Techno-economic Analysis of Microalgae-based Lipid Production

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Techno-economic Analysis of Microalgae-based Lipid Production: Considering Influences of Microalgal Species Seongwhan Kang, Seongmin Heo, and Jay Lee Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.8b03999 • Publication Date (Web): 09 Dec 2018 Downloaded from http://pubs.acs.org on December 9, 2018

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Industrial & Engineering Chemistry Research

Techno-economic Analysis of Microalgae-based Lipid Production: Considering Influences of Microalgal Species Seongwhan Kang, Seongmin Heo, Jay H. Lee* Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea

KEYWORDS: Microalgal lipids; Microalgal species; Cell characteristics; Techno-economic analysis; Sensitivity analysis

1 ACS Paragon Plus Environment

Industrial & Engineering Chemistry Research 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT

In this study, a techno-economic analysis is performed to analyze the effect of the choice of microalgal species on the economics of microalgae-based lipid production. Three microalgal species (Chlorella vulgaris, Tetraselmis suecica, and Nannochloropsis sp.) are selected given their disparate cell characteristics and high promise as feedstocks for commercial-scale microalgaebased lipid production. In the economic analysis, significantly different total production costs are obtained for the three species ($6.4/kglipid, $7.0/kglipid, and $8.3/kglipid for Chlorella vulgaris, Tetraselmis suecica, and Nannochloropsis sp., respectively), and the percentage of each processing stage contributing to the total cost also turned out to be quite different depending on the species. Based on the economic analysis, a sensitivity analysis is performed to analyze the effect of different cell characteristics on the overall economics, and to identify the most influential characteristic. The further scenario-based analysis shows that the economic results, as well as the relative standings of the species, change significantly when processing technologies are changed, pointing to the need to optimize the processing pathway individually for each species for a fair evaluation.


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1. INTRODUCTION

Microalgae have attracted attention as a promising feedstock for biofuel production owing to advantages such as high lipid productivity and ability to grow in a barren land and wastewater 1-4

. Despite such benefits, many challenges remain before microalgae-based biofuel (MAB) can be

commercialized. The main obstacle standing in the way is the poor economics of MAB. To tackle this shortcoming, intensive research efforts have gone into improving the economics of the MAB, either by developing efficient processing technologies or by designing and optimizing the microalgae-based biofuel production system 5. A typical microalgae-based biofuel production system is composed of a series of unit operations, starting from microalgae cultivation to biofuel conversion as shown in Figure 1. Various processing technologies are being developed and considered for each stage operation. Thus, the problem of designing an economical microalgae-based biofuel system can involve the evaluation and comparison of numerous processing pathway alternatives, which can be highly combinatorial. To solve such problem effectively, two different methods are mainly used: superstructure based optimization (for optimal processing route selection), and techno-economic analysis and optimization

6-7

. The superstructure optimization involves setting up and searching

through all the potential processing pathways to find the optimal one under a predefined objective function. Preceding such optimization is the techno-economic analysis (TEA), in which the economics of the system is analyzed under the assumption of operating conditions and processing parameters of the processing technologies involved in a specific pathway being investigated. So far, most such analysis and optimization studies in the literature have not considered the selection of microalgal species and its effects on the processing efficiencies. On the other hand, some 3 ACS Paragon Plus Environment


experimental and simulation results reported in the works of literature show that variations in the cell characteristics among species can have a significant impact on the process efficiency, and utilities and chemicals consumption 8-13. For instance, mechanical cell disruption, which is one of the pre-treatment processes before the lipid extraction stage, destroys cell walls with physical force, and the efficiency of this process is significantly affected by the physical strength of cell walls. Spiden et al. reported different cell disruption efficiencies of three microalgal species (Tetraselmis suecica, Chlorella sp., and Nannochloropsis sp.) after processing them by highpressure homogenization under a same operating condition 14. In the case of Tetraselmis suecica, most of the microalgae cells were disrupted when the process was performed once at 400bar, whereas cells remained mostly intact even at 800bar in the case of Nannochloropsis sp. These operation differences lead to differences in the economics of the cell disruption processes, which must be taken into account. On the other hand, few studies so far have analyzed the effect on the species selection on a quantitative basis.

Figure 1 General configuration of the microalgae-based biofuel production system


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Therefore, the objective of this study is to demonstrate the importance of considering the effect of cell characteristics in evaluating the economics of microalgae-based biofuel production. To this end, the techno-economic analysis integrated with models and parameters describing the effects of cell characteristics is performed for three representative species: Chlorella vulgaris, Tetraselmis suecica, and Nannochloropsis sp. From the results of the techno-economic analysis, influences of microalgal species choice and cell characteristics on the economics of microalgaebased lipids are investigated, and the major contributing processing stages for the economics are identified. Moreover, through a sensitivity analysis of the parameters related to cell characteristics, the major influential parameters are determined, and species selection and research strategy for reducing the production cost of MAB are suggested. Finally, a scenario-based analysis is performed to show how the relative economics among the species change when a different processing pathway is adopted.



