cover the process steps illustrated in Fig.1.
Similar to LCA, the construction of TEA is an iterative
process.
Goal and scope clarified the context of the study and the
reasons for carrying it out. Moreover, the methodological
framework was assessed in this section (e.g., functional unit,
system boundaries). The inventory was covered by the estab-
lishment of the microalgae cultivation model used in this
study. The cost assessment examined investment cost and
operating cost as well as the NPV and the ROI as significant
indicators for the economic potential of the model. Price
forecasts for the most important input materials during the
whole lifetime of the facility were conducted. A following
scenario analysis considered the variation of both relevant
economic and technical system parameters.
Goal, scope andtarget product
The overall goal of this study was to evaluate the economic
potential of microalgae biomass production for food in a
tubular photobioreactor in a humid continental climate. For
that matter, the costs for generic industrial scale microalgae
cultivation were assessed in a techno-economic assessment
(TEA) applying the NPV and the ROI. Furthermore, major
economic and technical hurdles were identified in a sce-
nario analysis where relevant alterations of system param-
eters were tested. System-boundaries comprised all relevant
processes and input materials of the cultivation stage up to
the dry biomass (target product) (Fig.2). One scenario con-
sidered EPA-rich microalgae oil and residual protein-rich
biomass as the final products. This scenario thus moreover
comprised an oil extraction stage. The evaluation of the eco-
nomic potential was conducted in EUR. The inputs in Fig.2
refer to the baseline scenario and the scenario considering
microalgae oil as the target product.
Microalgae cultivation model
Input flows of microalgae cultivation were based on the
design model of a previous study of the authors which evalu-
ated the environmental impacts of cultivation processes in
a hypothetical 628 m3 tubular photobioreactor located in
Halle/Saale, Central Germany (Schade and Meier 2020). Dry
biomass for human nutrition was produced in borosilicate
glass tubes with a diameter of 40mm (baseline scenario)
and a wall thickness of 2mm. Borosilicate glass as the mate-
rial for the tubes was contemplated as superior over other
possible tube materials, such as polymethyl methacrylate or
silicone as it has a long lifespan of at least 50years (Schultz
and Wintersteller 2016). Moreover, it has an excellent trans-
lucence, which does not degrade under solar radiation to
enable the photoautotrophic process (Schultz and Winter-
steller 2016), and it is a safe material to use for the produc-
tion of edibles. Further construction materials (aluminum,
steel, synthetic rubber) were designed in accordance with the
study by Pérez-López etal. (2017). Cultivation took place
from mid-April until mid-October, because an evaluation
of the climatic characteristics of the location suggested this
period to be most efficient for the cultivation of microalgae,
taking into account maximum, minimum and mean daily
temperatures as well as solar insolation.
The calculation of the yields and productivity was based
on the climatic conditions of the site, which were drawn
from detailed satellite data provided by the NASA Power
Data Access Viewer (NASA—National Aeronautics and
Space Administration 2019). The location shows tempera-
tures slightly below −3°C in the coldest months of the
year (January and February) and average temperatures
between 14.5 and 20.3°C from May until September. April
and October portray mean temperatures of slightly below
10°C which is why it was assumed that on average, half
of these months, cultivation was feasible. Solar insolation
is highest from May until August with 16.1–18.2MJ/m2/
day and still reaches 14.1MJ/m2/day in April and 10.8MJ/
m2/day in September. The month with the lowest radiation
during the cultivation season is October with 5.6MJ/m2/
Fig. 1 Process steps in TEA according to Zimmermann etal. (2020)
and reference to study chapters
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1479
Techno-economic assessment ofmicroalgae cultivation inatubular photobioreactor forfood…
1 3
day on average. Table1 lists the essential system parameters
and input materials of all scenarios, which were based on a
technical variation in the system (for scenario overview see
chapter2.6 Sensitivity analysis). All other scenarios that are
not portrayed in Table1 relied on a variation of economic
parameters and thus were equal to the baseline scenario. The
plant was modeled with a lifespan of 30years. The system
model relied on a combination of sources. Resource con-
sumptions of major processes during the cultivation were
based on own calculations and literature data. Additionally,
some input parameters relied on expert advice. More spe-
cifically, the nutritional values of the microalgae species
considered in this study are mean values compiled from the
literature. Likewise, the photoconversion efficiency (PCE) is
a mean value, which was obtained from several studies that
had all applied a similar cultivation system and taken into
account all relevant restraining parameters. Those param-
eters comprised light saturation and photoinhibition, practi-
cal losses through reflection, inactive absorption, respiration,
and high oxygen rates (Lundquist etal. 2010; Posten 2012;
Skarka 2012; Benemann 2013; De Vree etal. 2015).
The nutrient demand was calculated according to the
protein content of the microalgae species under implemen-
tation of the nutrient-to-protein conversion factor, which
was adopted from the literature (Templeton and Laurens
2015). The carbon dioxide demand was in accordance with
the literature, too (Chisti 2007; Patil etal. 2008; Lardon
etal. 2009; Ma etal. 2016). Usage of hydrogen peroxide and
hypochlorite was estimated after Pérez-López etal. (2017).
The electricity consumption and demand for natural gas
resulted from different processes in the cultivation model,
which were all based on own calculations. These processes
comprised water pumping, aeration with CO2, mixing of
the suspension in the tubes, centrifugation and drying of the
algae biomass (Schade and Meier 2020). Total uncertainty of
the electrical processes is 1.07, and total uncertainty of the
remaining foreground processes (photobioreactor materials,
nutrients, water use, land use) is 1.13. The complete system
model can be accessed in the previous study of the authors
(Schade and Meier 2020).
Cost assessment
For the compilation of the cost assessment, a net cash flow
table for the construction phase and the first 30years of
operation was established in order to calculate the NPV (dis-
counted cash flow) and the ROI from it. The net cash flow is
obtained by subtracting all cost paid from the received ben-
efits while including cost for a capital loan, such as capital
payback and interest rate (Lauer 2008). The first two years
were supposed to be the construction phase of the plant
without incoming benefits. Investment costs were evenly
spread over these two years. For the following first 6years
of cultivation, a reduced production was modeled to account
for the starting phase of the plant with the benefits doubling
in the second year of production and a growth of 6% for each
of the subsequent five years until full production is achieved.
