Improvement of microalgae biomass productivity and subsequent biogas yield of hydrothermal gasification via optimization of illumination

Accepted Manuscript Improvement of microalgae biomass productivity and subsequent biogas yield of hydrothermal gasification via optimization of illumination Daniel Fozer, Bernadett Kiss, Laszlo Lorincz, Edit Szekely, Peter Mizsey, Aron Nemeth PII:

S0960-1481(18)31578-7

DOI:

https://doi.org/10.1016/j.renene.2018.12.122

Reference:

RENE 10997

To appear in:

Renewable Energy

Received Date: 10 February 2018 Revised Date:

30 December 2018

Accepted Date: 31 December 2018

Please cite this article as: Fozer D, Kiss B, Lorincz L, Szekely E, Mizsey P, Nemeth A, Improvement of microalgae biomass productivity and subsequent biogas yield of hydrothermal gasification via optimization of illumination, Renewable Energy (2019), doi: https://doi.org/10.1016/ j.renene.2018.12.122. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

ACCEPTED MANUSCRIPT

Improvement of microalgae biomass productivity and subsequent biogas yield of hydrothermal gasification via optimization of illumination Daniel Fozera,*, Bernadett Kissb, Laszlo Lorincza, Edit Szekelya, Peter Mizseya,c, Aron Nemethb,* a

RI PT

Department of Chemical and Environmental Process Engineering, Budapest University of Technology and Economics, Budapest, Hungary b

Department of Applied Biotechnology and Food Science, Budapest University of Technology and Economics, Budapest, Hungary c

Department of Fine Chemicals and Environmental Technology, University of Miskolc, Miskolc, Hungary

*

SC

Correspondig authors

Email address: [email protected] (Aron Nemeth), [email protected] (Daniel Fozer) Abstract

−2

−1

M AN U

This study examines the light factorial optimization of Chlorella vulgaris microalgae cultivation under different wavelengths and light intensities. RGB light-emitting diodes were applied on microtiter plate and lab scale stirred tank photobioreactors. One-way ANOVA and response surface methodology were adopted to investigate the effects on biomass productivity. The highest biomass productivity is found at

D

243.5 and 96.8 µmol photon m s in case of red and blue color intensities, respectively. Scaled-up fermentation in stirred tank photobioreactors shows that changing light intensity and aeration settings result in differing biomass productivity and composition. The effects of targeted cultivation are investigated on hydrothermal gasification (HTG) which is carried out in tubular reactor system at 550°C, 30.0 MPa and average 120 sec residence time. It is found that the fermentation of microalgae under optimized light factor levels results in higher H 2 yield compared to unoptimized light intensity levels. Throughout the HTG process high H 2 yield is achieved (4.38-9.34 mol kg

−1

) without using any

TE

catalyst, which indicates that the efficiency of downstream processing can be increased already at the cultivation stage.

EP

Keywords: Cultivation, Chlorella vulgaris, Light optimization, Hydrothermal gasification, Biogas production

1. Introduction

AC C

Microalgal biomass receives high attention due to its climate change mitigating potential reducing GHG emission, negative environmental and social impacts [1, 2]. It also offers various available application possibilities such as alternative energy source (e.g., biodiesel, bioethanol, biogas), pharmaceutical components, food and fertilizer [3, 4, 5, 6]. Microalgae could also play a key role in carbon capture and utilization by capturing and transforming the carbon dioxide content of flue gas into biofuels with high energy density and valuable platform molecules [7]. A recent study showed that in case of microalgae biorefineries energy gain is already achievable at current technological levels, however, the energy demand of both upstream and downstream technologies (e.g., cultivation, thermochemical conversion) needs to be further reduced in order to operate energy efficiently [8]. Suganya et al. [9] highlighted in their work that the main problems of microalgae based biorefineries are related to low product yield and biomass production at large scale, thus the improvement of microalgae cultivation and productivity is highly required. High biomass productivity is favoured for the scaled up thermochemical conversion routes (e.g., hydrothermal liquefaction, gasification) because it facilitates the energy and cost effective processing of microalgal biomass [10, 11, 12]. In case of photoautotrophic organisms one of the most important cultivation factor is related to the efficient illumination of the culture broth. Closed indoor systems require artificial light sources which expands the number of possible influencing factors on cultivation with light’s wavelength distribution and intensity. Light emitting diodes (LEDs) emerged recently as one of the most appropriate light sources for microalgae cultivation [13, 14, 15]. LEDs provide longer lifetime and better energy

ACCEPTED MANUSCRIPT efficiency compared to fluorescent lamps or tubs. Cultivation experiments under LEDs were successfully carried out in a number of works [16, 17, 18]. Several studies showed that different wavelength influence growth and photosynthetic metabolism in algae [19, 20, 21, 22].

RI PT

Photon flux density (PFD) and wavelength are important cultivation factors because they have high impact on photosynthesis: low light intensities can cause photolimitation, while excessive intensities lead to photoinhibition, therefore as a consequence economic microalgae cultivation demands optimized illumination conditions. In our work a microplate modul and lab scale stirred tank photobioreactors are implemented wherein the achievable biomass productivity of Chlorella vulgaris is examined.

