CCU by Algal Polyacrylonitrile Fiber Production: Process-Design

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CCU by Algal Polyacrylonitrile Fiber Production: ProcessDesign, Techno-Economic Analysis and Climate related Aspects Uwe Arnold, Thomas Bartholomaeus Brück, Andreas De Palmenaer, and Kolja Kuse Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b04828 • Publication Date (Web): 04 May 2018 Downloaded from http://pubs.acs.org on May 5, 2018

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Industrial & Engineering Chemistry Research

CCU by Algal Polyacrylonitrile Fiber Production: Process-Design, Techno-Economic Analysis and Climate related Aspects Uwe Arnold a*, Thomas Brück b**, Andreas De Palmenaer c, Kolja Kuse d a

b

AHP GmbH & Co. KG, Karl-Heinrich-Ulrichs-Str. 11, D-10787 Berlin, Germany Werner Siemens Chair of Synthetic Biotechnology & Director TUM AlgaeTec Center,

Dept. of Chemistry, Technical University of Munich (TUM), Lichtenberg Str. 4, 85748 Garching, Germany c

Inst. Textile Technology, RWTH Aachen University, Otto-Blumenthal-Straße 1, 52074 Aachen, Germany d

TechnoCarbonTechnologies GbR, Oberföhringer Strasse 175 a, D-81925 München, Germany

1

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

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ABSTRACT STATIC & DYNAMIC TECHNO-ECONOMIC ANALYSIS

3

+ MONTE-CARLO-SIMULATION

Carbon capture and sustainable

product yields

PROCESS UNIT & CHAIN MODELS

4

process chain n

utilization (CCU) is essential to

resources demands

process chain 1

5

accomplish the targets of 2015’s Agreement.

A

promising

product price

NPV-IRR-ROE PAN fiber

6

Paris

7

option consists of algal based CO2

8

conversion into lipid rich biomass with further processing into polyacrylonitrile (PAN) fiber, the

9

major precursor for carbon fiber production. A first feasibility analysis was carried out under

10

multiple constraints for price, by-product yields, and consumption of land, CO2, and energy.

11

Several process-route alternatives were composed, modelled and compared in terms of mass and

12

energy flows, resources needs, and cost. To quantify risks from market and modeling

13

uncertainties, we conducted a primary techno-economic analysis (TEA) with variable process

14

pathways in a dynamic economic model of a related project company (SPV), embedded in a

15

Monte-Carlo simulation. First results indicate that process combinations with algal biodiesel-

16

production and biomass-liquefaction (BtL) components come close to meeting the multiple

17

constraints and justify progressing to extended research and development activities.

CO2

investment risk micro-algae biochemical + chemical conversion

CCU potential

18

INTRODUCTION

19

To meet the goals of the Paris Agreement of 2015 the constant rise of atmospheric greenhouse

20

gas (GHG) emissions needs to be stopped fast and effectively. In this respect the mere

21

substitution of fossil energy supplies by renewables will not be sufficient for a timely compliance

22

with the Paris goals. To that end CO2-sinks with lasting GHG-extraction and permanent storage

23

effects have to be installed in an industrially relevant scale.1 While current plans of massive

24

afforestation and reforestation are possible immediate actions, midterm concepts include


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25

combining biomass based power plants with carbon capture and storage equipment (BECCS)2,3,4

26

and of carbon capture and (sustainable) utilization (CCU)4,5.

27

A prominent CCU-solution involves carbon fiber production from CO2 via generation of lipid

28

rich algae biomass. Due to its high stability, flexibility and low weight carbon fibers are projected

29

to replace steel, aluminum, and concrete in technical applications, such as aircraft, automotive,

30

and building construction6,7,8,9. Currently, all of the aforementioned materials are generated from

31

fossil resources using processes with significant CO2 emission footprints10,11,12. However, only

32

carbon fibers have the potential to be generated directly from CO2 by combining biomass based

33

biotechnological with chemical transformation processes of optimum energy and mass efficiency.

34

As microalgae grow about 10 times faster than terrestrial plants, and can accumulate up to 70%

35

lipids w/cell d.w., they represent an optimal CO2 sink13,14,15,16. Moreover, microalgae cultivation

36

can be conducted in cost-effective open pond systems on non-arable lands, using either waste-,

37

brackish or salt water. Therefore, generation of algae biomass is feasible under economic

38

constraints without impacting agricultural activity, which is a prerequisite for sustainability

39

considering the food demands of a still growing global population. While the production of

40

cosmetics, biofuels and oleochemicals from oleaginous algae biomass has been reported17,18,19, a

41

method of producing polyacrylonitrile (PAN), the universal precursor of carbon fibers, from algal

42

biomass has not been disclosed to the authors’ knowledge17.