A bank loan covered the investment cost with an interest rate
Fig. 2 System-boundaries and input data of microalgae cultivation (baseline scenario and microalgae oil scenario)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1480 S.Schade, T.Meier
1 3
of 2.05% (average interest rate of the months 01/2019 to
09/2019 for Germany) (Trading Economics 2019). The net
present value of a given time period NPVn is calculated from
Eq.1 (Short etal. 1995) where NCF is the net cash flow of
a time period n, and d is the nominal discount rate, which
also covers inflation and is set here to 12% (Thomassen etal.
2016; Walsh etal. 2018).
For better comparability between the assessed scenar-
ios, the ROI is calculated in addition to the NPV. The ROI
indicates the percentage of a net value that is received
from an investment over a given period of time and was
calculated from the NPV of a given period of time n
(Eq.2) (Chen 2020a).
(1)
NPV
n=
NCF
(
1
+
d
)n
The ROI is estimated for every year of plant operation,
starting from the third year when facility operation begins.
For every time period n, the sum of the net present values
from year three to year n is calculated. The total costs Ctot
represent the sum of payments assigned in the first and sec-
ond year when all investment costs were paid. Since the
investment project covers a timeframe of 30years and the
ROI does not consider length of time, the calculation of the
annualized ROIA gives a more representative result. The ROI
can be annualized following Eq.3 (Chen 2020b) where n is
the number of years for the investment.
(2)
ROI
=
∑n
3NPVn−Ctot
C
tot
(3)
ROI
A=
[
(1+ROI)
1
n−1
]
×100
%
Table 1 Parameters and input materials in microalgae cultivation scenarios (the origin of the values presented are described in the text)
Parameter Unit Baseline scenario Long cultivation
period
Short cultivation
period Phaeodactylum
tricornutum Microalgae oil
Microalgae species Nannochloropsis sp. Nannochloropsis sp. Nannochloropsis sp. Phaeodactylum
tricornutum Nannochloropsis sp.
Calorific value MJ/kg DM 17.66 17.66 17.66 17.25 17.66
Lipids g/kg DM 206 206 206 180 206
EPA g/kg DM 42.0 42.0 42.0 31.1 42.0
DHA g/kg DM – – – 1.4 –
Protein g/kg DM 300 300 300 364 300
Cultivation time mm/dd 04/15–10/15 04/01–10/31 05/01–09/30 04/15–10/15 04/15–10/15
Cultivation days day 183 214 153 183 183
Total solar insola-
tion
MJ/m22779 3077 2481 2779 2779
Productivity g/L/day 0.56 0.53 0.60 0.57 0.56
Yield t/ha/a 53.5 59.2 47.8 54.8 53.5
Absolut yield t/1.2ha/a 64.20 71.04 57.36 65.76 64.20
PBR volume m3628 628 628 628 628
Tube diameter mm 40 40 40 40 40
Lifespan a 30 30 30 30 30
PCE % 3.4 3.4 3.4 3.4 3.4
Land ha 1.2 1.2 1.2 1.2 1.2
Steel kg 7062 7062 7062 7062 7062
Aluminum kg 63,558 63,558 63,558 63,558 63,558
Synthetic rubber kg 20,544 20,544 20,544 20,544 20,544
Glass tubes, U-glass
turns
kg 341,223 341,223 341,223 341,223 341,223
Hypochlorite kg/a 3.01 3.01 3.01 3.01 3.01
Hydrogen peroxide kg/a 45,216 45,216 45,216 45,216 45,216
Nkg/a 3852 4262.40 3441.60 4793.91 3852
Pkg/a 193.00 213.12 172.08 197.01 193.00
CO2 kg/a 115,560 127,872 103,248 118,368 270,295
Electricity MJ/a 463,745 522,367 406,322 419,099 1,238,742
Natural gas MJ/a 220,848 244,378 197,318 201,859 220,848
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1481
Techno-economic assessment ofmicroalgae cultivation inatubular photobioreactor forfood…
1 3
Although the facility was modeled as an ‘nth’ plant which
presupposes a mature state of facility design and technique,
there can still be contingencies that have not been accounted
for in the model. In order to avoid an underestimation of
the costs in the TEA and get a too optimistic result, a con-
tingency factor of 1.25 was put on the overall costs (Lauer
2008).
Price forecasts forinput materials
Prices for input materials that are constantly required in a
great amount over the lifetime of the facility were analyzed
in a forecast. Since the facility is supposed to run with a
30-year-lifespan, analyses of future prices intended to por-
tray—as accurately as possible—representative prices at a
given point in time. Historical prices were obtained for a
15-year period from 2004 to 2019, except electricity, for
which the annual price was observed from 2000 to 2018.
Prices were recorded for the materials ammonium, phos-
phate fertilizer and natural gas (World Bank 2019), as well
as electricity (Eurostat 2019). The prices for carbon dioxide
were investigated using the commodity price index (U.S.
Bureau of Labor Statistics 2019) and a reference year price
from 2016 (Thomassen etal. 2016). Concerning electric-
ity, location-specific prices for Germany were applied. For
natural gas, European prices were utilized. When prices
were given in US-Dollar, they were converted based on
the respective monthly currency rate [UKForex Limited
(OFX) 2019]. It was relied on the Geometric Brownian
Motion (Eq.4) in order to estimate future prices of required
resources (Hilpisch 2014).
ΔS(t) is the price change of a respective resource over a
given time period which changes as a function of the drift
(
𝜇
), the volatility (
𝜎
) and a Wiener process of random values
(ε). The time period t comprises one month. The calculation
is based on a starting price St−1. Whereas the drift indicates
the deterministic trend of the process, the volatility controls
the influence that coincidence has on the process S(t). The
drift and volatility are both constants and are estimated from
the mean and standard deviation of historical returns (
𝜇t
),
which in return are drawn from the price (st) of each time
period (Eq.5).
The simulation with the Geometric Brownian motion was
repeated 1000 times for each commodity and future prices
analyzed according to their mean, median and 90% confi-
dence interval. A 90% confidence interval was considered
appropriate, given that these results are meant to be used
(4)
Δ
S(t)=S
t−1
𝜇Δt+S
t−1
𝜎𝜀
√
Δ
t
(5)
𝜇
t=
s
t
−s
t−1
s
t−1
×
100
in the sensitivity analysis. Thus, they should not be char-
acterized by extreme values at the lower and upper end. A
timespan of 30years in the future was portrayed by means
of monthly prices (Fig.3).
Sensitivity analysis
The sensitivity analysis was based on both changes in tech-
nic and economic parameters in order to analyze the vol-
atility of the system model. Besides the earlier described
baseline scenario, the following alterations were made to
the model.