D

M AN U

SC

Supercritical water gasification or hydrothermal gasification (HTG) is a thermochemical process where high temperature ( > 374°C) and pressure ( > 22.1MPa) are applied in order to convert wet biomass into H 2 , CH 4 , CO 2 and CO containing biogas [23]. This process has high significance because it does not require prior drying of biomass, moreover, the reaction conditions are far moderate compared to conventional gasification. Hydrothermal gasification of biomass were carried out on model compounds such as humic acid, cellulose, and horse manure [25, 26]. These studies apply batch type reactors with high residence time (around 40-75 minutes). A few studies deal with microalgae biomass where generally the main objectives are the evaluation of different strains and catalysts to raise yields and decrease high reaction temperature [27, 28, 29, 30, 31]. Jiao et al. [32] processed Chlorella pyrenoidosa, S.platensis, Schizochytrium limacinum and Nannochloropsis species where the highest hydrogen yield was found to be 6.17 mol kg −1 dry microalgae. Onwudili et al. [33] used Spirulina platensis and Saccharina latissima as feedstock for catalytic HTG, within their work the H 2 yield was 15.1 mol kg −1 working with NaOH homogeneous catalyst. SamieeZafarghandi et al. [34] investigated the cultivation effects on hydrothermal gasification, however the biogas yield (2.70 mmol g −1 ) and also the hydrogen mole fraction (9.42 mol%) were found to be too low.

EP

TE

In our study we examine the effects of wavelength and intensity on microalgae cultivation and indirectly on the yields of hydrothermal gasification. Changing light intensity result in different biomass productivity and biological composition. It is shown that the properties of subsequent downstream processes such as non-catalytic hydrothermal gasification can be improved with targeted and optimized cultivation of microalgae. The highest achieved hydrogen yield (9.34 mol kg −1 ) is comparable with studies where the gas yield is improved with various homogeneous and heterogeneous catalysts.

AC C

2. Data and Methods

2.1. Organism and Medium Chlorella vulgaris was obtained from the Mosonmagyaróvár Algal Culture Collection (MACC) (Széchenyi István University). The microalgae were cultivated in BG11 medium with the following composition (in g L −1 ) [35]: NaNO 3 , 1.500; K 2 HPO 4 , 0.040; MgSO 4 ⋅ 7 H2O, 0.075; CaCl 2 ⋅ 2 H 2 O, 0.036; Citric acid, 0.006; FeNH 4 SO 4 , 0.006; EDTANa 2 , 0.001; Na 2 CO 3 , 0.020 and 1.0 ml of A5 Solution [36].

2.2. Microtiter plate module set up, operation and monitoring In our investigation a 24-well microplate (Polystyrene GPPS microplate, Porvair®) was used with a well volume of 3.1 ml. The bottom of the well is transparent while the sidewalls are white, thus the illumination is provided at the bottom of the microplate with an RGB-panel without crossillumination between wells. The structure of the RGB-panel is presented in Fig. 1a. The RGB-panel consists of 24 RGB-LED to be coherent with the microplate structure, thus every cell can be irradiated with an RGB-LED. The intensity and wavelength can be controlled on potmeters and jumpers, respectively. In one row only one adjustment can be done, thus this configuration allows the examination of six

ACCEPTED MANUSCRIPT

RI PT

different light adjustment (wavelength and/or intensity) variation at the same time. The experiments are carried out with 16:8 hour light and dark periods, respectively. The total volume of the micro fermentation was 1.5 ml in each well whereof the volume of the inoculum was 0.1 ml ( ∼ 7% inoculation). The initial optical density was set to between 0.1-0.3. The mixing of the cells is carried out with a shaking incubator (Innova 40, New Brunswick Scientific®) at constant temperature (25°C) and speed (250 rpm) by placing the microplate module into the shaker and fixing it with Enzyscreen’s (NL) Clamp system. Fermentations took 14 days, where the biomass concentration has been monitored daily by optical density measurement. Figure 1: The structure of the RGB-panel and LED module. (a) The schematic figure of the RGBLED panel, (b) Monitoring biomass growth in microplate cells.

SC

The RGB color model has been used for the evaluation of the microplate fermentation. The transparent side of the microplate has been scanned with a scanner (Scanworks 60a, LG) to determine the exact value of the green color in each well which is directly correlated to the amount of the green pigments (e.g., chlorophyll), thus the green value can be converted to biomass concentration due to proper calibration. The green values of each cells were determined at 5 points (Adobe Photoshop CS6) as it is illustrated in Fig. 1b.

DW =

( A − B ) ⋅1000 ,

M AN U

The dry weight of microalgae has been determined by gravimetric method. A dilution line was prepared, then suspensions were filtered through 0.22 µm nitrocellulose membrane (MILLIPORE). The membrane and microalgae were dried at 103-105 °C in a drying cabinet for constant weight (approx. 2 hours), then they were cooled in a dessicator to room temperature. The dry weight was determined with the following equation (Eq. 1): (1)

SV

D

where DW is the dry weight (g L −1 ), A is the weight of the filter and the microalgae (g), B is the weight of filter (g) and SV is the sample volume (L).

TE

Calibration has been made in order to calculate dry weight content using the RGB color model. The relation between DW and green color code was given in Eq. 2:

DW = 0.0104 × ( 255 − Green ) − 1.556, R 2 = 0.9830, (2)

EP

where Green is the green color code ranges between 0 and 255 (-).

AC C

The RGB LED intensity was measured by a lux meter (IEC 6 LF 22, Cosilux Tungsram). The measured lx values are converted to µmol photon m −2 s −1 by the following equations [37] (Eq. 3- Eq. 5): ' I RED [ µ molphotonm −2 s −1 ] =

I RED [lx] 26.80

' I GREEN [ µ molphotonm −2 s −1 ] =

' I BLUE [ µ molphotonm −2 s −1 ] =

(3)

I GREEN [lx] 8.77

(4)

I BLUE [lx] (5) 61.70

The biomass productivity was calculated by Eq. 6:

P=

DWi − DW0 , ti − t 0

(6)

where P is the productivity (g L −1 d −1 ), DWi and DW 0 are the biomass concentrations at times ti and t0.