43

In a primary step this manuscript identifies and analyses new mass efficient, sustainable

44

process routes for the transformation of CO2 into PAN fibers via microalgae derived lipids. The

45

identified processes focus on the holistic conversion of algae biomass under economic and energy

46

constrains following a zero waste biorefinery approach. The PAN fibers derived via these



Page 4 of 42

47

processes can subsequently be converted to carbon fibers via various zero or low emission

48

technologies20.

49

Moreover, the cumulative data assembled in this study provides the basis for a first principles

50

techno-economic feasibility analysis applying multi-criteria constraints encompassing parameters

51

such as, acceptable product price, technical/ financial risk, accountable by-product streams and

52

acceptable land consumption as well as CO2-feed, and energy. The overall study contributes to

53

the development and industrial deployment of permanent CCU technologies. To that end we

54

followed a reverse engineering approach by first analyzing the required conditions for economic

55

viability of the investigated technology options prior to proceeding towards more detailed and

56

resource consuming scientific and technical developments.

57

Based upon predominantly well-established industrial processes and in some variations at least

58

well documented process prototypese.g. 5,21,22 (process chain units PCUs) the analyzed process

59

path variations were composed from PCUs and modelled in terms of mass and energy flows, area

60

and investment needs, and cost. Economic characteristics of the overall process chains were

61

obtained and analyzed by techno-economic analysis (TEA). The TEA was carried out primarily

62

in a broad variation analysis of alternative process chains with average values. Subsequently,

63

these routes were confined to selected most promising process chain alternatives. A dynamic

64

economic model of a related project company (SPV) covering an operation period of decades was

65

devised for in-detail TEA. In order to quantify risks from market and early stage process

66

modeling uncertainties, a Monte Carlo simulation (MCS) was applied to the dynamic SPV-

67

model. MCS provided probability density distributions and confidence intervals of project NPV

68

and return on equity (ROE) as additional qualifiers of economic and financial feasibility.

69


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70

MATERIALS AND METHODS

71

Selected Process Chains: Primarily, feasible bio-chemical process paths (process chain) were

72

constructed, allowing for the conversion of CO2 to polyacrylonitrile (PAN) fiber via algae

73

biomass. A process chain (PC) is the investigated system inside a balancing boundary and the

74

analysis object of the TEA. It consists of interconnected process chain units (PCU) sequences.

75

PCUs are separate (bio-) chemical processes with defined feedstock and products. In the present

76

study they consist of industrial standard processes or documented process prototypes, which can

77

be parameterized based upon literature values or approximations.

78

All process chains were composed from PCUs and compared: PC 1 (see Figure 1, TOP), which

79

consists of the PCU-sequence: algae farming in open pond photobioreactors with injection of

80

sequestrated CO2 or concentrated flue gas (A)23, algae processing to extract glycerol and algae oil

81

(B and C), transesterification to biodiesel (D)24,25,26,27,28,29,30,31, conversion of glycerol to methanol

82

(GtM-process, E)21,32,33,34,35, conversion of methanol to propylene (Mobil-process, MtP, F)36,37,

83

propylene based acrylonitrile synthesis (Sohio-process, G)38,39,40, and polymerization of

84

acrylonitrile to polyacrylonitrile and its fiber (Dralon-process, H)41. Possible variations of PC 1

85

are 1b, 1d with energetic utilization of biodiesel by means of combined heat and power (CHP)

86

plants, and 1c, 1d with internal coverage of the methanol demand of D by means of partial back

87

feed from E. PC 2 (see figure S3 in Supporting Information SI) is characterized by the “shortcut”

88

of directly converting glycerol to acrylonitrile (GtAN)22. PC 3 (see Figure 1, BOTTOM) consists

89

of the PCU-sequence: algae farming in algae ponds (A), conversion of algal biomass to methanol

90

by means of a BtL-process (biomass to liquids, biomass reforming to syngas, methanol synthesis,

91

PCU: Y)5,42,43,44, followed by the PCUs F, G, and H. PC 4 (see figure S5 in SI) is a combination



92

of 1 and 3 with coverage of the internal methanol demand of D by means of a preceding BtL-

93

component (Y).

A

B

algae

C

algae separation splitting alg.oil/glycerol

farm

,

D

E

F

G

H

Bio-Diesel

GtM

MtP

SOHIO

DRALON

process

MeOH-synthesis

propylene-synthesis

acrylonitrilesynthesis

PAN-fiber production

c, d ,

CO2

,

, ω

, ω

separation: lipids/oils - glycerol

methanol

separation triglyceride

,

NH3

, ω

air

glycerol

propylene

AN

PAN

,

,

,

,

Σ

∆

PAN

, methanol

,

,

,

,

,

H2O nutrients

glycerol

bio-diesel

H2O

,

,

,

b, d

CHPplant

∆

,

by-products: ethylene gazoline LPG water

, H2O

main product PAN-fiber

, HCN

other by-products

G

,

nutrients

,

A

Y

F

G

H

BtL/BtM

MtP

SOHIO

DRALON

farm

gasification MeOH-synthesis

propylene-synthesis

acrylonitrilesynthesis

PAN-fiber production

, ω , algae biomass

,

,

air

water

steam reforming

CO2

reforming, scrubbing

,

, NH3

, methanol

,

bio-diesel

algae

algae preparation

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

Page 6 of 42

, air

propylene

AN

PAN

,

,

, PAN

, methanol

, H2O

,

, H2O

,

by-products: ethylene gazoline LPG water

, H2O

main product PAN-fiber

, HCN

other by-products

,

94 95

Figure 1. Process flow diagrams for modeled system. TOP: process chain 1, i.e. production of