2. SYSTEM DESCRIPTION

2.1 Microalgae-based lipid production system

In order to investigate the effect of cell characteristics on the economics of MAB, the microalgae-based lipid production system in Figure 2 is introduced as the base case. The system includes all the major processing stages from microalgae biomass production to lipid extraction and comprises five major stages: 1) microalgae cultivation, 2) biomass harvesting, 3) culture dewatering, 4) pre-treatment and 5) lipid extraction. The lipid extracted from microalgae in the extraction stage can be converted into biodiesel or bio jet-fuel through the appropriate conversion process, but in this analysis, the conversion process is left out since the composition of extracted lipid is assumed to be the same irrespective of microalgal species. For each processing stage, a representative processing technology was selected among the existing various processing technology based on the criteria of economics, scalability, and efficiency given in the literature. The data required for the techno-economic analysis, such as the reactor designs, operating conditions, chemicals/utility usages, and price information for each processing technology were decided based on the values from the references. The technical description and detailed data values for each processing technology are given in the following subsections. The total production scale of the system is assumed as 10,000 tons of microalgae-based lipids per year, assuming 330 days of operation per year, and the volume and flow rate of each processing stage were calculated to be commensurate with the assumed production scale.


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Figure 2 The microalgae-based lipid production system considered in the study

Under the suggested system, the economics of microalgae-based lipid derived from three target microalgal species (Chlorella vugaris, Tetraselmis suecica, and Nannochloropsis sp.) are calculated and compared. The process differences among the microalgae species are considered by assigning different process parameter values. The detailed description of the model and the parameters are discussed in the next subsection.

2.1.1 Microalgae cultivation

The microalgae cultivation systems people have studied come mainly in two forms: the open raceway pond (ORP) and the closed photobioreactor (PBR). Since the purpose of the system is to produce energy sources that can replace fossil fuels, the cultivation must be done at a large scale and low production costs. Therefore, the ORP is considered as the target cultivation technology in this study, as it can be constructed and operated at a lower cost and a larger scale compared to the closed photobioreactor system 1, 3. The key process parameters and assumptions used for the cultivation system are summarized in Table 1. The main equipment design and 7 ACS Paragon Plus Environment


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electricity consumption of the ORP and the carbon dioxide (CO2) supply are adopted from Lundquist et al 15. The assumed ORP unit is composed of a plastic liner and a paddlewheel that circulates the microalgae medium for improving the light distribution among the cells, and it has an area of 40,000m2 per unit at a medium depth of 0.3m to approach maximum economies of scale. The equipment cost of the ORP unit is fixed at $277,000/unit, and electricity is assumed to be consumed only by the paddlewheel, at 57.6kWh/ha/day. In order to cultivate microalgae successfully, it is necessary to supply CO2, water, light, and nutrients such as nitrogen and phosphorus, adequately. The CO2 is assumed to be supplied as 100% CO2 without impurities that would inhibit the growth of microalgae, such as sulfur oxides (SOx) or nitrogen oxides (NOx). This kind of flue gas is assumed to be supplied from nearby industrial plants such as natural gas sweetening, ammonia production, and coal and oil gasification. The delivery cost for CO2 is assumed as $0.04/kgCO2 to account for the upstream processes required to separate CO2 from the flue gas stream

16-17

. The amount of CO2 required for the

microalgae cultivation is calculated to be 1.9kgCO2/kgdry biomass, and CO2 consumption efficiency of microalgae cells is assumed as 75% 15, 18. The CO2 is transferred to the ORP by two counter-current carbonation sumps, which cost $5940/ha and consume 19.1kwh/ha/day of electricity

15

. The

nutrients required for the microalgae cultivation (e.g., nitrogen, phosphorous) are assumed to be supplied by externally purchased ammonia (NH3) and di-ammonium phosphate (DAP), and the amounts required for the cultivation are assumed as 0.0916kgNH3/kgdry

biomass

and

0.0127kgDAP/kgdry biomass, respectively. These values are calculated based on the Redfield ratio (C106N16P1) which describes the composition of marine phytoplankton as it has a similar composition with microalgae

18

. The purchasing costs for ammonia and DAP are assumed at

$0.407/kgNH3 and $0.442/kgDAP, respectively. The amount of makeup water for the cultivation is 8 ACS Paragon Plus Environment

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calculated based on the amount of loss due to evaporation (1.8m/day) and the amount of recycled water from harvesting and dewatering stage. The cultivation operation is assumed to be a batch process, which has certain advantages regarding enhancing the lipid contents and reducing the risk of microbial contamination. The initial inoculum concentration is assumed to be 0.1kg/m3, and after seven days of cultivation, the microalgae medium is assumed to be transferred to the harvesting process, and the new cultivation process is started. The final output biomass concentration transferred to the harvesting process is calculated according to the microalgae growth rate that varies with the species. The other processing operating conditions, e.g., light, pH, temperature, are assumed to be chosen optimally and not covered in this study.