ACCEPTED MANUSCRIPT

2.3. Photobioreactor set up and operation

RI PT

Scaled-up laboratory fermentations are carried out in a stirred tank reactor with a working volume of 2 L. RGB-LED lighting platform is provided by UTEX Culture Collection of Algae at The University of Texas at Austin with the following colors: red (626 nm), green (525 nm) and blue (470 nm). The LED lights and their intensity is adjustable with a controller. The duration of illumination is controlled with a timer to provide 16:8 hours light and dark period, similarly to scaled-down fermentations. The aeration was provided by a B.Braun (Melsungen, Germany) control unit where the aeration was controlled with a rotameter, the air was filtered with a sterile filter (0.2 µm, PTFE, Sartorius Midisart 2000). Culture mixing was provided by magnetic stirrer (IKA) and a sparger was placed at the end of the inlet air manifold to enhance gas transfer.

SC

The fermentor and medium were sterilized in an autoclave (3870ELV, Tuttnauer) at 121°C for 20 minutes. The amount of the inoculum was 100-150 ml ( ∼ 7% inoculum) to achieve an initial optical density of 0.15. The reactor temperature was maintained at 25°C and the magnetic stirrer at 250 rpm. The fermentation was continuously followed by measuring optical density which was converted into dry weight based on prior calibration (Eq. 7).

M AN U

Optical density (OD) was measured at 560 nm with a spectrometer (Pharmacia LKB ⋅ Ultraspec Plus Spectrophotometer).

DW ( gL−1 ) = 0.4014 × OD560 − 0.1471, R 2 = 0.9834. (7)

TE

D

Elemental composition (CHN content) was determined by Liebig-, and Dumas combustion techniques with LECO FP-528 [38, 39]. Protein content was determined by multiplying the N-content with 6.25 [40]. Hexane:methanol (2:1) mixture was used for lipid extraction from microalgae biomass. The two phase extract was separated, the hexane phase was distilled with rotadest (SIMEX Laborota 4000), the residue is the lipid content of algae. The proximate analysis (volatile matter, fixed carbon and ash content) was performed according to the following standards of American Society for Testing Materials (ASTM): D3172 for fixed carbon, D3174 for ash and D3175 for volatile matter content by weighting residue after burning sample with DENKAL 1.4/1000 oven. The carbohydrate content of the biomass was determined with the following equation (Eq. 8):

wcarbohydrate = 100% − wprotein − wlipid − wash ,

(8)

EP

where wcarbohydrate , wprotein , wlipid and wash are the weight percent (wt%) of carbohydrate, protein, lipid and ash, respectively.

AC C

2.4. Hydrothermal gasification Fig. 2 shows the P&I diagram of hydrothermal gasification process. Microalgae broth was centrifuged (Hettich ROTINA 380) and resuspended to get 1.5 wt% suspensions. Tubular reactor (stainless steel 316, 1/8” OD, 2m length) was placed in an oven where K-type thermocouples were installed at the beginning and at the end of the reactor. The algae suspension and water stream were transferred with two HPLC pumps (Jasco PU-980, Gilson Model 303). At the inlet of the reactor the two streams were mixed in order to obtain the required temperature. The reactor design let the Reynolds number to be higher than 105 even at lowest feed flow rates, therefore turbulent flow was provided in every residence time settings. The gas and liquid products were separated after the reactor system in a phase separator, then the produced gas phase was collected in a calibrated gas burette where the produced volume of gas was determined and the gas phase could be sampled via a gas sampling attachment. Figure 2: The P&I diagram of microalgae cultivation and hydrothermal gasification The carbon gasification efficiency was determined by the following equation (Eq. 9):

GEC ( % ) =

mC , gas mC , feed − mTC ,liq

⋅100%,

(9)

ACCEPTED MANUSCRIPT where GEC ( % ) is the carbon gasification efficiency, mC , gas is the carbon content of biogas (g),

mC , feed is the carbon content of feed (g), mTC ,liq is the total carbon content of residue (g) which was measured with Shimadzu TOC-VCSH. The total gas yield was calculated based on Eq. 10:

nbiogas mbiomass

,

(10)

RI PT

Ybiogas ( molkg −1 ) =

where Ybiogas is the total yield of biogas (mol kg −1 ), nbiogas is the mole number of gas product (mol),

mbiomass is the mass of dried biomass (kg).

M AN U

SC

The gas products were analysed by HP5890II/TCD/FID gas chromatograph. The packed stainless steel column length was 1.9m, 1/8” OD, it is filled with 80/100 mesh Porapak Q. The initial temperature was set to 50°C for 0.5 min, the final temperature was 150°C for 2 min, which was attained with 20°C min −1 heating rate. The carrier gas was argon with a column head pressure of 150 kPa.

2.5. Experimental design and statistical analysis

Statistica 13.1 software was used for statistical and graphical evaluation of the experimental results.

2.5.1. Design and evaluation for the microplate

D

One-way ANOVA was used to determine the ideal illumination wavelength. 6 level settings were considered, namely the 3 basic RGB colors: red (626 nm), green (525 nm), blue (470 nm) and 3 mixed colors: yellow (626 nm & 525 nm), aquamarine (525 nm & 470 nm) and purple (626 nm & 470 nm).

TE

The central composite design was applied for intensity optimization. Two factors, namely the red LED intensity (X 1 ) and the blue LED intensity (X 2 ) with 5 levels were considered to be the independent variables, while the dependent variable was the biomass productivity, (Y 1 ). The applied polynomial quadratic response model was given by Eq. 11: k

k

EP

k

k

ˆ = β + β X + β X2 + Y 0 ∑ i i ∑ ii i ∑∑βij Xi βii X j + ε , i =1

i =1

(11)

i =1 j =1

AC C

ˆ is the predicted response variable (biomass productivity), Xi is the independent variable where Y (red and blue intensities), β 0 , β i , β ii , β ij are the regression coefficients and ε is the random error.