96

PAN-fiber from algae via biodiesel process, option a (basic reference process from A to H):

97

without internal methanol cycle and biodiesel conversion to heat and electric power, options b

98

and d with internal biodiesel conversion to heat and electric power, options c and d with internal

99

methanol cycle. BOTTOM: process chain 3, like process chain 1, but without algae processing 6 ACS Paragon Plus Environment

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100

(B+C) and biodiesel-process (D), instead conversion of complete algae biomass to methanol via

101

BtL-process (Y) incl. methanol (MeOH) synthesis.

102

Variant b again contains energetic utilization of biodiesel by means of a CHP plant. PC 5 (see

103

figure S6 in SI) consists of direct conversion of CO2 to methanol by means of Fischer-Tropsch

104

(FTS) and methanol synthesis (Z)45,46, followed by F,G, and H. Option b includes a high

105

temperature electrolysis component to cover the H2-demand of Z. PC 6 (see figure S7 in SI) (6b

106

with CHP) combines PCs 1 and 5 in analogy to 4 with preceding PCU Z to cover the methanol

107

demand of D. PCs 7 and 8 (see figures S8 and S9 in SI) contain an auto-thermal reforming and

108

partial oxidation step (ATR)47, converting biodiesel of D to syngas as input to the methanol

109

synthesis components of Z (PC 7) or Y (PC 8). Flow diagrams and specifications of PCs 2, 4, 5,

110

6, 7, and 8 are detailed in the Supporting Information (SI, section 3.1).

111

Process Chain Modeling, Mass and Energy Balances, PCU Characteristics: An object-

112

oriented modeling system was set up and applied to the mass flows in, out of PCUs and within

113

the process chains including total net energy consumptions, area demand, ISBL (inside battery

114

limits) investment needs and operation cost. Its structure represents a network of PCU-objects,

115

comprising (bio-) chemical processes with m mass inflows m*i,j (educts) and n mass outflows

116

m*i,k (products) – see figure S21 in SI. Invariable and inherited PCU-attributes are various mass

117

flow ratios (e.g. product yields m*i1/m*i1), specific energy q*i, area A’i, and investment K’Inv,i

118

demands, and specific cost parameters KOp,I – see figure S22 in SI. Attributes of PCU-instances

119

in a specific PC are explicit mass flows, energy and area consumptions, investment (ISBL)

120

demands and related operation cost. The PCU-links are created by addressing specific mass flows

121

as arguments in determination formulae of other mass flows. – see figure S23 in SI. The

122

comprehensive mass flows of a whole PC are the sums of PCU mass flows of the same reactant 7 ACS Paragon Plus Environment


Page 8 of 42

123

crossing the system boundary. Other comprehensive attributes of a PC are the sums of related

124

PCU attributes (e.g. energy and area demand, investment need, cost). A more detailed description

125

of the PCU-based mass and energy balancing model system and its application to PCs is

126

presented in the SI, section 4.1.

127

The characteristics of the single PCUs as used for static and dynamic TEA including average

128

base case values of mass ratios, specific energy, area, investment demands, and specific operation

129

cost, were derived from literature or heuristically determined. Details of every single PCU are

130

layed out in the SI, section 3.2. An overview of significant PCU crucial for the TEA is listed in

131

table 1.

132

Static Techno-Economic Analysis Method: To identify relevant PC-alternatives a broad

133

variation analysis was carried out with a static TEA based upon base case average values. This

134

process comprises static accounting of revenues, cost, and cost covering prices of PAN fiber.

135

Primarily a total capital investment, is calculated from ISBL cost of the PCUs, before capacity

136

specific scale effects were considered:

137 138

′!"#,$ = a ∗ ($ )

(1)

with K’Inv,i: specific ISBL investment demand of PCU i, related to Xi

139

X i:

reference attribute (e.g. production capacity) of PCU i

140

a, b

scale function coefficients (for derivation see SI)

141

Cost differentiation and the complete TEA calculus is discussed in the SI, section4.2. Due to

142

unknown plant site patterns, logistics, supply chain management and trade cost are not taken into

143

account explicitly. These factors are modeled indirectly either by means of the OSBL (outside

144

battery limits) surcharge or/and by increasing the overhead surcharge factor. Revenues of

145

secondary products and from CO2-pricing are deducted from total annual cost to finally derive


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146

the cost covering selling price of PAN fiber. For evaluation of economic viability the benefit cost

147

ratio (BCR)45 was applied: +,- =

148 149

with SN:

./0 1 )"1$. 120 324/5 .6. 6 ./0 1 5.