Table 1 Process parameters implemented in the cultivation stage Parameters

Value

Reference

ORP area

40,000m2/unit

15

ORP depth

0.3m

15

ORP equipment cost

$277,000/unit

15

ORP electricity consumption

57.6kWh/ha/day

15

CO2 requirement

1.9kg/kgdry biomass

18

CO2 consumption efficiency

75%

15

CO2 supply equipment cost

$5,940/ha

15

CO2 delivery electricity consumption

19.1kWh/ha/day

15

Nitrogen requirement

0.0916kg/kgdry biomass

18

Phosphorous requirement

0.0127kg/kgdry biomass

18

Water evaporation loss

1.8m/day

19

CO2 price

$0.04/kgCO2

17



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Water price

$0.1/m3

20

Nitrogen source (Ammonia) price

$0.407/kgNH3

17

Phosphorous source (DAP) price

$0.442/kgDAP

17

Initial biomass concentration

0.1kg/m3

Batch operation time

7 days

2.1.2 Biomass harvesting

Since the microalgae biomass from the cultivation stage is in dilute condition (~0 .5kg/m3), the biomass should be concentrated for an efficient downstream operation and a reduction of processing scale. In this stage, microalgae biomass is harvested using pH-in duced flocculation and sedimentation, in which NaOH and HCl are used as the flocculan t and the defloccluant, respectively. The process parameters implemented for the harvestin g process are summarized in Table 2. A circular clarification pond used in wastewater tr eatment is assumed as the process equipment for the sedimentation process. Microalgae medium from the cultivation process is fed to the clarification pond at the flow rate of 4000m3/hr, and the equipment cost and electricity consumption for the pond are assumed as $2,264,134/unit and 0.1kWh/m3, respectively

21-22

. After the harvesting process, 90% of

the microalgae culture medium is assumed to be recycled without any additional treatme nt. In this process, the harvesting efficiency, NaOH dosage, and concentration factor all have different values for different species. These differences in the parameter values betw een the species and a reason for the differences will be discussed in the next section.


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Table 2 Process parameters implemented in the biomass harvesting stage Parameters

Value

Reference

Sedimentation tank feed flow rate

4000m3/hr/unit

21

Sedimentation tank equipment cost

$2,264,314/unit

21

NaOH price

$0.38/kg

11

HCl price

$0.25/kg

23

Electricity consumption

0.1kWh/m3

22

Medium recycling

90%

2.1.3 Culture dewatering

Although microalgae are concentrated through the harvesting process, additional c oncentration process is required since the concentration of microalgae is still below the c oncentration required for the efficient downstream operation. Therefore, the microalgae co ncentrate from the harvesting process is further dewatered using the dynamic filtration. T he process parameters used for the dewatering stage are listed in Table 3. The equipmen t design of dynamic filtration is the perforated rotation disk type developed by Kim et al . and the parameter values for the flow rate, equipment cost, and electricity consumption are obtained from the reference

24

. The process operation is assumed to be continuous th

at operates all day long (24h/day), and it can treat microalgae concentrate by the flow ra te of 7.434m3/hr. The equipment cost and electricity consumption of the filtration equipm ent are set to $500,000/unit and 75kWh/unit, respectively. The concentration of microalga e cake output from the dewatering process is assumed to be 250kg/m3 irrespective of spe cies and zero loss of microalgae biomass is assumed during the dewatering process.



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Table 3 Process parameters implemented in the culture dewatering stage Parameters

Value

Reference

Flow rate

7.434m3/hr

24

Filtration unit equipment cost

$500,000/unit

24


75kWh/unit

24

Output biomass concentration

250kg/m3

24

2.1.4 Pre-treatment

Microalgae cells are encapsulated by a thick cell wall, which inhibits the contact between solvents and microalgal lipids, resulting in poor lipid extraction efficiency. There fore, the cell disruption process facilitates the lipid extraction process by liberating the m icroalgal lipids from the cellular matrix, allowing the extraction solvent to contact them directly

25

. For this system, the high-pressure homogenization (HPH) process is selected a

s the reference processing technology, as it is effective in aqueous environments and can be operated continuously in a large scale

14

. In this process, the main operating condition

s (i.e. pressure, the number of processing paths) for each microalgal species are determin ed through the developed model, which will be presented in the next section, under the criteria that the process should achieve 90% disruption efficiency. The flow rate, electrici ty consumption, and equipment cost for high-pressure homogenization are referred from t he equipment manual for the GEA Ariete NS5355 homogenizer manufactured by GEA N iro Soavi and are summarized in Table 4.


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Table 4 Process parameters implemented in the pre-treatment stage Parameters

Value

Reference

Flow rate (400bar)

23m3/hr

Flow rate (1500bar)

5m3/hr

HPH equipment cost

$475,209/unit

Electricity consumption (400bar)

0.0487kWh/kgdry

biomass

Electricity consumption (1500bar)

0.1784kWh/kgdry

biomass

Cell disruption efficiency

90%

26 26

2.1.5 Lipid extraction

For the lipid extraction process, a hexane-based extraction process is selected since it has been extensively studied, and the hexane solvent can be easily separated from wet microalgae and recycled. In this study, the extraction process design and operation were referred from the simulation study conducted by Delrue et al 19. The main equipment required for the hexane based extraction process are the natural gas reboiler, which delivers the heat required for the extraction process, as well as the extraction column, heat exchanger, storage tank, and distillation column. First, in the extraction column, microalgae-based lipids are extracted from microalgae through mixing with the hexane, and then microalgae solid residue and water are separated by using centrifugation. After that, the mixture of solvent and microalgae-based lipids are separated by distillation. Recovered hexane from the distillation column are returned to the extraction column for recycling with 99% efficiency, and the required amount of the solvent is assumed as 10kghexane/kgdry

biomass

27

. The lipid recovery efficiency is assumed as 80%, and the energy

requirement and capital costs for the system are calculated from the simulation provided in the reference 19. The process parameters used for the lipid extraction are summarized in Table 5. 13 ACS Paragon Plus Environment