2.5.2. Design of experiment for the scaled-up photobioreactor and hydrothermal gasification A 2 2 factorial design was carried out where the factors were RGB LED intensity (Red: 178.9-256.9 µmol m −2 s −1 , Blue: 64.8-102.1 µmol m −2 s −1 ) and aeration rate (0.50-0.75 vvm).

3. Results and Discussion Chlorella vulgaris microalgae biomass was used for the fermentation and hydrothermal gasification experiments, where the proximate and elemental analysis of microalgae are presented in Table 1. Table 1: Proximate and elemental analysis of microalgae biomass Microalgae biomass

Proximate analysis (wt%)

Ultimate analysis (wt%)

Volatile matter Fixed carbon Ash Chlorella

75.36

21.53

C

N

H

O

3.10 59.77 10.07 4.36 25.80

ACCEPTED MANUSCRIPT vulgaris

3.1. Optimal wavelength

RI PT

The effect of different wavelength settings on biomass productivity is investigated using the MTP device. The dry weight content of the cells during cultivation time is presented in Fig. 3. The experimental results show that the biomass productivity and dry weight content can be significantly different under various wavelength regimes, as listed in Table 2. The highest dry weight attained with mixed colors (in order: purple > yellow > bluegreen > white), which is in agreement with the fact that the absorbent peaks of chlorophyll pigments are in the red and blue intervals, thus higher photosynthetic efficiency can be attained using more wavelength option for algae cultivation. Figure 3: Fermentations to determine optimal wavelength. Data are arithmetic means of 3 replicates

M AN U

SC

The ANOVA results are detailed in Table S1. The Fischer’s variance ratio was high for the model (F = 3.50) with low probability (p < 0.05) which indicates that wavelength is a significant factor. The highest productivity is attained with the purple irradiation, which is 49.9% higher than illuminating the culture with mixed white color. The lowest productivity associated with green color which makes this wavelength unsuitable to increase further the biomass productivity of Chlorella vulgaris. The same result was found with Nannochloropsis salina and Nannochloropsis oculata [17]. The results suggest that the mixed colors provide higher biomass productivity compared to monochromatic irradiation which is in agreement with Schulze et al. [41] who proposed the application of dichromatic LEDs in case of N.oculata and T.chuii. In our study the highest biomass productivity attained with dichromatic red and blue LEDs, however, de Mooij et al. [42] found the lowest productivity in case of Chlamydomonas reinhardtii with this combination, which suggests the strain specificity of optimal light condition.

D

Table 2: Biomass productivity under illumination by different wavelengths. Data are arithmetic means ( ± S.E.) of 3 different experiments. Red (626 nm)

Overall productivity (mgL −1 d −1 )

31.7( ± 2.5)

Green (525 nm)

Blue (470 nm)

Yellow (626 nm, 525 nm)

Purple (626 nm, 470 nm)

Bluegreen (525 nm, 470 nm)

White (626 nm, 525 nm, 470 nm)

29.86( ± 0.41)

33.4( ± 0.1)

41.2( ± 12.0)

60.4( ± 1.7)

50.0( ± 7.8)

40.3( ± 9.4)

AC C

EP

TE

Color (Wavelength)

3.2. The effect of light intensity 3.2.1. Statistical analysis The light intensity is playing key role attaining elevated biomass productivity. Central composite experimental design (CCD) was applied to determine the optimal light intensities on favorable wavelength levels in order to increase productivity. The 5-level-2-factor CCD is shown in Table 3 with intensity code levels and attained biomass productivities. According to the results the highest productivity is attainable at 243.47 and 96.76 µmol m −2 s −1 red and blue light intensities, respectively. The experimental data was analysed by the Response Surface Methodology, the estimated regression coefficients are listed in Table S2. Table S3 describes the ANOVA results for the fitted polynomial ˆ is affected most significantly by quadratic response surface model. The results showed that Y quadratic blue effect followed by the quadratic red and the cross effect between the two main effects. It is found that the Fischer’s variance ratio was high in case of the quadratic red and blue main effects (F=2768.28 and F=7041.13, respectively) with low probability (p=0.0121 and p=0.0076). The mutual interaction between red and blue intensities is also found to be significant (F=807.62, p=0.0224)

ACCEPTED MANUSCRIPT

RI PT

which means that the two factor’s combined effect can influence the photosynthetic apparatus and thus the biomass productivity. A test of lack-of-fit for the model provides a sensitive tool to test the model fitting. The lack of fit is found to be insignificant (F=30.66, p=0.132), thus it can be stated that the fitted model is appropriate. The R 2 is 0.9889, which means that the applied model fits well onto the experimental data and the 98.89% of the variability can be explained with it. Adequacy was further investigated on Fig. 4. On the normal probability plot the residues show normal distribution (Fig. 4a), on Fig. 4b the predicted and observed values indicate high accuracy. Raw residuals versus case number plot (Fig. 4c) do not show unique patterns, therefore the statistical model considered adequate. Table 3: Central composite design. Data are arithmetic means ( ± S.E.) of 3 different experiments. Real values of variables

SC

Independent variable levels

Run

X2

Red (626 nm) intensity (µmol m −2 s −1 )

Blue (470 nm) intensity (µmol m −2 s −1 )

1

0

0

243.47

96.76

2

1.41

0

312.32

3

1

-1

292.16

4

-1

-1

194.78

5

-1

1

194.78

6

0

-1.41

243.47

7

-1.41

0

174.60

8

0

1.41

9

0

0

10

1

1

M AN U

X1

Productivity (mg L −1 d −1 )

74.62( ± 9.66) 36.75( ± 5.97)

58.05

40.69( ± 1.72)

58.05

25.50( ± 1.16)

135.46

43.70( ± 4.59)

42.02

20.50( ± 2.41)

96.76

39.50( ± 2.75)

243.47

151.49

14.30( ± 1.30)

243.47

96.76

75.57( ± 1.93)

292.16

135.46

18.72( ± 4.37)

TE

D

96.76

EP

Figure 4: Testing adequacy of the fitted statistical model for investigation of light intensity. (a) Normal probability plot, (b) Predicted vs Observed values, (c) Raw residuals vs Case number plot.