=

78 97:;< 9 0=>8 ∗ ?=>8 @A

(2)

revenues from secondary products (biodiesel, ethylene, LPG, gasoline etc.)

150

SCO2:

151

m*PAN annual production amount of PAN-fiber

152

PPAN

PAN target price (2.50 €/kg)

153

K3

total annual cost

CO2-revenues

154

Table 1.

Characteristics of Process Chain Units and Assumptions used for TEA of Process

155

Chains

156

Additional mass ratios, PCU features, and information about how specific PCU characteristics

157

were derived from source data, analogy considerations and qualified estimates are given in SI,

158

section 3.2.



PCU A

quantity

PCU B

PCU C

PCU D

PCU E

PCU F

PCU G

PCU H

PCU X

PCU Y

PCU Z

PCU ATR

PCU CHP

alg. oil extract

biodiesel

GtM

MtP Mobil

Sohio

Dralon

GtAN

BtL

FTS

autotherm. reforming

CHP

32, 21, 44, PROBAS

36, 37

38,39,40

fin.reports, press re. Dralon, Dormagen

22

42,44

44,45,46

47

algae ponds algae split

emp. T. Brück, TUM AlgaeTech and 25, 31, 48, 49

data sources

Page 10 of 42

mass ratios CO2 / algaedr

mass ratio

biodiesel/algae oil

, / ,

- term - value

propylene/methanol F acrylonitrile/propylene

, / ,

1.8

, / ,

0.824 triglyceride/algaedr

mass ratio

methanol Din /biodiesel

, / ,

- term - value

0.2857 , / ,

0.109

0.383

methanol/CO 2

C , / C,

1.163

D , / D,

0.21

gazoline/propylene ammonia/acrylonitrile

, / ,

0.5

acrylonitrile/glycerol

, / ,

0.685-0.791

formic acid/acrylonitrile

H2/CO 2

C, / C ,

D , / D,

1.667

0.560

, / ,

0.390

mass ratio

algae oil/triglyceride

methanol E/glycerol LPG/propyl. oxygen/acrylonitrile

ammonia/acrylonitrile

- term - value

, / ,

, / , , / , , / ,

C, / C ,

0.857

0.62 glycerol.2/biodiesel

mass ratio - term - value

0.077

0.905

ethylene/propylene

CO/biodiesel

E , / E,

3.950

1.427

PAN fiber/acrylonitrile

methanol/algaedr

, / ,

, / ,

, / ,

, / ,

0.102

0.043

1.00

0.41

H2 /biodiesel

E , / E,

0.155

spec. energy demand unit

[MWh/(t/yr)]

[MWh/(t/yr)] [MWh/(t/yr)] [MWh/(t/yr)]

[MWh/(t/yr)] [MWh/(t/yr)] [MWh/(t/yr)]

electric power, q*el,i

0.2 (ref. m*Aω,1)

0.944

0.518

-0.327

1.228

0.5

0.227

process heat, q*t h,i

0.2 (ref. m*Aω,1)

1.750

1.729

-1.053

2.865

1.0

0.740

[MWh/(t/yr)] 10.21 (electrolysis)

1.301 (FTS, Za)

[MWh/(t/yr)]

[-]

0.086

ηel = 0.31

0.651

ηth = 0.59

spec. area demand 2

algae productivity, a'A

21.9 [kg/m /yr]

spec.area demand, A'i

45.7 (ref. m*Aω,1)

unit

2

4.050

1.25

2.50

2.5

2.5

8.1

4

2

0.2

[m /(t/yr)]

[m /(t/yr)]

[m /(t/yr)]

[m /(t/yr)]

[m /(t/yr)]

[m /(t/yr)]

[m /(t/yr)]

[m /(t/yr)]

[m /(t/yr)]

2

[m /(t/yr)]

2

2

2

2

2

2

2

2

spec. cost 2

spec. ISBL-invest., K'Inv,i a: 20.122 ref. AA [m ] * if not otherw. spec. ref. m iω,1 b: 0.1 unit

spec. op. cost, K'Op,i

2

[€/(t/yr)]

[€/m ]

125 (ref. m*Dω,4)

unit

159

a: 808.18 ref. m*Dω,4 [t/yr] a: 8,760 a: 14,742 a: 30,247 a: 13,780 b: 0.1 b: 0.25 b: 0.25 b: 0.25 b: 0.25

[(€/yr)/(t/yr)]

[€/(t/yr)]

0.1 K'InvE

[€/(t/yr)]

44

[€/(t/yr)]

513.74

[(€/yr)/(t/yr)] [(€/yr)/(t/yr)] [(€/yr)/(t/yr)]