Min/max commodity prices
In the baseline scenario, the mean predicted commod-
ity prices calculated in the forecast were used for the cost
assessment. However, forecasts, especially over a long time
period, are rather volatile and subject to a great amount of
unpredictable parameters. In order to evaluate the influence
of possibly higher or lower developments of the commod-
ity prices, the upper and lower ends of the 90% confidence
intervals were included in the assessment.
Long/short production periods
The selected location in Central Germany as part of the
humid continental climate zone is subject to variations in
seasonal temperatures. As a result, cultivation season lengths
can fluctuate drastically with a focus on the months April
and October. Both can be rather warm and thus suitable
for microalgae cultivation, or cold on too many days of the
month with minimum temperatures dropping below 0°C.
The system model was thus evaluated applying constantly
longer and shorter cultivation periods. As can be seen in the
inventory table (Table1), the length of the production period
influenced the yield tremendously. Even though productivity
decreases in spring and fall due to a lower solar insolation, a
longer cultivation season is favorable and expands the yield.
This climatic dependence was considered a key element of
the cultivation in a ‘cold-weather’ climate and was hence
analyzed in the cost assessment, too.
Tube price
The borosilicate glass tubes used in the photobioreactor are
one of the main materials needed for microalgae cultivation
and constitute one of the biggest investments concerning the
whole facility. Consequently, it was tested how a fluctuation
in the cost of a rather big share of the investment affects the
whole cost assessment of the plant.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1482 S.Schade, T.Meier
1 3
Variations inselling price
The agreement on a specific reasonable selling price is a
crucial step in the cost assessment as it here generates the
only source for benefits. The selling price used in this study
was determined according to values found in the literature.
Yet, a sample analysis on microalgae biomass products on
the German market was conducted which revealed great
fluctuations in the selling price. Price variations within the
same product were due to package size where a bigger size
generated a discount. Among the different products, prices
differed at times tremendously without evident reason. At
the same time, it was not obvious where and how the bio-
mass had been produced. However, even with the economy
Fig. 3 Historical and predicted
future prices of commodities
used during microalgae cultiva-
tion. Colors indicate a random
selection of scenarios from the
calculation of the Geometric
Brownian Motion
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1483
Techno-economic assessment ofmicroalgae cultivation inatubular photobioreactor forfood…
1 3
of buying the biggest package, industry net selling prices
still outnumbered the selling price suggested in the literature
to a great extent with the literature price of EUR 38 per kg
dry mass hardly being in the low range of the 90% confi-
dence interval (Fig. S1). However, the mean value of the
market analysis resulted in a net selling price (EUR 109 per
kg)—equaling 187% of the literature price. The results of the
market analysis can be found in the supplementary material
(Tab. S1, Fig. S1). Consequently, these possible variations
were accounted for in the sensitivity analysis by altering the
selling price of the biomass by 5% and 15%.
Alternative microalgae species
In a previous study of the authors, it has been shown how the
choice of microalgae species can alternate the environmen-
tal impacts, depending on the target product (Schade etal.
2020). For that matter, the cost assessment was repeated
using P. tricornutum as the cultivated species in the PBR.
Phaeodactylum tricornutum also is an oleaginous microalga
with a nutritional profile that is similar to Nannochloropsis
sp. However, in comparison with the latter, P. tricornutum
possesses a higher percentage of protein (36.4%) (Rebol-
loso-Fuentes etal. 2007) and slightly less EPA + DHA
(3.25%) (Zhukova and Aizdaicher 1995; Ryckebosch etal.
2014). The cultivation of another microalga with a compara-
ble nutritional profile not only illuminates possible changes
in the outcome due to taxonomical reasons. Because of the
similar profile, information can be obtained about how sen-
sitively the system reacts if the composition of one and the
same alga fluctuate like it naturally does.
Microalgae oil
In another scenario, it was tested how the choice of a differ-
ent goal product affects the NPV. In the baseline scenario,
the whole biomass was considered as the target product.
However, whole microalgae biomass only procures a fraction
of the selling price compared to more refined products such
as extracted lipids or antioxidants. Thus, the production of
microalgal oil from Nannochloropsis sp. as a source for EPA
was modeled with the residual protein-rich biomass being
sold as an alternative to soybean meal. It was assumed that
the whole lipid content of the biomass was extracted using
supercritical CO2. In order to extract 1kg of microalgae
oil, 11.7kg CO2 (Wang etal. 2016) and 58.6MJ electricity
(Shimako etal. 2016) were needed. Based on the inventory
data for microalgae cultivation used in this study, 1kg DM
contains 206g lipids and 42g EPA. The yearly microalgae
oil yield would accordingly add up to 13,225.2kg lipids
with an EPA content of 20.39%. Microalgae omega-3 oil
has been reported to realize a net market price between
USD 80 and 160 (Borowitzka 2013; Matos 2017; Barkia
etal. 2019) which roughly corresponds to EUR 72–144.
Yet, a sample market analysis of microalgae oil products for
omega-3 PUFAs for the German market resulted in much
higher net prices between EUR 240 and 900 (mean: EUR
452) per kg microalgae oil with an EPA + DHA share of
40–50%. Remarkably, the prices for microalgae oil were
not correlated with the actual concentration of EPA and
DHA in the product which provoked a massive fluctuation
of the EPA + DHA net price across the products with around
EUR 530–2300 (mean: EUR 1025) per kg EPA + DHA. In
order to obtain a reasonable selling price, the mean value
of the EPA + DHA kilogram prices was multiplied with the
percentage of EPA contained in the microalgae oil. Hence,
a net selling price of EUR 209 per kg microalgae oil was
estimated in this study. For each kilogram microalgae oil,
3.85kg protein-rich residual biomass was generated. The
unit price for the residual protein-rich biomass was assumed
to amount to 0.44 EUR/kg DM (van der Voort etal. 2017).
The results of the market analysis on microalgae oil prod-
ucts can be accessed in the supplementary material (Tab.
S2, Fig. S2). In order to conduct a profound analysis, the
cost assessment for microalgae oil as the target product was
repeated using the highest price suggested in the literature
(144 EUR/kg oil).
Results
Investment cost andoperating cost
The proportionate distribution of investment costs is shown
in Fig.4 with all components contributing more than EUR
100,000 being portrayed. In three scenarios, investment
costs differed from the baseline scenario. All other scenarios
equaled the baseline scenario regarding investment costs and
were thus not portrayed here. The cost for the glass tube sys-
tem was the biggest share and made up between 24 and 31%.