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Table 5 Process parameters implemented in the lipid extraction stage Parameters

Value

Reference

Extraction equipment cost

$88,445.5/kgdry

Natural gas reboiler equipment cost

$23,376.3/MW


0.00035kWh/kgdry

Heat consumption

1.3kWh/kgdry

Heat efficiency

80%

19

Extraction efficiency

80%

19

Solvent input

10kghexane/kgdry

Solvent recovery efficiency

99%

biomass/year

19 19

biomass

19 19

biomass

19

biomass

19

2.2 Cell characteristic models and parameters

In this study, three microalgal species are selected as target species for the analy sis: Chlorella vulgaris, Tetraselmis suecica, and Nannochloropsis sp. These species have been studied extensively as feedstocks for microalgae-based biofuel because of their high biomass productivities and lipid contents 1. Several key characteristics for these species a re summarized in Table 6.

Table 6 Cell characteristics of target microalgal species1, Microalgal species

Habitat

Morphology

Chlorella vulgaris

Freshwater

Tetraselmis suecica

Marine

Nannochloropsis sp.

Marine

Cell diameter

28

Biomass productivity (kg/m3 day)

Lipid content (%)

Spherical

~5μm

0.02-0.2

5.0-58.0

Prolate

~10.7μm

0.12-0.32

8.5-23.0

~2.5μm

0.01-1.43

12.0-53.0

spheroid Spherical 14

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Among the processing stages of the microalgae-based lipid production system, cul tivation, harvesting, and cell disruption are selected as the processing stages targeted for considering the effect of cell characteristics as summarized in Table 7. This subsection d escribes the models and values of the parameters that are used to consider the influences of different cell characteristics on the process efficiency and economics.

Table 7 Cell characteristics considered in different processing stages Processing stage

Processing technology

Considered microalgae cell characteristics

Cultivation

Open raceway pond

Lipid productivity

Harvesting

pH-induced flocculation

Habitat of microalgal species

Cell disruption

High-pressure homogenization

Cell wall strength

2.2.1 Microalgae cultivation: Open raceway pond

The growth rate and lipid content of microalgae vary depending on the species, l ife cycle of the microalgae cell, and cultivation conditions such as light, temperature, sali nity, pH, and nutrient1. While these factors influence the lipid productivity altogether, this study assumes that the environment or cultivation conditions are the same for all specie s, to consider only the effect of microalgal species on the lipid productivity. In order to establish the assumption, this study tried to use the experimental results under the similar operating condition for the cultivation of the target microalgal species in the open race way pond29. The parameter values for the biomass productivity and lipid content of each microalgal species used for the techno-economic analysis are summarized in Table 8.



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Table 8 Biomass productivity and lipid content for target microalgal species in open race way pond29 Microalgal species

Biomass productivity

Lipid content (%)

(kg/m3 day)

Lipid productivity ( kg/m3 day)

Chlorella vulgaris

0.053

25.0

0.013

Tetraselmis suecica

0.063

22.0

0.014

Nannochloropsis sp. 0.050

21.0

0.011

2.2.2 Biomass harvesting: pH-induced flocculation

The pH-induced flocculation involves precipitation of magnesium hydroxides (Mg( OH)2) induced by a pH increase as shown in Figure 3. In the normal state, microalgae c ells tend to stay apart from each other due to their negatively charged cell surface. As t he pH is increased by the addition of base or depletion of CO2, the magnesium ions co ntained in the microalgae medium start to react with OH- ions to form magnesium hydro xide precipitates, typically at a pH of 10~11

23

. Since the magnesium hydroxide precipita

tes are positively charged, they form large flocs with microalgae cells and induce sedime ntation of the microalgae cells. After the sedimentation, the precipitates can be dissolved in the medium by pH neutralization, so that microalgae concentrate can be obtained. In t his study, it is assumed that sodium hydroxide (NaOH) is used for flocculation and hydr ochloric acid (HCl) is used for deflocculation.


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Figure 3 An illustration of the mechanism of pH-induced flocculation

Since magnesium precipitate is a compound of magnesium ion and OH- ion, the amount of magnesium precipitate is controlled by the magnesium concentration and pH o f the medium (concentration of OH- ion). Therefore, differences in the magnesium conce ntration between the seawater (~50mM) and the freshwater (~1mM) result in the differen ces in the pH value at which flocculation occurs as shown in the experimental result 1

30-3

. In addition, the experimental results of pH-induced flocculation using several microalga

l species reported different harvesting efficiencies and concentration factor results dependi ng on the species

11

. Therefore, different values of NaOH and HCl dosages and concentr

ation factor are employed for the techno-economic analysis, which are summarized in Ta ble 9.

Table 9 Parameters for pH-induced flocculation of target species11,

Microalgal species

Chlorella vulgaris

NaOH dosage

HCl dosage

(kg/kgdry

(kg/kgdry

0.38

biomass)

biomass)

0.158 17 ACS Paragon Plus Environment

23

Harvesting

Concentratio

efficiency (

n factor

%)

(Cf/Ci)

95

31.0


Tetraselmis suecica

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0.40

0.167

93

24.0


0.042

95

19.2

2.2.3 Cell disruption: High-pressure homogenization

In the high-pressure homogenization (HPH), the microalgae cell medium is pumpe d into the narrow orifice with high pressure and collide with an impact ring or valve

25

.