[€/(t/yr)]

417 [(€/yr)/(t/yr)]

a: 30,247 a: 6,428 b: 0.25 b: 0.25 [€/(t/yr)]

500

[€/(t/yr)]

76.4

[(€/yr)/(t/yr)] [(€/yr)/(t/yr)]

a: 6,428 b: 0.25

a: 2,870 b: 0.25

[€/(t/yr)]

25 [(€/yr)/(t/yr)]

[€/(t/yr)]

a: 4,975 ref. kWel b: 0.292 [€/kW]

12.5 19 €/kW +2 €/kWh [(€/yr)/(t/yr)]

[(€/yr)/(t/yr)]

160

Dynamic Techno-Economic Analysis and Monte Carlo Simulation. Static TEA identified a

161

set of PC candidates. Economic viability from an investor’s viewpoint is determined by analyzing

162

a PC alternative by means of DCF (Discounted Cash Flow) methodology and risk assessment

163

inclusion.

164

The principle model system applied in this study for dynamic TEA is a generic dynamic

165

business plan (“complete finance plan”), as applied to investment projects. It consists of a time

166

series stack of the standard financial accounting statements in combination with compatible

167

planning sheets of financing transactions in excess of 20 cost centers and up to 50 accounting

168

periods (e.g. years). Site, technology and case specific value creation processes, i.e. the dynamic

169

development of revenues and cost over time of plant operations as a function of multiple input

170

and boundary variables, are simulated in separate project specific calculation sheets (“project

171

specific sub-models”). 10 ACS Paragon Plus Environment

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172

The core model is embedded in multiple shells for sensitivity analysis and Monte Carlo

173

simulations (MCS). Probability density functions (Pdf) of MCS input quantities are derived by

174

iteration from worst/base/best case anchor values of selected input parameters (random variables)

175

of the dynamic TEA model. Main results are project net present value (NPV), internal rate of

176

return (IRR), return on equity (ROE), amortization time, average prices, sensitivity analysis

177

diagram and MCS results, such as pdf and confidence intervals of NPV, cumulated risk of loss

178

and other. A description of the model system, its underlying math and methods are published

179

elsewhere50. Computational performance constraints limit the number independent random

180

variables for MCS to 10 at 10,000 MCS samples. Additional details are presented in the SI,

181

section 4.3.

182

Scenarios. Two different reference scenarios were defined as base case scenarios for TEA:

183

Reference scenario A evaluates the economic viability of initial (first mover) investments into

184

the technology and assumes a complete production plant configuration with a production capacity

185

matching a medium size production plant for carbon fiber (CF) (e.g. the CF-production plant of

186

company SGL Carbon in Moses Lake, WA, U.S.A., capacity: 9,000 t/yr carbon fiber). Due to the

187

average yield of 0.5 kg CF per kg PAN fiber, an overall plant capacity of 18,000 t/yr PAN fiber

188

was chosen. Further assumption is the availability of a suitable plant site with sufficient

189

development and infrastructure. Thus, the OSBL surcharge was set to zero. Since for CO2-costs

190

only a credit equivalent in form of CO2-emission certificates is established, the value of CO2-

191

emissions avoided by the final CF is taken into account. Reference scenario B represents a “nth

192

plant scenario” with already established CCU-economy and a globally accepted CO2-pricing

193

system. Some countries, e.g. Sweden (110 €/t CO2 since 2010), Switzerland (96 CHF/t CO2 from

194

fossil fuels) already introduced such a fee. The model assumption for a CO2-price needed to 11 ACS Paragon Plus Environment


Page 12 of 42

195

foster CCU-techniques effectively is 100 €/t CO2. Hence, scenario B assumes the same capacity

196

and completeness of PC-investments as reference scenario A. Because of B being a “nth plant

197

scenario, the OSBL surcharge is set to 20% (remote locations left).

198

Base Case Input Data applied in both reference scenarios comprise: annual operation time

199

8,000 hrs/yr43,44,45, engineering and project management surcharge rate of 10%31, while the

200

contingency and working capital surcharge was set to zero since these quantities are covered by

201

dynamic TEA explicitly. The average depreciation period of the whole plant ensemble was set to

202

15 years (mean of range of 10 to 20 years

203

4% (actual quoting of KfW banking group), average labor cost to 22,500 €/yr (mean of spanning

204

Central Europe and MENA region) and the RMI rate to 3% of ISBL-investment29,45.

205

Additionally, an overhead surcharge 69% of labor cost25,45, venture and profit surcharge of 10%

206

on primary cost plus overhead was added. Based upon desk market research the following

207

feedstock prices were determined and used in TEA: methanol 400 €/t, formic acid 400 €/t,

208

ammonia 450 €/t, hydrogen 3,090 €/t, average of other 400 €/t (source: online commodity price

209

portals). Biodiesel wholesale price: 704.50 €/t51 (for orientation: on 1 Sept. 2017 the 5 year

210

average of petro-diesel prices was appr. 600 €/t52. Land price was estimated with 1,000 €/ha

211

(semiarid, not suitable for agriculture), heat price (natural gas) with 32 €/MWh (VCI statistics

212

2016), price of electric power with 100 €/MWh, which is slightly above the present LCOE

213

(levelized cost of electricity) of photovoltaics and onshore wind power53.