Only when the glass tube price was diminished by 20%, the
system was exceeded in its costs by the price for the drying
system (21–24% of all investment costs). The third biggest
cost component was the construction of all buildings needed
for the cultivation system (18–21% of all investment costs.).
All other investments contributed only 7% or less to the total
investment sum.
The proportional distribution of the operating costs
(Fig.5) varied for every scenario (excluding selling price
change scenarios) and was clearly dominated by labor cost,
which contributed between 39 and 42% to the overall oper-
ating costs. This position was mostly followed by costs for
the maintenance of the mechanical and electrical equipment.
However, when commodity prices develop at the maximum
end, costs for carbon dioxide exceeded these maintenance
costs. The scenario using microalgae oil as the target product
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1484 S.Schade, T.Meier
1 3
was also characterized by relatively high carbon dioxide
costs due to the lipid extraction with supercritical carbon
dioxide. Administration usually made up approximately
10% or less of the operating costs. The smallest share was
constituted by the overall fuel costs and maintenance of the
photobioreactor (cleaning). Even in the scenario with maxi-
mum commodity prices, fuel costs only contributed a minor
share of the operating costs. Electricity processes, which are
usually the main drivers in environmental assessments of
microalgae cultivation, only had an insignificant share of the
operating costs. They ranged from almost 2% in the mini-
mum commodity prices scenario to 5.81% in the microalgae
oil scenario, which had an expressively higher energy use.
The total investment and operating cost per kilogram dry
microalgae biomass are portrayed in Table2, assuming a
lifespan of 30years. Around 80% of the costs in all sce-
narios was produced by infrastructure, labor cost and main-
tenance cost. However, with commodity prices reaching
their maximums, and in the microalgae oil scenario also fuel
costs became significant. Overall, investment and operating
costs summed up to EUR 8.63–11.00 per kilogram of dry
biomass. The most favorable scenario in terms of production
cost was the one with extended cultivation seasons, which
was even more desirable than commodity prices dropping
to a minimum. The highest production costs were generated
when microalgae oil was the target product. However, also
short cultivation seasons were highly critical and generated
production costs almost as high as when microalgae oil was
produced.
Net present value
The NPV puts the costs in ratio to the benefits, while it also
considers a diminution of the cash flow over the years, thus
taking into account the time value of money. Additionally,
the contingency factor and interests are included in the cal-
culation of the NPV. The baseline scenario results in a posi-
tive NPV of EUR 4.5 million after 30years, which indicates
that the investment in the microalgae photobioreactor in this
region is profitable. The effects of the variation of certain
technical and economic parameters are shown in Fig.6.
Despite the partially extensive changes in the NPV, the
Fig. 4 Percental component
contribution to investment costs
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1485
Techno-economic assessment ofmicroalgae cultivation inatubular photobioreactor forfood…
1 3
results remained always positive indicating an overall prof-
itable investment. The least change in the NPV was given
with the variance of commodity prices which only lessened
the NPV by 3.9% when prices develop at the maximum end,
and effected a rise of the NPV by 2.9% when commodity
prices were set to a minimum. The cultivation of another
microalgae species, namely P. tricornutum, caused a positive
shift with a 7.3% higher NPV. Moreover, the change in the
kilogram costs for the production of P. tricornutum (Table2)
differed only by 2.4%. The change of the glass tube price by
Fig. 5 Percental component
contribution to operating costs
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1486 S.Schade, T.Meier
1 3
Fig. 5 (continued)
Table 2 Total investment and operating cost in EUR per kg dry microalgae biomass (excluding interests and contingency factor)
Baseline
scenario
(€)
Minimum
commodity
prices (€)
Maximum
commodity
prices (€)
Short period
(€)
Long period
(€)
Tube price
−20% (€)
Tube
price + 20%
(€)
Phaeod-acty-
lum tricor-
nutum (€)
Micro-
algae oil
(€)
Investment cost
Initial invest-
ment
0.36 0.36 0.36 0.41 0.33 0.33 0.39 0.36 0.38
Infrastructure 1.90 1.90 1.90 1.96 1.58 1.77 2.02 1.85 2.00
Operating cost
Fuel cost 0.82 0.41 1.44 0.81 0.82 0.82 0.82 0.78 1.93
Labor cost 2.95 2.95 2.95 3.30 2.67 2.95 2.95 2.88 2.95
Maintenance
cost
2.88 2.88 2.38 3.22 2.60 2.70 3.05 2.81 2.99
Administra-
tion and
insurance
0.70 0.70 0.70 0.78 0.63 0.64 0.76 0.68 0.74
Total 9.60 9.20 9.73 10.48 8.63 9.22 9.99 9.37 11.00
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1487
Techno-economic assessment ofmicroalgae cultivation inatubular photobioreactor forfood…
1 3
20%, which was considered tremendous, only affected the
NPV to an acceptable extent, causing a shift of around 12%.
The selling price, on the other side, had a great effect on the
NPV with a 5% shift resulting in an NPV change of around
15.5% and a 15% selling price change causing an alteration
in the NPV of approximately 47%, which makes the consid-
eration of the selling price a major issue. The choice of EPA-
rich oil in combination with the production of protein-rich
dry biomass as the target products also needs to be evaluated
carefully. The maximum net selling price for microalgae oil
which was found in the literature (EUR 144) resulted in a
reduction in the NPV by more than 90%. Another scenario
with microalgae oil as the target product was calculated
using a net selling price (EUR 209) that was planned accord-
ing to the average market price of EPA + DHA in microalgae
oil. This scenario provoked an NPV increase of 19%. The
effect of the length of the cultivation period on the NPV
was also rather substantial. Continuously shorter or longer
cultivation periods altered the NPV by almost one third com-
pared to the baseline scenario, which is a significant impact
taking into account that, for instance, longer periods implied
that production was just two weeks extended in both April
and October.
Return‑on‑investment
The ROI not only gives information about how lucrative an
investment will be but also at which point in time returns are
to be expected. The annual ROI of the microalgae cultivation
scenarios is depicted in Fig.7. The baseline scenario showed
an ROI of 80.66% after the 30-year lifespan, while positive
returns were to be expected after ten years and six months.