3.2.2. Optimization of intensity and verification of experiments

AC C

The desirability function approach was applied for intensity optimization, where the objective was finding specific red and blue intensity levels which provide high biomass productivity. The response surface plot is presented in Fig. 5 which shows the effect of red and blue color LED intensities on microalgal productivity. The results indicate that the critical values (where the highest biomass productivity can be estimated) of the model are 241.34 and 95.97 µmol m −2 s −1 for red and blue intensities, respectively. In our experiments two extremum, namely the photolimitation and photoinhibition were also observed, these phenomena diminishing biomass productivity and thus highlight the importance of optimization. Figure 5: Productivity under different intensity levels, surface plot Following the optimization process further experiments were carried out for model verification. Random high, middle and low levels of each variables are selected to repeat measurements. Table S4 summarises the model based predicted biomass productivity and experimental results at adjusted red and blue intensity levels. The results show that the predicted and experimented productivities are not differing significantly, thus it can be seen that the fitted polynomial model was adequate and describes properly the biomass productivity in function of various light intensity levels (Fig. 6).

ACCEPTED MANUSCRIPT Figure 6: Predicted versus experimental values, model verification

3.3. Scaled up fermentation

Figure 7: Fermentations with the scaled up fermentors.

SC

RI PT

Fermentations are carried out in laboratory scale stirred tank photobioreactors in order to (i) justify the results of MTP optimization and to (ii) investigate the effects of increased biomass productivity and different illumination conditions on biogas yield of hydrothermal gasification. The light intensity and aeration rate are examined at two different levels. Fig. 7 demonstrates the dry weight content versus cultivation time in function of different fermentation conditions. The highest final dry weight was found to be 0.644 g L −1 at 256.88 and 102.10 µmol m −2 s −1 red and blue light intensity and 0.75 vvm aeration rate. Comparing the achieved biomass productivities to the MTP device, the same tendency is observed, that is, applying optimized intensity levels result in elevated biomass productivity. However, the highest biomass productivity was 59.82 mg L −1 d −1 , which is lower compared to the optimized MTP result. The difference can be explained by the differing geometrical design of the cultivation systems.

M AN U

The biomass productivities and the final biological compositions are presented in Table 4. It is found that the protein content of the biomass is almost the same at the end of the cultivation. Notable differences can be reported regarding carbohydrate and lipid content. The experimental results suggest that increased light intensity combined with lower aeration levels contribute to higher carbohydrate content while increasing aeration provides higher amount of lipids in the algal biomass. Table 4: Microalgae cultivation in photobioreactor and biological composition Intensity levels

Aeration (vvm)

Biomass productivity (mg L −1 d −1 )

Protein (wt%)

Carbohydrate (wt%)

Lipid (wt%)

Ash (wt%)

D

Run

Blue (470 nm) intensity (µmol m −2 s −1 )

1

256.88 (1)

102.10 (1)

0.50 (-1)

52.34

60.79

20.52

13.18

5.51

2

256.88 (1)

102.10 (1)

0.75 (1)

59.82

62.59

8.77

23.02

6.62

4

EP

AC C

3

TE

Red (626 nm) intensity (µmol m −2 s −1 )

178.90 (1)

64.82 (-1)

0.50 (-1)

25.64

65.31

17.70

9.60

7.38

178.90 (1)

64.82 (-1)

0.75 (1)

22.96

63.07

14.47

18.39

4.07

The cross effect between the factors can be shown in means plots (Fig. 8). Inspecting the data with bar charts, significant cross-over interaction revealed in case of biomass productivity and protein content of biomass (Fig. 8a-8b). An interaction can be expected in case of carbohydrate content (Fig. 8c), while investigating the lipid content, no interaction found between the examined factors (Fig. 8d). Figure 8: Detecting interaction between aeration and light intensity parameters in case of (a) biomass productivity, (b) protein, (c) carbohydrate and (d) lipid content. Intensity level -1: 178.90 (Red) & 64.82 (Blue) µmol m −2 s −1 ; 1: 256.88 (Red) & 102.10 (Blue) µmol m −2 s −1 . Aeration level 1: 0.50 vvm; 1: 0.75 vvm. Fig. 9 shows the specific component production of the biomass in function of investigated cultivation parameters. The radial graph demonstrates that the protein production is influenced mainly by light intensity while the carbohydrate and lipid production can be controlled by the rate of aeration.