Then the disrupted microalgae cells are released into a low-pressure chamber. Spiden et al. tested several numbers of paths of HPH under different pressures using three microal gal species (Chlorella sp., Tetraselmis suecica, and Nannochloropsis sp.) and compared th e disruption efficiency

14

. The experimental result revealed different disruption efficiencies

for the HPH process depending on the microalgal species, operating conditions (e.g., ap plied pressure,), and the number of processing paths. Since the microalgae cells are disru pted by the mechanical force caused by high pressure in the HPH process, the different cell disruption efficiencies are observed under the identical operating conditions of HPH due to the differences in the cell wall strength of the microalgae. Therefore, to predict the cell disruption efficiency for each microalgal species und er a given operating condition (pressure and number of processing paths), the following empirical model is developed: 𝑋𝑋𝑡𝑡 = 𝐴𝐴𝑁𝑁 𝑃𝑃

where Xt is the cell count ratio after the process relative to initial cell count, Ap, a pres sure dependent empirical parameter, and N, the number of times the cells are subjected t o the high-pressure homogenization path

14

. From the experimental result, the parameter 18


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value of Ap can be expressed as an empirical model which shows the linear relationship between Ap value and the applied pressure as summarized in Table 10. Since Ap is the ratio of microalgae cells which are disrupted per single path of high-pressure homogeniza tion, a smaller value of Ap implies that more cells are disrupted, and physical strength o f the cell wall is weak. Therefore, when comparing Nannochloropsis sp. with Tetraselmis suecica, it can be concluded that Nannochloropsis sp. has a much stronger cell wall ph ysical strength. Using the above model, the operating pressure and number of paths need ed to obtain 90% disruption efficiency for each target species are calculated. Under the 400bar condition and a single path of the process, the 90% disruption efficiency is obtai ned for Tetraselmis suecica, while the other two species are not disrupted even after mul tiple paths processed. Therefore, only Tetraselmis suecica is treated under the 400bar con dition, and the other two species are treated under the maximum pressure (1500bar) and multiple paths. The operating conditions for achieving the 90% disruption efficiency for e ach target species are summarized in Table 10.

Table 10 The equation of Ap value for each microalgal species and number of paths req uired to get the target disruption efficiency (90%), P: operating pressure in equipment (b ar)14 Operating pressure (

Species

Ap

Chlorella vulgaris

-0.0006P + 1.0476

1500

2

Tetraselmis suecica

-0.0023P + 0.9142

400

1

1500

6

bar)

Nannochloropsis sp. -0.00035P + 1.1839


Number of paths


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3. RESULTS & DISCUSSION

3.1 Economics of microalgae-based lipids

A techno-economic analysis is carried out under the assumptions, model, and pro cess parameters about the processes and microalgal species described in the previous sect ions, to analyze the effect of microalgae cell characteristics on the economics. Based on the equipment requirements, chemicals, utility usage and price information, the total prod uction cost (the cost of producing 1kg of microalgae-based lipids) is calculated for each species using the economic analysis method suggested by Delrue et al. and Chauvel et al . (Table 11)

19, 32

.

Table 11 The method used for the techno-economic analysis19, Parameter

32

Method of calculations Various estimation methods and actual prices from

Capital cost (CC)

manufacturers

Cost of chemicals (Cchem)

Actual prices of nutrients, solvents, chemicals Actual prices of electricity, natural gas, and utiliti

Cost of utilities (Cut)

es 0.2

Labor cost (Clabor)

 CC  LC = 106  6   10 × 500 

Other costs (Cother)

0.009 × CC

Operating cost (OC)

OC = Cchem + Cut + Clabor + Cother

General maintenance and storag

0.35 × CC

e costs (Cm&s)

0.15 × CC

Engineering cost (Ceng) 20


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Spare parts cost (Cspare)

0.15 × CC

License fees (Clicense)

Fixed at 0.5 M€

Fixed Capital (FCC)

FCC =CC + Cm& s + Ceng + Cspare + Clicense

Initial expenses (CIexp)

0.02 × CC

Process start-up cost (Cstart-up)

0.25 × OC

Additional expenses (CAexp)

C= CI exp + Cstart −up A exp

Depreciable Capital (DC)

= DC FCC + C A exp

Annuities

20 years

Discount rate

10% / year

Maintenance cost (Cmaintenance)

0.04 × FCC

Taxes and insurances (Ctax)

0.02 × FCC

Business expenses (CBexp)

0.01× FCC

Fixed cost (FC)

DCannalized+Cmaintenance+Ctax+CBexp

Total Production

TPC = FC + OC

Cost (TPC)

Can be converted into $/kg of lipid

The capital costs used in the techno-economic analysis are updated using dollar values for the year 2016 using the Chemical Engineering Plant Cost Index (CEPCI):

CC= CCref × 2016

CEPCI 2016 CEPCI ref

where CC2016 is the capital cost in 2016 dollars, CCref, a capital cost in the reference, CEPCI2016, the CEPCI value for 2016, and CEPCIref, the CEPCI value of the reference year.