The least attractive scenario was microalgae oil as the target
product in combination with an application of the selling
price suggested in the literature. This scenario only had an
ROI of 6.41% after the whole lifespan and did not portray a
positive ROI before more than 24years. Two more scenarios
were relatively unprofitable with an ROI below 55%, namely
the short period scenario (54.59%) and the scenario with the
selling price dropping by 15% (42.92%). These scenarios
had positive returns after 12years and 5months, and after
13years and 9months respectively. The most profitable sce-
nario was the one with the selling price rising by 15%, which
resulted in an ROI of 118.21% and positive returns after
9years. The scenario with constantly long cultivation peri-
ods followed. Here, an ROI of 106.62% could be expected,
with positive returns after 9years and 3months.
However, since the investment duration covers a rather
long timeframe of 30years, a more robust analysis of the
ROI can be obtained by annualizing the value. The annual-
ized ROI is illustrated in Fig.8 for all scenarios. The base-
line scenario in this case accomplished returns of 1.87% per
year. All scenarios yielded returns between 1.1 and 2.5%
per year, except for the microalgae oil scenario applying the
literature selling price which only had returns of 0.19% per
year and thus was rather unprofitable. Besides a 15% rise in
the net selling price, the most lucrative scenario was the one
with extended production periods from April until October.
This scenario accounted for 2.29% of annual returns.
Discussion
To the best of our knowledge, here for the first time, in this
techno-economic analysis we assessed the expenditures
and revenues of cultivating microalgae for food in a humid
continental climate in order to determine the NPV and the
ROI. A set of different scenarios comprising the alteration of
technic and economic parameters to the system was analyzed
to identify critical processes.
The biggest share of the investment cost was held by the
costs for the glass tubes system, the dryer and the buildings
Fig. 6 Effects of parameter variation on the NPV of the photobioreactor
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1488 S.Schade, T.Meier
1 3
of the facility. All other costs were under 10% of the invest-
ment. Concerning operating cost, a great portion was due to
labor (around 40%), followed by costs for the maintenance
of the mechanical and electrical equipment. All costs exclud-
ing interests and the contingency factor summed up to EUR
9.60 per kilogram dry biomass of which 80% were made
up by infrastructure, labor cost and maintenance cost. In
the context of the PBR considered, this resulted in an NPV
of EUR 4.5 million and positive returns in year eleven with
an annualized ROI of 1.87%. Beside the application of the
literature price for microalgae oil, which hardly yielded a
positive value, the least favorable scenarios were the net sell-
ing price reduction by 15% (−47% NPV) and continuously
short cultivation seasons (−32% NPV). The best options, in
return, included a net selling price increase of 15% that aug-
mented the NPV by 47% and continuously long cultivation
periods which accounted for a 32% higher NPV. Alterations
in the glass tube price, commodity prices and microalgae
species all affected the NPV to less than 12%. With regard
to the ROI, all scenarios yielded positive returns of annually
Fig. 7 Annual return-on-invest-
ment for microalgae cultivation
scenarios
Fig. 8 Annualized return-on-
investment in % for the different
microalgae cultivation scenarios
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1489
Techno-economic assessment ofmicroalgae cultivation inatubular photobioreactor forfood…
1 3
1.1–2.5%. Only microalgae oil as the target product sold for
the price drawn from the literature had very little returns of
0.19% annually.
The sensitivity analysis revealed that the NPV of the
microalgae cultivation system always stayed positive. How-
ever, the profit varied drastically at times. In particular, the
parameters, which withdraw from outer control to a certain
extent, must be considered carefully. In particular, the influ-
ence of cultivation season length showed an immense impact
in our analysis. Both constantly short and long cultivation
periods altered the NPV by almost one third, which poses
a great concern keeping in mind that this is a parameter,
which is dependent on the climatic conditions. With EUR
10.48, the total cost per kilogram dry biomass was almost
as high in the short period scenario as in the microalgae
oil scenario (EUR 11.00) in which a whole new process
step was added. Another surprising factor was the alteration
provoked through a change in the microalgae species culti-
vated. P. tricornutum induced an NPV rise of 7.3%, which
is immense considering that the nutritional profile of the
microalga is similar to this one of Nannochloropsis sp. This
implies that comparable alterations can also occur within
the cultivation of one species, as certain fluctuations in the
nutritional profile and growth performance are natural, even
when cultivation processes and parameters are optimized.
Both these fluctuations attributable to the microalgae them-
selves and the climatic conditions should be reflected in the
selling price, which could be used to absorb any instabilities
to a certain extent due to uncontrollable cultivation param-
eters and thus function as a ‘buffer.’
The market analysis showed that the net selling price for
microalgae biomass drawn from the literature was rather at
the lower end of actual market prices. In fact, selling prices
varied tremendously but were disguised through the use of
different packaging sizes and content units. In the European
Union, there are only few microalgae species, which have
already been approved for human consumption by regula-
tors, in terms of either the whole biomass or concerning
certain metabolites. Chlorella vulgaris and Arthrospira plat-
ensis have been consumed in reasonable amounts prior to the
Novel Food Act and were as such approved by implication.
Moreover, Odontella aurita and Tetralsemis chuii have been
approved (European Commission 2018) as well as algae oil
from Ulkenia sp., Schizochytrium sp and Crypthecodinium
cohnii (European Commission 2018; Molino etal. 2018)
and antioxidants from Haematococcus pluvialis, Nanno-
chloropsis gaditana and Dunaliella (Enzing etal. 2014;
European Commission 2018). The compound market value
of microalgae products is assumed to reach a market value
of USD 3.32 billion by 2022 (Molino etal. 2018). Dry bio-
mass is still mainly produced from Arthrospira and Chlo-
rella and sold as a whole. However, besides the extraction of
n−3 PUFAs, the market for antioxidants from microalgae
is promising, with astaxanthin achieving a market price of
USD 2500–7000 per kilogram (Panis and Carreon 2016) and
β-carotene realizing USD 300–1500 per kilogram (Barkia
etal. 2019). DHA is currently largely produced in hetero-
trophic cultivation from Cryptothecodininum cohnii and
Schizochytrium limacinum (Barkia etal. 2019). Taking into
account the restraints of heterotrophic cultivation, above all
concerning land use, the here modeled system could provide
an environmentally friendly alternative. The environmental
analysis of the cultivation system also showed that micro-
algae production in a humid continental climate is feasible
concerning environmental impacts. The carbon footprint and
energy use compared well to wild-caught fish and favorable
to aquaculture fish, whereas the latter was specifically char-
acterized by a relatively high land use rate. Costs for Atlan-
tic salmon (Salmo salar) aquaculture production, the third
most consumed fish species in Europe (EUMOFA 2018) and
the globally largest single fish commodity by value (FAO
2018), have been increasing over the last years mainly due
to higher costs for feed as well as increasing labor costs and
depreciation (Iversen etal. 2020). Production costs varied
between USD 4.35 and 5.93 (EUR 3.92–5.35) per kilogram
depending on the country of origin (Iversen etal. 2020). Pre-
supposed an EPA + DHA content of 7.43g/kg edible salmon
(Schade etal. 2020), this results in an EPA + DHA price of
0.52–0.72EUR/g. Nannochloropsis sp. contains 42g EPA/
kg dry biomass. If the costs for interests and the contin-
gency factor are added to the production costs, it needs EUR
12.43 to cultivate one kilogram of biomass. Consequently,
this accounts for an EPA price of 0.29 EUR/g, which makes
microalgae a superior source for n−3 PUFAs in economic
terms, too. As a final point, microalgae are able to serve the
market for vegan products and as such represent the only
extensive vegan source for EPA and DHA.