ACCEPTED MANUSCRIPT Figure 9: Specific component production of biomass

3.4. Hydrothermal gasification

M AN U

SC

RI PT

Hydrothermal gasification of microalgae biomass was carried out in a tubular reactor at 550°C, 30.0 MPa for 120 sec. The results of the HTG process are summarized in Table 5. It is found that the biological composition of microalgae cells can influence the biogas quality that produced via the hydrothermal process. Throughout the cultivation step the lipid and carbohydrate content can be affected by changing light intensity and aeration levels. It turns out that higher methane yield can be reached by the hydrothermal treatment if the lipid content of the biomass increased during the upstream process. While in case of algae samples within the biological composition shifted through targeted cultivation towards higher carbohydrate content result in higher H 2 mole fraction. The yields of biogas components are presented in Fig. 10. The highest hydrogen yield was found to be 9.34 mol kg −1 which is a noticeable increasement compared to the lowest yield, which was only 4.38 mol kg −1 . This is more than a double increasement which is significant because it is achieved throughout the optimization of the cultivation parameters without using any catalyst during the HTG process. Previous studies reported hydrogen yields in the range between 5.97 to 15.10 mol kg −1 [32, 43], however these yields achieved using catalysts and not investigated the effects of the cultivation stage. In our experiment fermentations with increased biomass productivity are paired with high lipid content of microalgae biomass which contribute elevating methane yield (from 0.35 to 1.68 mol kg −1 ). Table 5: Hydrothermal gasification of microalgae biomass at 550°C and 30.0 MPa. Intensity levels

Blue intensity (µmol m −2 s −1 )

H2 (mol%)

CH 4 (mol%)

CO 2 (mol%)

CO (mol%)

GEC (%)

Y biogas

(mol kg −1 )

EP

TE

Red intensity (µmol m −2 s −1 )

Aeration (vvm)

D

Run

256.88 (1)

102.10 (1)

0.50 (-1)

37.50

1.41

8.70

52.40

67.56

24.91

2

256.88 (1)

102.10 (1)

0.75 (1)

23.78

7.59

7.51

60.77

69.60

22.07

3

178.90 (1)

64.82 (-1)

0.50 (-1)

28.23

1.75

7.82

62.20

85.87

24.24

64.82 (-1)

0.75 (1)

24.34

2.02

2.35

71.29

81.12

17.98

4

AC C

1

178.90 (1)

Figure 10: Yields of H 2 , CH 4 , CO 2 and CO biogas components at 550°C, 30.0 MPa and average

120 sec residence time. (a) 256.88(R) 102.10(B) µmol m −2 s −1 , 0.50 vvm; (b) 256.88(R) 102.10(B) µmol m −2 s −1 , 0.75 vvm; (c) 178.90(R) 64.82(B) µmol m −2 s −1 , 0.50 vvm; (d) 178.90(R) 64.82(B) µmol m −2 s −1 , 0.75 vvm. The specific gas yields are estimated based on achieved biomass productivities and total gas yields considering 200 h cultivation periods (Fig. 11). The results show that the final specific gas yield elevated via light intensity optimization, thus the cultivation parameters are determinant factors which should be considered not only in upstream technologies but in the downstream stage as well because the overall energy efficiency of a biorefinery can be upgraded further. Fig. 12 demonstrates the specific gas yield of biogas components where the same tendency can be drawn, that is, the different

ACCEPTED MANUSCRIPT cultivation settings can indirectly influence the obtainable amount of H 2 and CH 4 gases, therefore, targeted cultivation should be implemented to raise efficiencies.

RI PT

Figure 11: Comparing biomass productivity, total gas and total specific yield of hydrothermal gasification. (a) 256.88(R) 102.10(B) µmol m −2 s −1 , 0.50 vvm; (b) 256.88(R) 102.10(B) µmol m −2 s −1 , 0.75 vvm; (c) 178.90(R) 64.82(B) µmol m −2 s −1 , 0.50 vvm; (d) 178.90(R) 64.82(B) µmol m −2 s −1 , 0.75 vvm. Figure 12: Specific gas yield of biogas components. (a) 256.88(R) 102.10(B) µmol m −2 s −1 , 0.50 vvm; (b) 256.88(R) 102.10(B) µmol m −2 s −1 , 0.75 vvm; (c) 178.90(R) 64.82(B) µmol m −2 s −1 , 0.50 vvm; (d) 178.90(R) 64.82(B) µmol m −2 s −1 , 0.75 vvm.

SC

4. Conclusions

M AN U

In this study a microtiter plate (MTP) modul is presented and applied for screening purposes where the effects of light wavelength and intensity are evaluated based on one-way ANOVA, and response surface methodology. Light factors optimization are carried out in the MTP device and served as an input for scaled-up fermentations. Due to the proper optimization of wavelength and intensity factors biomass productivity is increased from 14.30 mg L −1 d −1 to 75.57 mg L −1 d −1 . The highest microalgal productivity attained at 243.5 and 96.8 µmol m −2 s −1 in case of red and blue LEDs, respectively. The increased biomass productivity is validated in the MTP and scaled-up systems as well.

D

Scaled-up fermentation in stirred tank photobioreactors shows that changing light intensity and aeration settings result in differing biomass productivity and composition. These parameters ultimately affect the overall efficiency of downstream processes. The effects are investigated on hydrothermal gasification (HTG) which is carried out in tubular reactor system at 550°C, 30.0 MPa and 120 sec residence time. Throughout the HTG process high H 2 yield is achieved (4.38-9.34 mol

TE

kg −1 ) without using any catalyst, which indicates that the efficiency of downstream processing of microalgae biomass can be increasing already at the cultivation stage.

EP

Due to the great number of microalgae species and influencing cultivation and processing parameters it is important to develop procedures that grant screening and evaluation of the effects of these specifications because it contributes to higher overall processing efficiencies and lower operating costs.

5. Acknowledgment

AC C

This work was supported by the New National Excellence Program (ÚNKP-17-3-I-BME-022) and the National Talent Program (NTP-NFTÖ-18-B-0154) of The Ministry of Human Capacities. We wish to thank Ian Ashdown from SunTracker Technologies Litd. for his support in providing information about light intensity’s conversion factors. The authors are grateful for the financial support from the Hungarian National Scientific Research Foundations (OTKA) projects: nr.: 112699 and nr.:128543. This research was supported by the European Union and the Hungarian State, co-financed by the European Regional Development Fund in the framework of the GINOP-2.3.4-15-2016-00004 project, aimed to promote the cooperation between the higher education and the industry.

