Electronic Supplementary Material (ESI) for Green Chemistry. This journal is © The Royal Society of Chemistry 2017
Electronic Supplementary Information for
Quantitative multiphase model for hydrothermal liquefaction of algal biomass
Yalin Li,1 Shijie Leow,1,2 Anna C. Fedders,2 Brajendra K. Sharma,3 Jeremy S. Guest2 and Timothy J. Strathmann*1,4 1. Colorado School of Mines, Department of Civil and Environmental Engineering, Golden, CO 80401, USA. 2. University of Illinois at Urbana-Champaign, Department of Civil and Environmental Engineering, Urbana, IL 61801, USA. 3. University of Illinois at Urbana-Champaign, Illinois Sustainable Technology Center, Champaign, IL 61820, USA. 4. National Renewable Energy Laboratory, Golden, CO 80401, USA. *Corresponding Author: T. J. Strathmann, E-mail:
[email protected], Phone: +1.303.384.2226
S1
Table of Contents S1. Feedstock characterization and HTL product analysis results .......................................................................S3 Figure S1 Feedstock biochemical composition .............................................................................................S3 Table S1 Feedstock species and compositional analysis ...............................................................................S4 Table S2 HTL product yields and characteristics .........................................................................................S5 Table S3 Feedstock and biocrude fatty acid analysis ...................................................................................S6 S2. MCA model for HTL multiphase yields ...........................................................................................................S7 Table S4 Pearson Correlation Coefficients for feedstock components and HTL products ...........................S7 Figure S2 HTL aqueous yields and feedstock ash contents...........................................................................S7 Figure S3 Biochar appearance comparison ...................................................................................................S7 Table S5 Regression statistics for HTL product yield predictions ................................................................S8 S3. MCA model for elemental composition of HTL products ...............................................................................S8 Figure S4 Reported carbon distribution trends ..............................................................................................S8 Average oxidation state of carbon in feedstock ........................................................................................S8 Table S6 Regression statistics for HTL product characteristics predictions .................................................S9 Figure S5 Feedstock protein content and aqueous co-product nitrogen speciation .......................................S9 Figure S6 Biochar hydrogen and nitrogen content trends .............................................................................S9 Figure S7 MCA predicted product characteristics for data from this study ................................................ S10 S4. Model validation and research needs............................................................................................................... S11 Table S7 MCA predicted product yields for literature data (microalgae) ................................................... S11 Figure S8 MCA predicted product yield residuals (microalgae) ................................................................. S12 Table S8 MCA predicted biocrude yields for literature data (waste biosolids) ........................................... S12 Table S9 MCA predicted product characteristics for literature data (microalgae) ...................................... S13 Existing model validation ......................................................................................................................... S14 Figure S9 Existing model predictions ......................................................................................................... S15 Figure S10 Influence of feedstock non-FA lipid and protein components on HTL mass balance .............. S15 S5. Experimental methods ...................................................................................................................................... S16 Microalgae feedstock acquisition and characterization ......................................................................... S16 HTL reaction and product analysis ......................................................................................................... S16 References ................................................................................................................................................................ S17
S2
S1. Feedstock characterization and HTL product analysis results The feedstock collection provides a wide variation in biochemical composition with 0 – 43.0 dw% (dry weight basis) lipids, 11.4 – 69.8 dw% proteins, 10.0 – 63.6 dw% carbohydrates, and 2.0 – 13.1 dw% ash contents (Table S1), corresponding to the 0 – 46.7 afdw% (ash free dry weight basis) lipids, 12.9 – 85.6 afdw% proteins, and 12.8 – 75.8 afdw% carbohydrates (Figure S1). Carbon content varies from 42.7 to 56.9 dw%, hydrogen content from 6.1 to 8.7 dw%, nitrogen content from 1.8 to 11.2 dw%, and volatile oxygen content (estimated by difference as 100 – ash% – C% – H% – N%) from 23.5 to 43.5 dw%.
Figure S1 Ternary plot summarizing the biochemical composition (corrected to 100 afdw%) of HTL feedstocks used for the multiphase component additivity (MCA) model calibration. Detailed information on the feedstocks is provided in Table S1.