At some points in the cost model of the system, assump-
tions had to be made due to a lack of data, which can be
tracked in the summary of the cost model in the supplemen-
tary material (Tab. S3). Moreover, a major constraint was
the diversity of net selling prices on the market and in the
literature. However, these points were addressed by intro-
duction of the contingency factor.
The here conducted analysis poses a first calculation of
economic profitability in a ‘cold-weather’ climate and can
thus function as a primary reference for stakeholders who
consider investing in such a system. The system model used
in this study introduces the photoautotrophic cultivation of
EPA from Nannochloropsis sp. In particular the production
of n−3 PUFAs largely relies on heterotrophic cultivation
which results in a competition with other food production
systems for arable land. It would be remarkable to compare
these systems in a techno-economic assessment for eco-
nomic profitability, and in a life cycle assessment for their
environmental impacts. Heterotrophic production has hardly
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1490 S.Schade, T.Meier
1 3
been studied in the literature and analyses on its economic
value a scarce. Considering the growing market for n−3
PUFAs, especially EPA and DHA, such investigation would
be of high interest. Here, to our knowledge for the first time,
we showed that microalgae cultivation in a ‘cold-weather’
climate is feasible, which enlarges the possible production
area for microalgae tremendously. While a great part of
global microalgae production is based in Asia where it is
cultivated in open ponds, PBRs in a ‘cold-weather’ climate
could be a save alternative, especially regarding the risk
of contamination. The preferred application of PBRs in a
colder climate, such as the humid continental climate, could
possibly lead to the establishment of a whole new industry
in these climates.
However, the risks deriving from the climatic precondi-
tions need to be assessed carefully, given that constantly
short cultivation periods over a few years could seriously
hamper the economic viability of such cultivation plants.
The adjustment of the selling price of the final products defi-
nitely should consider these aspects.
Conclusion
Distinct microalgae species have the potential to comple-
ment human nutrition with health-promoting and disease
suppressing nutrients and metabolites. It has been shown
that microalgae cultivation is profitable in a humid conti-
nental climate, a so-called ‘cold-weather’ climate. Yet, the
climatic preconditions can influence the economic profitabil-
ity distinctly which should be reflected in the selling price of
the target product. Given the abundance of essential nutri-
ents in microalgae resulting in beneficial effects on human
health, it is probable that their cultivation becomes a massive
industry (Probst etal. 2015) even more so since they are still
considered as a ‘poorly explored natural source for a healthy
diet’ (Sathasivam etal. 2019). Finally, the exploitation of
microalgae could help reduce pressure on natural fish stocks.
Supplementary information The online version contains supplemen-
tary material available at https:// doi. org/ 10. 1007/ s10098- 021- 02042-x.
Funding The study was funded by the Federal Ministry of Education
and Research (Grant No: 031B0366A). Open Access funding enabled
and organized by Projekt DEAL.
Compliance with ethical standards
Conflict of interest The authors declare no conflict of interests.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.
References
Adarme-Vega TC, Thomas-Hall SR, Schenk PM (2014) Towards
sustainable sources for omega-3 fatty acids production. Curr
Opin Biotechnol 26:14–18. https:// doi. org/ 10. 1016/j. copbio.
2013. 08. 003
Barkia I, Saari N, Manning SR (2019) Microalgae for high-value
products towards human health and nutrition. Mar Drugs 17:1–
29. https:// doi. org/ 10. 3390/ md170 50304
Barsanti L, Gualtieri P (2018) Is exploitation of microalgae eco-
nomically and energetically sustainable? Algal Res 31:107–115.
https:// doi. org/ 10. 1016/j. algal. 2018. 02. 001
Belda M, Holtanová E, Halenka T, Kalvová J (2014) Climate classifi-
cation revisited: from Köppen to Trewartha. Clim Res 59:1–13.
https:// doi. org/ 10. 3354/ cr012 04
Benemann J (2013) Microalgae for biofuels and animal feeds. Ener-
gies 6:5869–5886. https:// doi. org/ 10. 3390/ en611 5869
Borowitzka MA (2013) High-value products from microalgae-their
development and commercialisation. J Appl Phycol 25:743–
756. https:// doi. org/ 10. 1007/ s10811- 013- 9983-9
Chen J (2020a) Return on investment (ROI). In: Investopedia.
https:// www. inves toped ia. com/ terms/r/ retur nonin vestm ent. asp.
Accessed 10 Mar 2020
Chen J (2020b) Annualized total return. In: Investopedia. https://
www. inves toped ia. com/ terms/a/ annua lized- total- return. asp.