Nomenclature A

Broth weight of filter and microalgae (g) ANOVA

B

Tare weight of filter (g) CCD

DW

Dry weight (g L −1 )

Analysis of variance Central Composite Design Carbon gasification efficiency (%)

ACCEPTED MANUSCRIPT GEC Hydrothermal gasification MTP

Microtiter plate

w ash

Weight percent of ash (wt%) w carbohydrate

Weight percent of carbohydrate (wt%)

w lipid

Weight percent of lipid (wt%) w protein

Weight percent of protein (wt%)

OD

Optical density (-) PBR

Photobioreactor

PFD

Photon Flux Density (µmol m −2 s −1 ) RGB

Red, Green, Blue

RSM

Response surface methodology SV

P

Biomass productivity (g L −1 d −1 ) Y biogas

SC

RI PT

HTG

Sample volume (ml)

M AN U

Total biogas yield (mol kg −1 )

References

[1] A. B. Avagyan, A contribution to global sustainable development: inclusion of microalgae and their biomass in production and bio cycles, Clean Technologies and Environmental Policy 10 (4)

D

(2008) 313–317, dx.doi.org/10.1007/s10098-008-0180-5.

TE

[2] A. H. Shimako, L. Tiruta-Barna, Y. Pign, E. Benetto, T. N. Gutirrez, P. Guiraud, A. Ahmadi, Environmental assessment of bioenergy production from microalgae based systems, Journal of

EP

Cleaner Production 139 (Supplement C) (2016) 51 – 60, dx.doi.org/10.1016/j.jclepro.2016.08.003. [3] S. Dickinson, M. Mientus, D. Frey, A. Amini-Hajibashi, S. Ozturk, F. Shaikh, D. Sengupta, M. M. El-Halwagi, A review of biodiesel production from microalgae, Clean Technologies and

AC C

Environmental Policy 19 (3) (2017) 637–668, dx.doi.org/10.1007/s10098-016-1309-6. [4] N. He, X. Sun, Y. Zhong, K. Sun, W. Liu, S. Duan, Removal and biodegradation of nonylphenol by four freshwater microalgae, International Journal of Environmental Research and Public Health 13 (12), (2016) dx.doi.org/10.3390/ijerph13121239. [5] M. Gong, A. Bassi, Carotenoids from microalgae: A review of recent developments, Biotechnology Advances 34 (8) (2016) 1396 – 1412, dx.doi.org/10.1016/j.biotechadv.2016.10.005. [6] D. L. Medeiros, E. A. Sales, A. Kiperstok, Energy production from microalgae biomass: carbon footprint and energy balance, Journal of Cleaner Production 96 (2015) 493 – 500, dx.doi.org/10.1016/j.jclepro.2014.07.038.

ACCEPTED MANUSCRIPT [7] I. L. Wiesberg, G. V. Brigago, J. L. de Medeiros, O. de Q. F. Arajo, Carbon dioxide utilization in a microalga-based biorefinery: Efficiency of carbon removal and economic performance under carbon taxation, Journal of Environmental Management, dx.doi.org/10.1016/j.jenvman.2017.03.005. [8] D. Fozer, N. Valentinyi, L. Racz, P. Mizsey, Evaluation of microalgae-based biorefinery

dx.doi.org/10.1007/s10098-016-1242-8.

RI PT

alternatives, Clean Technologies and Environmental Policy 19 (2) (2016) 501 – 515,

[9] T. Suganya, M. Varman, H. Masjuki, S. Renganathan, Macroalgae and microalgae as a potential source for commercial applications along with biofuels production: A biorefinery approach,

SC

Renewable and Sustainable Energy Reviews 55 (2016) 909 – 941, dx.doi.org/10.1016/j.rser.2015.11.026.

M AN U

[10] M. Zheng, L. C. Schideman, G. Tommaso, W.-T. Chen, Y. Zhou, K. Nair, W. Qian, Y. Zhang, K. Wang, Anaerobic digestion of wastewater generated from the hydrothermal liquefaction of Spirulina: Toxicity assessment and minimization, Energy Conversion and Management, dx.doi.org/10.1016/j.enconman.2016.10.034.

[11] C. Gai, Y. Zhang, W.-T. Chen, P. Zhang, Y. Dong, An investigation of reaction pathways of hydrothermal liquefaction using Chlorella pyrenoidosa and Spirulina platensis, Energy Conversion

D

and Management 96 (2015) 330 – 339, dx.doi.org/10.1016/j.enconman.2015.02.056.

TE

[12] D. Choi, J. Lee, Y. F. Tsang, K.-H. Kim, J. Rinklebe, E. E. Kwon, Modification of hydrothermal liquefaction products from arthrospira platensis by using carbon dioxide, Algal Research 24, Part A (2017) 148 – 153, dx.doi.org/10.1016/j.algal.2017.03.017.

EP

[13] S. Helena, M. Zainuri, J. Suprijanto, Microalgae Dunaliella salina (Teodoresco, 1905) Growth Using the LED Light (Light Limiting Dioda) and Different Media, Aquatic Procedia 7 (2016) 226 –

AC C

230, dx.doi.org/10.1016/j.aqpro.2016.07.031. [14] M. Atta, A. Idris, A. Bukhari, S. Wahidin, Intensity of blue LED light: A potential stimulus for biomass and lipid content in fresh water microalgae Chlorella vulgaris, Bioresource Technology 148 (2013) 373 – 378, dx.doi.org/10.1016/j.biortech.2013.08.162. [15] P. S. Schulze, R. Guerra, H. Pereira, L. M. Schler, J. C. Varela, Flashing leds for microalgal production, Trends in Biotechnology 35 (11) (2017) 1088 – 1101, dx.doi.org/10.1016/j.tibtech.2017.07.011. [16] J.-Y. Hu, T. Sato, A photobioreactor for microalgae cultivation with internal illumination considering flashing light effect and optimized light-source arrangement, Energy Conversion and Management 133 (2017) 558 – 565, dx.doi.org/10.1016/j.enconman.2016.11.008.