24,31,42,45

). The investment loan interest rate was set to

214

RESULTS

215

Assessment Criteria. First benchmark of economic viability of PCs is the current market price

216

of PAN fiber for CF production at outlet (2,500 €/t, based upon producer quotations of 2016).

217

PCs, however, are not only analyzed and assessed with respect to economic efficiency and


Page 13 of 42 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


218

viability. Moreover, their potential to contribute to global CO2 extraction and to establish global

219

CCU mechanism is of equal importance. Climate simulations suggest that 1 Gt extraction of

220

carbon (C) per year (appr. 1/8 of the current annual C-emissions from fossil fuels) would

221

contribute substantially to global climate change mitigation3. This could be achieved virtually by

222

111.111 of 18 kt-plants which would have the joint effect of a 1 Gt/yr C-extraction. Benchmark

223

are BECCS-concepts based upon large-area afforestation or large switchgrass plantations. The

224

switchgrass alternative would require about 5.8 mio. km2 of land3 marking the upper limit of

225

competitive algae-to-CF solutions. The demand of CO2 is a another assessment criterion for PCs

226

since available amounts of sequestrated CO2 are limited and less than global CO2,e-emissions

227

(2010 appr. 32 Gt/yr, 2015 appr. 36 Gt/yr4). Injection of atmospheric air to cover the CO2

228

demand of algae farming from atmospheric sources may be feasible, its impacts on investment

229

and operation cost and on the energy consumption of PCU A, however, have not yet been

230

determined reliably. The volumes of secondary PC-products such as biofuels (specifically

231

biodiesel) face different market absorption capacities. The order of magnitude of global diesel

232

consumption is 0.8 Gt/yr54. Regarding the status and trends of cargo transport (trucks, trains,

233

container ships), this value is not expected to decrease in the short perspective. Finally, energy

234

consumption of PAN and carbon fiber production is a constraint. The current global demand of

235

primary energy sums up to some 160,000 TWh/yr, which at least defines plausibility criteria for

236

the total energy consumption of a process meant to extract 1 Gt/yr of carbon.

237

Table 2.

238

Analysis

Process Chain Comparison based upon Results of Static Techno-economic



process chain

ref. scen. A ref. scen. B

239

1a

1c

1d

2a

3

4a

5b

6a

8b

18.00

18.00

18.00

18.00

18.00

18.00

18.00

plant scale

[kt/yr]

18.00

18.00

calculated PAN fiber price

[€/kg]

-1.82

-5.87

9.51

2.42

3.43

0.39

7.19

1.77

5.81

[-]

1.24

1.34

0.28

1.01

0.74

1.18

0.37

1.06

0.47

BCR with PAN-price 2.50 €/kg investment/annual sales ratio

1.71

1.38

162.68

1.47

39.95

1.94

71.60

2.27

break-even biodiesel-price (PAN 2.50 €/kg)

[€/kg]

535.51

503.98

-

699.14

-

571.38

-

658.34

calculated PAN fiber price

[-]

22.07

13,559.76

[€/kg]

-4.13

-12.02

1.69

-2.74

2.93

-4.08

7.05

-2.07

5.27

BCR with PAN-price 2.50 €/kg

[-]

1.55

1.73

1.08

1.27

0.89

1.53

0.41

1.33

0.56

investment/annual sales ratio

[-]

1.49

1.19

4.68

1.28

11.53

1.62

28.79

1.97

9.40

[€/kg]

0.00

0.00

90.41

5.36

165.63

0.00

1,188.37

0.00

490.03

4.15

8.62

8.68

4.04

0.73

4.86

0.04

4.16

0.78 28.37

break-even CO2-price (PAN 2.50 €/kg) global projection

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

Page 14 of 42

global area-need for 1 Gt/yr Carbon sink [mio. km2 ] global CO2-need for 1 Gt/yr Carbon sink

[Gt/yr]

161.98

337.46

337.46

158.88

26.43

188.41

8.36

166.78

global biodiesel for 1 Gt/yr Carbon sink

[Gt/yr]

31.73

66.11

0.00

31.12

0.00

31.73

0.00

31.73

0.51

global energy need 1 Gt/yr Carbon sink

[TWh/yr]

43,893

80,960

-530,536

40,125

16,453

52,651

71,801

79,585

18,781

240

Base Case Comparison of Process Chains. Complete results of the static TEA covering the

241

full set of investigated PC are presented in SI, section 6.1. Table 2 compares the most concise PC

242

features. The PAN fiber target price is met in both scenarios A and B by the biodiesel dominated