Accessed 10 Mar 2020
Chini Zittelli G, Lavista F, Bastianini A etal (1999) Production of
eicosapentaenoic acid by Nannochloropsis sp. cultures in out-
door tubular photobioreactors. J Biotechnol 70:299–312. https://
doi. org/ 10. 1016/ S0168- 1656(99) 00082-6
Chisti Y (2007) Biodiesel from microalgae. Biotechnol Adv 25:294–
306. https:// doi. org/ 10. 1016/j. biote chadv. 2007. 02. 001
de Oliveira Finco AM, Mamani LDG, de Carvalho JC etal (2016)
Technological trends and market perspectives for production
of microbial oils rich in omega-3. Crit Rev Biotechnol 37:656–
671. https:// doi. org/ 10. 1080/ 07388 551. 2016. 12132 21
De Vree JH, Bosma R, Janssen M etal (2015) Comparison of four
outdoor pilot-scale photobioreactors. Biotechnol Biofuels 8:1–
12. https:// doi. org/ 10. 1186/ s13068- 015- 0400-2
EFSA Panel on Dietetic Products Nutrition and Allergies (NDA)
(2012) Scientific Opinion on the Tolerable Upper Intake Level
of eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA)
and docosapentaenoic acid (DPA). EFSA J 10:1–48. https:// doi.
org/ 10. 2903/j. efsa. 2012. 2815
Enzing C, Ploeg M, Barbosa M, Sijtsma L (2014) Microalgae-based
products for the food and feed sector: an outlook for Europe. JR
Sci Policy Rep.https:// doi. org/ 10. 2791/ 3339
EUMOFA - European Market Observatory for Fisheries and Aqua-
culture Products (2018) The EU Fish Market. Brussels. https://
doi. org/ 10. 2771/ 41473
European Commission (2018) DURCHFÜHRUNGSVERORD-
NUNG (EU) 2018/1023 DER KOMMISSION vom 23. Juli
2018 zur Berichtigung der Durchführungsverordnung (EU)
2017/2470 zur Erstellung der Unionsliste der neuartigen
Lebensmittel
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1491
Techno-economic assessment ofmicroalgae cultivation inatubular photobioreactor forfood…
1 3
Eurostat (2019) Industriestrompreis in Deutschland in den Jahren 2000
bis 2018 (in Euro-Cent pro Kilowattstunde). https:// de. stati sta.
com/ stati stik/ daten/ studie/ 155964/ umfra ge/ entwi cklung- der- indus
tries tromp reise- in- deuts chland- seit- 1995/
FAO (2018) The State of World Fisheries and Aquaculture 2018: meet-
ing the sustainable development goals. Rome
Frischknecht R, Büsser Knöpfel S (2013) Ökofaktoren Schweiz 2013
gemäss der Methode der ökologischen Knappheit - Methodische
Grundlagen und Anwendung auf die Schweiz. Bundesamt für
Umwelt BAFU 256
Galán B, Santos-Merino M, Nogales J etal (2019) Microbial oils as
nutraceuticals and animal feeds. In: Goldfine H (ed) Health con-
sequences of microbial interactions with hydrocarbons, oils, and
lipids. Springer, Cham, pp 1–45. https:// doi. org/ 10. 1007/ 978-3-
319- 72473-7_ 34-1
García JL, de Vicente M, Galán B (2017) Microalgae, old sustainable
food and fashion nutraceuticals. Microb Biotechnol 10:1017–
1024. https:// doi. org/ 10. 1111/ 1751- 7915. 12800
Hilpisch Y (2014) Python for finance. In: MacDonald B, Blanchette
M (eds). O’Reilly Media Inc., Sebastopol.https:// doi. org/ 10. 1111/
febs. 12952
ISO Organisation (2006) ISO 14044:2006 - environmental manage-
ment - life cycle assessment - requirements and guidelines. Genf
Iversen A, Asche F, Hermansen Ø, Nystøyl R (2020) Production cost
and competitiveness in major salmon farming countries 2003–
2018. Aquaculture. https:// doi. org/ 10. 1016/j. aquac ulture. 2020.
735089
Keller H, Reinhardt GA, Rettenmaier N etal (2017) Integrated sustain-
ability assessment of algae-based PUFA production. In: PUFA-
Chain project reports, supported by the EU’s FP7 under GA No.
613303. IFEU - Institute for Energy and Environmental Research
Heidelberg, Heidelberg. https:// www. ifeu. de/ algae
Lardon L, Helias A, Sialve B etal (2009) Life-cycle assessment of
biodiesel production from microalgae. Environ Sci Technol
43:6475–6481
Lauer M (2008) Methodology guideline on techno economic assess-
ment (TEA). Graz
Lundquist T, Woertz I, Quinn N, Benemann J (2010) A realistic tech-
nology and engineering assessment of algae biofuel production.
Berkeley, California
Ma XN, Chen TP, Yang B etal (2016) Lipid production from Nan-
nochloropsis. Mar Drugs. https:// doi. org/ 10. 3390/ md140 40061
Matos ÂP (2017) The impact of microalgae in food science and tech-
nology. JAOCS, J Am Oil Chem Soc 94:1333–1350. https:// doi.
org/ 10. 1007/ s11746- 017- 3050-7
Meier T (2017) Planetary boundaries of agriculture and nutrition: an
Anthropocene approach. In: Proceedings of the symposium on
communicating and designing the future of food in the anthropo-
cene. Bachmann Verlag, Berlin, pp 69–79
Mišurcová L, Škrovánková S, Samek D etal (2012) Health benefits
of algal polysaccharides in human nutrition. In: Henry J (ed)
Advances in food and nutrition research, vol 66. Elsevier Inc, pp
75–145. https:// doi. org/ 10. 1016/ B978-0- 12- 394597- 6. 00003-3
Molino A, Iovine A, Casella P etal (2018) Microalgae characteriza-
tion for consolidated and new application in human food, animal
feed and nutraceuticals. Int J Environ Res Public Health 15:2436.
https:// doi. org/ 10. 3390/ ijerp h1511 2436
NASA—National Aeronautics and Space Administration (2019)
Power data access viewer. https:// power. larc. nasa. gov/
data- access- viewer/
Panis G, Carreon JR (2016) Commercial astaxanthin production
derived by green alga Haematococcus pluvialis: a microalgae
process model and a techno-economic assessment all through
production line. Algal Res 18:175–190. https:// doi. org/ 10. 1016/j.
algal. 2016. 06. 007
Patil V, Tran KQ, Giselrød HR (2008) Towards sustainable production
of biofuels from microalgae. Int J Mol Sci 9:1188–1195. https://
doi. org/ 10. 3390/ ijms9 071188
Peel MC, Finlayson BL, McMahon TA (2007) Updated world map of
the Köppen–Geiger climate classification. Hydrol Earth Syst Sci
11:1633–1644
Pérez-López P, de Vree JH, Feijoo G etal (2017) Comparative life
cycle assessment of real pilot reactors for microalgae cultivation
in different seasons. Appl Energy 205:1151–1164. https:// doi. org/
10. 1016/j. apene rgy. 2017. 08. 102
Posten C (2012) Design and performance parameters of photobioreac-
tors. Tech - Theor und Prax 21:38–45
Probst L, Frideres L, Pedersen B etal (2015) Sustainable, safe and
nutritious food. BusinessInnovation Observatory, European
Union, vol 17
Qiu C, He Y, Huang Z etal (2019) Lipid extraction from wet Nanno-
chloropsis biomass via enzyme-assisted three phase partitioning.