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Figure 4 Total production costs of microalgae-based lipid of the three target microalgal s pecies

Table 12 Detailed cost breakdown of microalgae-based lipid in dollars per kg lipid for t he target species. Indirect capital cost includes: general maintenance, storage, engineering, spare parts, and license fees Process

$/kglipid

Fixed Cultivation

Operating

Elements

Chlorella

Tetraselmis su

Nannochlorop

vulgaris

ecica

sis sp.

Reactor cost

0.664

0.671

0.831

Land cost

0.068

0.069

0.086

0.520

0.525

0.649

Other

0.206

0.212

0.256

Total

1.458

1.478

1.822

Electricity

0.159

0.161

0.199

CO2

0.533

0.619

0.635

NH3

0.196

0.228

0.234

Indirect capital c ost


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DAP

0.030

0.034

0.035

Water

0.255

0.258

0.319

Labor

0.067

0.067

0.070

Other

0.060

0.061

0.076

Total

1.300

1.428

1.567

Total

2.758

2.906

3.389

Sedimentation tan k Fixed

0.115

0.143

0.081

0.081

0.100

Indirect capital c ost Other

Harvesting

0.115

0.051

0.058

0.035

Total

0.247

0.254

0.278

Electricity

0.089

0.090

0.111

NaOH

0.758

0.928

0.237

HCl

0.208

0.254

0.065

Labor

0.045

0.045

0.047

Other

0.009

0.009

0.011

Total

1.109

1.326

0.472

Operating

Total

1.356

Filtration unit

1.580

0.750

0.346

0.452

0.696

0.231

0.300

0.459


Fixed

Other Dewatering

n

Fixed

0.077

0.117

Total

0.636

0.829

1.273

Electricity

0.280

0.365

0.563

Labor

0.057

0.060

0.065

Other

0.026

0.035

0.053

Total

0.363

0.460

0.682

Operating

Cell disruptio

0.059

Total

0.999

Homogenizer unit

0.060

0.011

0.202

0.046

0.014

0.138

1.289

1.955




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Other

0.015

0.004

0.046

Total

0.120

0.028

0.386

Electricity

0.143

0.023

0.510

Labor

0.040

0.028

0.051

Other

0.005

0.001

0.015

Total

0.187

0.052

0.576

0.308

0.080

0.962

0.052

0.059

0.062

0.002

0.003

0.003

0.042

0.047

0.049

Other

0.037

0.041

0.043

Total

0.133

0.150

0.157

Electricity

0.0001

0.0002

0.0002

Heat

0.500

0.568

0.595

Solvent

0.370

0.421

0.441

Labor

0.039

0.040

0.040

Other

0.004

0.005

0.005

Total

0.914

1.034

1.081

1.046

1.184

1.238

Total fixed cost

2.595

2.739

3.915

Total chemical cost

2.480

2.889

2.120

Total utility cost

1.041

1.060

1.825

Total labor cost

0.248

0.241

0.274

Total other cost

0.104

0.110

0.160

Total operating cost

3.873

4.300

4.379

Total

6.468

7.039

8.294

Operating

Total Extraction unit Natural gas reboil er Fixed


Extraction

Operating

Total

The calculated total production costs for microalgae-based lipids of the three target species are graphically described in Figure 4, and detailed cost breakdowns are summarized in Table 12.


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The economic analysis shows that different total production costs are obtained depending on which microalgal species is used ($6.468/kglipid, $7.039/kglipid, and $8.294/kglipid for Chlorella vulgaris, Tetraselmis suecica, and Nannochloropsis sp., respectively). In all three species, the operating cost is the largest contributor to the total production cost. The operating cost contributes about 60% of the total production cost in the cases of Chlorella vulgaris and Tetraselmis suecica, while the Nannochloropsis sp. case shows a slightly lower proportion of the operating cost (53%). Among the items of operating cost, the largest contributor was chemical cost, which contributes over 48% of the total for all three microalgal species. The ranking for the chemical costs calculated is Tetraselmis > Chlorella > Nannochloropsis, unlike the ranking of the total production costs. The largest difference in the chemical cost is seen in the harvesting stage, which comes from differences in the amounts of NaOH and HCl required for controlling the pH in the harvesting process. This amount is almost four times for Chlorella and Tetraselmis compared to Nannochloropsis. On the other hand, in the utility cost, Chlorella and Tetraselmis showed almost the same value while Nannochloropsis showed about 1.8 times higher value than the other two species. These differences mainly come from the dewatering and cell disruption stages. The concentration of Nannochloropsis biomass from the harvesting stage is lower compared to the concentrations of the other two species (14.67kg/m3, 13.04kg/m3 and 8.64kg/m3 for Chlorella, Tetraselmis, and Nannochloropsis respectively), and therefore, the volume of the culture medium to be processed in the dewatering stage for Nannochloropsis is larger. As a result, a larger number of filtration equipment is required, and the amount of electricity consumed is increased accordingly. For the same reason, Nannochloropsis shows larger electricity consumption in the cell disruption process because it requires more paths of the process to obtain the target disruption efficiency than the other two species. 25 ACS Paragon Plus Environment