S3
Table S1 Feedstock species and compositional analysis (dw%)a Speciesb CZ1
Lipid 23.5±0.2
Proteinc 56.2
Carbohydrate 15.9±0.6
Ashd 8.4±0.1
Ce 51.9
He 7.3
Ne 9.0
Of 23.5
AOSC -0.56
CZ2
10.3±0.2
48.9
22.8±0.2
11.0±0.1
47.9
6.9
7.8
26.4
-0.47
CZ3
9.7±0.1
60.6
14.8±0.3
5.4±0.1
48.4
7.0
9.7
29.5
-0.31
CZ4
13.9±1.1
54.5
10.0±0.2
7.5±0.0
49.9
7.2
8.7
26.6
-0.48
CZ5
24.8±5.6
31.4
32.9±0.0
4.3±0.1
50.2
7.3
5.0
33.2
-0.51
CZ6
43.0±1.0
21.0
35.9±0.5
4.7±0.1
52.0
7.8
3.4
32.2
-0.70
SD1
17.9±0.5
44.3
25.3±0.1
5.8±1.1
50.4
7.0
7.1
29.8
-0.41
SD2
13.7±0.1
58.0
15.0±0.3
4.9±0.1
51.9
7.2
9.3
26.7
-0.44
SD3
16.1±0.5
22.9
45.7±0.1
2.3±0.1
51.1
7.7
3.7
35.3
-0.58
SD4
41.3±0.3
11.4
35.6±0.1
2.0±0.0
56.9
8.7
1.8
30.6
-0.95
Cf
8.6±0.2
52.4
26.8±0.4
5.1±0.0
46.9
6.6
8.4
33.0
-0.18
Sp
10.3±0.4
64.7
12.3±0.3
10.6±0.1
47.5
6.6
10.4
24.9
-0.33
DCZ1
2.1±0.1
64.2
17.5±0.4
9.1±0.1
46.2
6.4
10.3
28.1
-0.18
DCZ2
0.8±0.6
55.8
27.0±0.5
13.1±0.1
43.4
6.7
8.9
27.9
-0.35
DCZ3
0.0±0.0
68.9
15.6±0.3
5.7±0.2
45.2
6.7
11.0
31.3
-0.10
DCZ4
3.7±1.3
62.6
10.1±0.3
8.9±0.1
43.7
6.1
10.0
31.2
-0.01
DCZ5
6.7±0.2
35.8
34.2±0.4
4.9±0.2
44.7
6.4
5.7
38.3
-0.10
DCZ6
3.3±0.3
27.8
43.7±0.6
4.9±0.2
42.7
6.1
4.4
41.8
0.01
DSD1
1.0±0.2
51.7
28.6±0.2
6.1±0.1
46.6
6.6
8.3
32.4
-0.20
DSD2
0.0±0.0
67.4
15.6±0.0
5.7±0.2
47.3
6.7
10.8
29.6
-0.17
DSD3
0.7±0.7
27.5
59.4±2.7
2.8±0.0
44.2
6.6
4.4
42.0
-0.10
DSD4
1.3±0.1
19.0
63.6±0.9
3.6±0.1
43.4
6.5
3.0
43.5
-0.11
DCf
0.0±0.0
56.0
30.0±0.3
5.8±0.1
45.1
6.4
9.0
33.7
-0.08
0.0±0.0 69.8 11.7±0.5 11.1±0.1 45.2 6.2 11.2 26.4 -0.12 DSp Measured results are reported as the mean of duplicate analysis with max/min (±) values. Unless otherwise indicated, all results are reported on dry weight basis (dw%). b CZ is Chlorella, SD is Scenedesmus, Cf is Chlorogloeopsis, Sp is Spirulina, the prefix “D” refers to corresponding defatted batches (e.g., DCZ represents a defatted Chlorella species). Microalgae sources and defatting protocol are described in Section S5. c Protein content calculated by multiplying nitrogen content by 6.25.1 d Ash content reported as the mean of triplicate analysis with standard deviations (±). e Errors for duplicate elemental analysis (72 h of extraction due to recalcitrant cell walls,20–23 and no further defatting efforts were made. Defatted microalgae were separated from solvents using glass fiber filters (Whatman®), and dried at 40 °C for 1 h before homogenized into powder. All feedstocks were stored at 4 °C prior to use. Feedstock moisture, ash, and elemental (CHN) contents were determined according to methods described previously.2 Lipid content was analyzed by the Folch method24 for most of the species, but extended sonication (30 min) and extraction (24 h) periods were used for CZ4 – 6 because of their recalcitrant cell walls. Protein content was estimated from nitrogen content using a conversion factor of 6.25.1 Carbohydrate content was measured by colorimetric assay with 3-methyl-2-benzothiazolinone hydrazone (MBTH), a method that quantifies >95% of the total carbohydrates, including fibrous carbohydrates.25,26 Apart from moisture content (calculated on total weight basis of the powdered feedstocks), all feedstock characterization results are reported on dry weight basis (dw%). Moisture and ash contents are reported as the mean of triplicate analysis with standard deviations (±), and the rest of results are reported as the mean of duplicate analysis with max/min (±) values.
HTL reaction and product analysis HTL reaction and product recovery were conducted according to methods as previously described.2 HTL product quantification and analysis also followed the same protocols as in literature,2 except that total nitrogen content (TN) in aqueous phase was measured using persulfate digestion method with Hach colorimetric assay,27 and aqueous ammonium content (NH4+) was measured using the phenate colorimetric method.28 Beyond mass yield, no further analysis of the gas co-product was conducted, and the gas co-product was assumed to be 100% CO2 based on past reports demonstrating it to be the predominant gas phase product (>87%) from HTL of microalgae feedstocks.3,29 All product results are on dry weight basis (dw%) and reported as the mean of duplicate analysis with max/min (±) values. Higher heating values (HHV, MJ kg-1) of feedstock and HTL biocrude product were estimated according to Dulong’s formula29 as: HHV = 0.338×C% + 1.428×(H% −
O% 8
)
(S3)
where C%, H%, and O% represent dw% of carbon, hydrogen, and volatile oxygen contents, respectively. Energy recovery (ER, %) of the biocrude product was calculated as: ER =
YBio ×HHVBio ×100% HHVFeed
(S4)
where YBio is the biocrude yield, and HHVBio/HHVFeed are HHV values for the biocrude and feedstock, respectively.
S16
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