Bioresour Technol 284:381–390. https:// doi. org/ 10. 1016/j. biort
ech. 2019. 03. 148
Rebolloso-Fuentes M, Navarro-Perez A, Ramos-Miras JJ, Guil-Guer-
rero JL (2007) Biomass nutrient profiles of the Microalga Phaeo-
dactylum tricornutum. J Food Biochem 25:57–76. https:// doi. org/
10. 1111/j. 1745- 4514. 2001. tb007 24.x
Rockström J, Edenhofer O, Gaertner J, DeClerck F (2020) Planet-
proofing the global food system. Nat Food 1:3–5. https:// doi. org/
10. 1038/ s43016- 019- 0010-4
Ryckebosch E, Bruneel C, Termote-Verhalle R etal (2014) Nutritional
evaluation of microalgae oils rich in omega-3 long chain poly-
unsaturated fatty acids as an alternative for fish oil. Food Chem
160:393–400. https:// doi. org/ 10. 1016/j. foodc hem. 2014. 03. 087
Salem N, Eggersdorfer M (2015) Is the world supply of omega-3 fatty
acids adequate for optimal human nutrition? Curr Opin Clin Nutr
Metab Care 18:147–154. https:// doi. org/ 10. 1097/ MCO. 00000
00000 000145
Sathasivam R, Radhakrishnan R, Hashem A, Abd_Allah EF, (2019)
Microalgae metabolites: a rich source for food and medicine.
Saudi J Biol Sci 26:709–722. https:// doi. org/ 10. 1016/j. sjbs. 2017.
11. 003
Schade S, Meier T (2019) A comparative analysis of the environmental
impacts of cultivating microalgae in different production systems
and climatic zones: a systematic review and meta-analysis. Algal
Res. https:// doi. org/ 10. 1016/J. ALGAL. 2019. 101485
Schade S, Meier T (2020) Distinct microalgae species for food—
part 1: a methodological (top–down) approach for the life cycle
assessment of microalgae cultivation in tubular photobioreac-
tors. J Appl Phycol 32:2977–2995. https:// doi. org/ 10. 1007/
s10811- 020- 02177-2
Schade S, Stangl GI, Meier T (2020) Distinct microalgae species for
food—part 2: comparative life cycle assessment of microalgae
and fish for eicosapentaenoic acid (EPA), docosahexaenoic acid
(DHA), and protein. J Appl Phycol 32:2997–3013. https:// doi. org/
10. 1007/ s10811- 020- 02181-6
Schultz N, Wintersteller F (2016) Status and trends of photoautotrophic
algae cultivation from the viewpoint of a glass manufacturer. In:
European Algae Biomass. Berlin
Shimako AH, Tiruta-Barna L, Pigné Y etal (2016) Environmental
assessment of bioenergy production from microalgae based sys-
tems. J Clean Prod 139:51–60. https:// doi. org/ 10. 1016/j. jclep ro.
2016. 08. 003
Short W, Packey D, Holt T (1995) A manual for the economic evalu-
ation of energy efficiency and renewable energy technologies.
Renew Energy 95:73–81. NREL/TP-462-5173
Skarka J (2012) Microalgae biomass potential in Europe: land avail-
ability as a key issue. Tech - Theor und Prax 21:72–79
Templeton DW, Laurens LML (2015) Nitrogen-to-protein conversion
factors revisited for applications of microalgal biomass conversion
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1492 S.Schade, T.Meier
1 3
to food, feed and fuel. Algal Res 11:359–367. https:// doi. org/ 10.
1016/j. algal. 2015. 07. 013
Thomassen G, Egiguren Vila U, Van Dael M etal (2016) A techno-
economic assessment of an algal-based biorefinery. Clean Tech-
nol Environ Policy 18:1849–1862. https:// doi. org/ 10. 1007/
s10098- 016- 1159-2
Tocher DR (2015) Omega-3 long-chain polyunsaturated fatty acids and
aquaculture in perspective. Aquaculture 449:94–107. https:// doi.
org/ 10. 1016/j. aquac ulture. 2015. 01. 010
Trading Economics (2019) Germany bank lending rate. https:// tradi
ngeco nomics. com/ germa ny/ bank- lendi ng- rate. Accessed 3 Dec
2019
U.S. Bureau of Labor Statistics (2019) Producer price index by
commodity for chemicals and allied products: carbon dioxide
[WPU06790302]. https:// fred. stlou isfed. org/ series/ WPU06
790302. Accessed 25 Nov 2019
UKForex Limited (OFX) (2019) Yearly average rates. https:// www.
ofx. com/ en- gb/ forex- news/ histo rical- excha nge- rates/ yearly- avera
ge- rates/
van der Voort MPJ, Spruijt J, Potters J, etal (2017) Socio-economic
assessment of Algae-based PUFA production. Public output report
of the PUFAChain project
Walsh MJ, Gerber Van Doren L, Shete N etal (2018) Financial trade-
offs of energy and food uses of algal biomass under stochastic
conditions. Appl Energy 210:591–603. https:// doi. org/ 10. 1016/j.
apene rgy. 2017. 08. 060
Wang TH, Hsu CL, Huang CH etal (2016) Environmental impact of
CO2-expanded fluid extraction technique in microalgae oil acqui-
sition. J Clean Prod 137:813–820. https:// doi. org/ 10. 1016/j. jclep
ro. 2016. 07. 179
World Bank (2019) World Bank commodity price data (The Pink
Sheet): monthly prices. https:// www. world bank. org/ en/ resea rch/
commo dity- marke ts
Zhu Y, Anderson DB, Jones SB (2018) Algae farm cost model: consid-
erations for photobioreactors. Richland. https:// doi. org/ 10. 2172/
14851 33
Zhukova NV, Aizdaicher NA (1995) Fatty acid composition of 15 spe-
cies of marine microalgae. Phytochemistry 39:351–356. https://
doi. org/ 10. 1016/ 0031- 9422(94) 00913-E
Zimmermann AW, Wunderlich J, Müller L etal (2020) Techno-eco-
nomic assessment guidelines for CO2 utilization. Front Energy
Res 8:1–23. https:// doi. org/ 10. 3389/ fenrg. 2020. 00005
Publisher’s Note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional affiliations.
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