In the case of fixed cost, Nannochloropsis shows a higher cost than the other two species (about 1.5 times larger), and among the processing stages, cultivation, dewatering, and cell disruption processes show huge differences in the fixed cost. In the cultivation process, the relatively low lipid productivity of Nannochloropsis results in a requirement of a larger number of ORPs for the production of the target amount of lipid, and also a larger number of equipment in the harvesting process and the cell disruption process. Analysis of costs by processing stages showed that cultivation, harvesting, and dewatering processes, which are processes for preparing the microalgae biomass for lipid extraction, accounted for more than 73% of the total production costs in all three species, and especially, in the case of Tetraselmis, these three processing stages accounted for 82.0% of the total production cost. These cost differences mainly came from processing scale differences. The microalgae biomass obtained from the cultivation is in very dilute condition (~0.5kg/m3), but in the extraction stage, the process treats microalgae in the form of a high-density cake (~250kg/m3). These concentration differences result in a 500-fold difference in the scales of the process. Therefore, a significant reduction in the production cost can be achieved by obtaining a high concentration of microalgae in the cultivation process and by obtaining a high concentration factor in the harvesting and dewatering process. In the extraction process, the cost for purchasing the hexane solvent was high despite the very optimistic assumptions about the recovery rate of hexane (99%). This suggests that it is necessary to develop an industrial scale process that can extract lipids from microalgae more effectively using less solvent.

3.2 Sensitivity analysis


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A local sensitivity analysis is carried out using the cell characteristics related parameters to determine which cell characteristics significantly affect the total production cost of microalgaebased lipids. The analysis is performed by changing the parameter values one by one by ± 50% of the baseline value to observe the change in the total production cost. The definition of sensitivity is defined as follows: Si ,±50% =

( Pi ,±50% − Pbase ) Pbase

where i is a subscript for target parameters, Pbase, a production cost of the base case, and Pi,±50%, a production cost of lipid when the value of parameter i is varied. The cell characteristic related parameters include microalgae growth rate, lipid content, harvesting efficiency, concentration factor, flocculants input, number of cell disruption paths, and lipid extraction efficiency. Among the parameters, the parameter values for harvesting, extraction efficiency and a number of disruption paths are varied in some pre-defined ranges (50% decrease and 100% value for efficiencies and ±1 paths for a number of disruption paths) as they cannot be varied ± 50% from the baseline value. The sensitivity analysis results for Chlorella vulgaris are depicted by tornado plots in Figure 5.



Figure 5 Sensitivity analysis results for Chlorella vulgaris, for extraction and harvesting efficiency, +50% means 100% recovery, for a number of disruption paths, the -50% valu e means single path and +50% scenario means triple paths

The results show that the most sensitive parameters are extraction efficiency and lipid content, followed by harvesting efficiency and microalgal growth rate. The reason w hy extraction efficiency and lipid content have the greatest impact is that they affect the processing scale of the overall production system to meet the target lipid yield, whereas harvesting efficiency and growth rate only affect the costs of cultivation, harvesting and dewatering processes, rather than the entire processing pathway. Therefore, optimizing pro cess efficiencies (harvesting and extraction) and microalgae lipid productivity (lipid conten t and growth rate) is expected to improve the economics of microalgae-based biofuel dra


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matically. The results for Tetraselmis and Nannochloropsis (not shown here) showed alm ost the same results as Chlorella. Among the process parameters not related to the cell characteristics, the recovery rate of hexane in the extraction process shows high influences. In the current economic analysis, the recovery rate of hexane is assumed as 99%, which is an optimistic assumpti on. When the hexane recovery rate decreases to 90%, the total production cost of lipids using Chlorella vulgaris increases to $11.109/kglipid, which represents a 71.8% increase. T herefore, it is important to reduce the usage of makeup solvent in the extraction process to improve the economics of microalgae-based biofuel.

3.3 Scenario analysis of microalgae-based lipid production

Since various processing technology options exist for microalgae-based biofuel pro duction system and each technology option operates through different processing mechanis ms, choice of processes may also influence the effects of microalgae species on the over all economics. Therefore, it is essential to apply the appropriate processing technologies f or each microalgae species to obtain the best economics. In order to investigate the diffe rences in the effects of cell characteristics quantitatively, the economic analysis is repeate d after the introduction of alternative processing technology. In an alternative scenario, ch emical flocculation using FeCl3 as a flocculant is used instead of pH-induced flocculation in the harvesting stage. It is assumed that the operating conditions and the economic pa rameters of the chemical flocculation are the same as those used in the base case except for the amount of flocculant input and price ($0.5/kg for FeCl3). Table 13 summarizes t 29 ACS Paragon Plus Environment


he values of flocculant input, harvesting efficiency, and concentration factor, for the three microalgal species in the alternative scenario.

Table 13 Parameters for chemical flocculation of target species11 Microalgal species

FeCl3 input

Harvesting

(kg/kgdry

efficiency (%)

biomass)

Concentration factor (Cf/Ci)

Chlorella vulgaris

0.12

96

28.6

Tetraselmis suecica

0.04

94

39.5


92

31.0

Figure 6 Economic analysis result for the target microalgal species under the alternative processing pathway using chemical flocculation (FeCl3) Table 14 Breakdown of differences in production cost between the base case and the alt


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ernative scenario case Stage

Elements

Flocculant Harvesting Total operating cost

Fixed

Dewatering

Electricity

Total operating cost

Chlorella

Tetraselmis

Nannochlorop

vulgaris

suecica

sis sp.