243

PCs (1a, 1c, 2a, 4a, 6a). Break-even CO2-prices in scenario B show that PCs 1a, 1c, and 4a have

244

potential to be viable prior to the introduction of a worldwide CO2 pricing system. Conversely,

245

direct BtL- and Fischer-Tropsch-conversion (FTS) of CO2 and ATR miss the target value of cost

246

covering PAN price, whereas area demand of these processes is a clear advantage in comparison

247

with the BECCS alternatives. Of these process chains which refrain from a biodiesel-PCU the

248

BtL-alternative comes closest to the PAN fiber target price. Additional criteria of market

249

compatibility are the amounts of additional educts needed (e.g. ammonia in PCU G) and

250

secondary products generated, such as biodiesel in PCU D. PCs, which satisfy the price

251

constraint, for instance, produce a challenging output of biodiesel. The break-even biodiesel

252

prices indicate that chances to withstand market volatility risks are provided for PC 1a, 1c, and 4a

253

since their break-even biodiesel-prices are lower than the mid-term average of the petro-diesel

254

wholesale price. For the 1 Gt/yr-C-extraction scenario, apart from FTS-based PCs 5a and b, all

255

other PCs need more CO2 than presumably available from carbon capture worldwide (present 14 ACS Paragon Plus Environment

Page 15 of 42 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


256

perspective). Hence, either additional CO2-sources (e.g. atmospheric) are to be made available

257

technologically or less CO2-consuming PCs are to be applied.

258

A comparison of the biodiesel based PCs (PC 1a, 1c, 2a, 4a, 6a) with the BtL-based (PC 3) and

259

FTS-based (PC 5b) alternatives in Table 2 clearly highlights the economic advantages (calculated

260

PAN-price, capital expenditure) of the path to produce PAN fiber as a byproduct of algal biofuel.

261

Especially PCs 1a and 1c and to a lower extent PC 4a as well seem to be highly attractive from an

262

economic point of view if the assumed market price for biodiesel can be achieved - a condition

263

which is not required for the BtL- and FTS-based PCs. In general, BtL- (PC 3), FTS- (PC 5b) or

264

ATR- (PC 8b) based process chains are preferable if area-, energy-, and CO2-demands need to be

265

minimized. A comparison of PC 1c with 4a, representing PC-alternatives of covering of the

266

methanol-demand of the biodiesel-process either by means of a methanol backflow cycle from

267

the exit of the GtM-unit (PC 1c) or by means of a preceding BtL-component parallel to PCU B

268

and C (PC 4a), reveals significantly lower specific area-, energy- and CO2-demands of the latter

269

alternative at the expense of economic efficiency. The comparison of PC 1d with PC 1c

270

illustrates that a complete local combustion of the biodiesel output in order to generate electric

271

power and process heat does not pay off (BCR of 1d is 37% to 79% lower than BCR of 1c) as

272

long as there is no matching additional industrial heat and power demand.

273

Static TEA results suggest that no single PC-alternative is completely satisfying, i.e.

274

simultaneously matching with all constraints and target values of the assessment criteria

275

introduced above. The 1 Gt/yr-C-extraction goal, however, can also be achieved by combining

276

capacities of plants with different PCs. Some examples of such combinations are layed out in the

277

SI, section 6.1, table S 17. In example, a combination of PC-3-plants which contribute 97% to the

278

joint capacity and of PC-1c-plants which cover just 3% of the joint capacity meets the PAN fiber



Page 16 of 42

279

target price and almost matches all remaining constraints. The related area demand is less than 1

280

mio. km2 (less than one eighth of the corresponding PC-1c value), while the CO2 demand is about

281

10% of the PC-1c value (order of magnitude of the actual global CO2-emissions). The biodiesel

282

output is less than 2 Gt/yr and the total energy demand is equivalent to about 11% of the current

283

global consumption of primary energy. This solution would come close to matching the

284

requirements of a relevant global CCU-mechanism.

285

Sensitivity Analysis. A broad sensitivity analysis of static TEA disclosed the influence of

286

single model input parameter variations to the benefit cost ratio (BCR), area demand, and cost

287

covering PAN fiber price. Main cost components and significant input parameters such as e.g.

288

algae productivity, specific CO2 consumption, interest rate and other were varied in a range of -

289

100% to +100% of their base case value. Figure 2 presents examples of TEA-sensitivity diagrams

290

concerning the influence of scale, algae productivity, biodiesel price and energy cost upon BCR

291

of selected PCs. It illustrates that scale effects upon BCR are moderate for practically all of the

292

compared process chains. Significant BCR-reductions (more than 10%) are observed below a

293

base case capacity of about 5 kt/yr. Economic efficiency is strongly affected if algae productivity

294

falls below 50% of the base case assumption (21.9 kg/m2/yr). Productivities in the order of

295

magnitude of 10 kg/m2/yr and above, however, were confirmed by multiple authors24,25,29,30 and

296

were already demonstrated in the TUM AlgaeTec Center23. The risk associated to the difference

297

between already observed algae productivity and the base case target value seems to be lower

298

than 10% of BCR. As expected the biodiesel price has a linear influence upon BCR of PC-1. In

299

variation of energy cost all related parameters (price of electric power and heat, biodiesel,

300

methanol, and hydrogen) were synchronized. Therefore, biodiesel and/or power generation as


Page 17 of 42

301

well as selling PCs in total, draw benefit from rising energy prices in contrast to purely energy

302

consuming PCs (e.g. BtL and FTS, PC-3 and 5b).

components, input parameters and PC features.