Base case

0.966

1.182

0.302

Alternative Difference

0.312 -0.654

0.121 -1.061

0.097 -0.205

Base case

1.356

1.580

0.750


0.680 -0.676

0.485 -1.095

0.542 -0.208

Base case

0.636

0.829

1.273


0.689 +0.053

0.509 -0.350

0.827 -0.446

Base case

0.280

0.365

0.563

Alternative

0.223

Difference

0.303 +0.023

-0.142

0.365 -0.198

Base case

0.999

1.289

1.955

Alternative

1.079 +0.080

0.807 -0.482

1.286 -0.669

$/kglipid

Difference

Figure 6 shows the economic analysis results under the alternative processing pathway using chemical flocculation. All three species show lower production costs in the alternative scenario compared to the base case (-$0.625/kglipid, -$1.608/kglipid, and -$0.735/kglipid for Chlorella vulgaris, Tetraselmis suecica, and Nannochloropsis sp.), and most of these reductions occurred in the harvesting and the dewatering stage. Among the species, Tetraselmis suecica showed the greatest reduction in the cost and this reduction made the Tetraselmis suecica to have the lowest production cost, unlike the base case. These differences in the reductions are due to species-tospecies differences in the degree of change in the flocculant input and concentration factor after harvesting as the processing technology changes. For instance, comparing Chlorella vulgaris and 31 ACS Paragon Plus Environment


Tetraselmis suecica, flocculant input for the base case was the same for the two species and the concentration factor was slightly higher for Chlorella vulgaris (31 for Chlorella and 24 for Tetraselmis), but in the alternative scenario, the flocculant input for Tetraselmis suecica is one third of the input for Chlorella vulgaris, and concentration factor is higher in Tetraselmis suecica. These differences made the reversal in the lipid production cost of the two species. To understand these cost reductions precisely, the breakdown of cost differences between the base case and the alternative scenario case is summarized in Table 14. First, in harvesting, the difference in the cost of flocculant varies significantly with species, and Tetraselmis suecica shows the largest difference in this cost. In addition, as the concentration factor in the harvesting stage is varied, the amount of culture medium to be treated in the dewatering stage is also changed under a different flocculant. This change alters the number of filtration units required, which affects the fixed cost and the electricity cost of the dewatering process. As the harvesting process is changed, the concentration factor of Chlorella vulgaris decreased increasing the dewatering cost, whereas, in the other two species, the dewatering cost decreased due to increases in the concentration factor. As a result of the scenario analysis, it can be concluded that the optimal processing pathway should be considered for each species for fair economic comparison. Although we only tested the harvesting stage in this analysis, the choice of processing technology should have a significant impact on the relative economics of species at other processing stages as well. Therefore, a processing pathway optimization model, such as superstructure models, should be extended to consider the impact of microalgal species on the production costs 33.


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4. CONCLUSION

In this study, the impact of choice of microalgal species on the economics of microalgaebased lipid production is analyzed by comparing three target microalgal species. As the different target microalgal species, Chlorella vulgaris, Tetraselmis suecica, and Nannochloropsis sp., have different cell characteristics, a techno-economic analysis is performed focusing on the differences in these cell characteristics and their effects on the processing efficiencies. The techno-economic analysis results showed substantially different production cost values for the three microalgal species ($6.5/kglipid, $7.0/kglipid, and $8.3/kglipid for Chlorella vulgaris, Tetraselmis suecica, and Nannochloropsis sp.) due to their varying cell characteristics. For all the tested species, cultivation, harvesting, and dewatering processes accounted for large portions of the total production costs due to relatively large processing scales compared to the other downstream processing stages. A sensitivity analysis was performed based on the techno-economic analysis result by changing some parameters related to cell characteristics. Based on the results of the sensitivity analysis, it was confirmed that the harvesting and extraction process efficiencies and lipid content are the most important parameters to be considered for the economics and their dependencies on the species should be determined precisely for accurate economic analysis. In the scenario analysis, assuming a different processing pathway resulted in different economic values and rankings among the target microalgal species compared to the base case ($5.8/kglipid, $5.4/kglipid, and $7.6/kglipid for Chlorella vulgaris, Tetraselmis suecica, and Nannochloropsis sp., respectively). This result showed the importance of selecting the optimal processing technology for each target microalgal species for a fair evaluation.



AUTHOR INFORMATION Corresponding Author Jay H. Lee, Professor IEEE/IFAC/AIChE Fellow Department of Chemical and Biomolecular Engineering, Korea Advanced Institute for Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141 Republic of Korea Phone: +82-42-350-3966 Fax: +82-42-350-3966 E-mail: [email protected]


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Author Contributions Seongwhan Kang: 

Conception and design



Analysis and interpretation of data



Drafting the article

Seongmin Heo: 

Critical revision of the article for important intellectual content

Jay H. Lee: 

Critical revision of the article for important intellectual content



Final approval of the article



Funding Sources This work was supported by the Advanced Biomass R&D Center(ABC) of Global Frontier Project funded by the Ministry of Science and ICT (ABC-2011-0031354)

ACKNOWLEDGMENT This work was supported by the Advanced Biomass R&D Center(ABC) of Global Frontier Project funded by the Ministry of Science and ICT (ABC-2011-0031354)


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Techno-economic Analysis of Microalgae-based Lipid Production

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