BCR

304

SI, section 6.2, provides complete results of sensitivity analysis, covering additional PCs, cost

2

2

1,8

1,8

1,6

1,6

1,4

1,4

1,2

1,2

BCR

303

1

1

0,8

0,8

0,6

0,6

0,4

0,4

0,2

0,2

0

0 0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

2

0

0,2

0,4

plant capacity (100% = 18,000 t/a) 1a

1b

1c

1d

3

4a

0,6

0,8

1

1,2

1,4

1,6

1,8

2

algae productivity (100% = 21 kg/m2/a) 5b

8b

1a

2

2

1,8

1,8

1,6

1,6

1,4

1,4

1,2

1,2

BCR

BCR

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


1

1c

1d

3

4a

5b

8b

1

0,8

0,8

0,6

0,6

0,4

0,4

0,2

0,2

0

1b

0 0

0,2

0,4

0,6

0,8

1

1,2

1,4

1,6

1,8

2

0

0,2

0,4

Diesel price (100% = 704.50 €/t) 1a

1b

1c

1d

3

4a

0,6

0,8

1

1,2

1,4

1,6

1,8

2

energy cost (100% = base values) 5b

8b

1a

1b

1c

1d

3

4a

5b

8b

305 306

Figure 2. Static TEA of process chain alternatives - sensitivity analysis results. Impact of scale

307

effects (upper left), algae productivity (upper right), biodiesel-price (lower left), and energy cost

308

variations (lower right) upon benefit-cost-ratio BCR for biodiesel, BtL, FTS and ATR based algal

309

PAN production process chains.



310

Dynamic TEA-Results, Economic Viability. For full assessment of economic viability based

311

upon a virtual SPV that carries out all required investments and operates the facilities, a dynamic

312

TEA including an evaluation of investment risk by means of MCS was applied to the 5 most

313

promising PCs. These are 1a, 1c, 3, 4a, and a combination solution with 90% of PC-3 and 10% of

314

PC 1c. Due to the limitation of the MCS-shell to 10 random variables, 10 input parameters with

315

highest model sensitivity had to be identified and initially selected . Applying the sensitivity

316

analysis shell of the dynamic TEA model to 46 model input parameters and based upon related

317

averaged partial NPV-gradients (significant influence of parameter X is given if

318

∂(NPV/NPVbase)/∂(X/Xbase) > 0.4), the parameters listed in the legend of Figure 3 were selected

319

for MCS. Worst/best case intervals, used as anchor values for computing the probability density

320

functions of the MCS input variables, were specified for each parameter. 50% 40%

change of project NPV

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

Page 18 of 42

30% PAN fiber price biodiesel price yield propylene/methanol yield propylene/acrylonitrile algae productivity yield methanol/glycerol yield methanol/algae (Y) investment demand yield triglyceride/algae el. power price (1st yr)

20% 10% 0% -10% -20% -30% -40% -50%

321 322

parameter variation

Figure 3. Dynamic TEA results. example of sensitivity diagram, PC 1c, ref. scenario B.


Page 19 of 42 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


323 324

Figure 4. Dynamic TEA results, example of MCS derived probability density distribution of

325

project NPV, PC 1c, ref. scenario B. the yellow dotted line denotes NPV=0, the blue dotted line

326

denotes the NPV-median.

327

Specific investment demands were varied within a +25%/-10% range of the base case values,

328

algae productivity was varied broadly within a -50%/+50% range, yields and mass ratios within

329

different parameter specific ranges of -10…20%/+10…20%. A more detailed description of the

330

model inputs and simulations is provided by SI, section 6.3.

331

Table 3.

332

PC3+10%PC1c in ref. scenarios A and B, design capacity 18,000 t/yr PAN fiber production,

333

TEA-results: investment sum, equity demand, ROE, NPV, amortization time, relative risk

334

measures

Results of dynamic TEA incl. MCS PCs 1a, 1c, 3, 4a, combination 90%



process chain:

1a

1c

351 110 13.74 8 611 0.39

582 180 15.42 7 1,351 0.12

0.34

0.15

Page 20 of 42

4a

90% PC3 + 10% PC1c

1c

151 50

CCU by Algal Polyacrylonitrile Fiber Production: Process-Design

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