ACCEPTED MANUSCRIPT [17] C.-H. Ra, C.-H. Kang, J.-H. Jung, G.-T. Jeong, S.-K. Kim, Effects of light-emitting diodes (LEDs) on the accumulation of lipid content using a two-phase culture process with three microalgae, Bioresource Technology 212 (2016) 254 – 261, dx.doi.org/10.1016/j.biortech.2016.04.059. [18] A. Severes, S. Hegde, L. D’Souza, S. Hegde, Use of light emitting diodes (leds) for enhanced

RI PT

lipid production in micro-algae based biofuels, Journal of Photochemistry and Photobiology B: Biology 170 (Supplement C) (2017) 235 – 240, dx.doi.org/10.1016/j.jphotobiol.2017.04.023. [19] J. Aguilera, F. J., L. Gordillo, U. Karsten, F. L. Figueroa, F. X. Niell, Light quality effect on photosynthesis and efficiency of carbon assimilation in the red alga porphyra leucosticta, Journal of

SC

Plant Physiology 157 (1) (2000) 86 – 92, dx.doi.org/10.1016/S0176-1617(00)80140-6.

[20] T. T. Samudra, H. W. Nasution, W. Pratama, C. Pradhitaningrum, R. Alyza, E. A. Suyono, Effect

M AN U

of red light-blue and nitrogen starvation on growth of microalgae Tetraselmis sp., KnE Life Sciences 2 (1) (2015) 670–672, dx.doi.org/10.18502/kls.v2i1.245.

[21] R. L. Carneiro, M. E. V. dos Santos, A. B. F. Pacheco, S. M. F. d. O. e. Azevedo, Effects of light intensity and light quality on growth and circadian rhythm of saxitoxins production in Cylindrospermopsis raciborskii (Cyanobacteria), Journal of Plankton Research 31 (5) (2009) 481– 488, dx.doi.org/10.1093/plankt/fbp006.

D

[22] C. L. Teo, M. Atta, A. Bukhari, M. Taisir, A. M. Yusuf, A. Idris, Enhancing growth and lipid

TE

production of marine microalgae for biodiesel production via the use of different LED wavelengths, Bioresource Technology 162 (Supplement C) (2014) 38 – 44, dx.doi.org/10.1016/j.biortech.2014.03.113.

EP

[23] G. Kumar, S. Shobana, W.-H. Chen, Q.-V. Bach, S.-H. Kim, A. E. Atabani, J.-S. Chang, A review of thermochemical conversion of microalgal biomass for biofuels: chemistry and processes,

AC C

Green Chem. 19 (2017) 44–67, dx.doi.org/10.1039/C6GC01937D. [24] T. G. Madenolu, M. Salam, M. Yksel, L. Ballice, Hydrothermal gasification of biomass model compounds (cellulose and lignin alkali) and model mixtures, The Journal of Supercritical Fluids 115 (2016) 79 – 85, dx.doi.org/10.1016/j.supflu.2016.04.017. [25] S. Nanda, A. K. Dalai, I. Gkalp, J. A. Kozinski, Valorization of horse manure through catalytic supercritical water gasification, Waste Management 52 (2016) 147 – 158, dx.doi.org/10.1016/j.wasman.2016.03.049. [26] M. Gong, S. Nanda, M. J. Romero, W. Zhu, J. A. Kozinski, Subcritical and supercritical water gasification of humic acid as a model compound of humic substances in sewage sludge, The Journal of Supercritical Fluids 119 (2017) 130 – 138, dx.doi.org/10.1016/j.supflu.2016.08.018.

ACCEPTED MANUSCRIPT [27] I. Deniz, F. Vardar-Sukan, M. Yksel, M. Saglam, L. Ballice, O. Yesil-Celiktas, Hydrogen production from marine biomass by hydrothermal gasification, Energy Conversion and Management 96 (2015) 124 – 130, dx.doi.org/10.1016/j.enconman.2015.02.048. [28] Y. Graz, S. Bostyn, T. Richard, P. E. Bocanegra, E. de Bilbao, J. Poirier, I. Gokalp,

RI PT

Hydrothermal conversion of ulva macro algae in supercritical water, The Journal of Supercritical Fluids 107 (2016) 182 – 188, dx.doi.org/10.1016/j.supflu.2015.07.038.

[29] F. Safari, O. Norouzi, A. Tavasoli, Hydrothermal gasification of cladophora glomerata macroalgae over its hydrochar as a catalyst for hydrogen-rich gas production, Bioresource

SC

Technology 222 (2016) 232 – 241, dx.doi.org/10.1016/j.biortech.2016.09.082.

[30] R. Cherad, J. Onwudili, P. Biller, P. Williams, A. Ross, Hydrogen production from the catalytic

M AN U

supercritical water gasification of process water generated from hydrothermal liquefaction of microalgae, Fuel 166 (2016) 24 – 28, dx.doi.org/10.1016/j.fuel.2015.10.088. [31] O. Norouzi, F. Safari, S. Jafarian, A. Tavasoli, A. Karimi, Hydrothermal gasification performance of Enteromorpha intestinalis as an algal biomass for hydrogen-rich gas production using Ru promoted FeNi/-Al2O3 nanocatalysts, Energy Conversion and Management 141 (2017) 63 – 71, doi.org/10.1016/j.enconman.2016.04.083.

D

[32] J.-L. Jiao, F. Wang, P.-G. Duan, Y.-P. Xu, W.-H. Yan, Catalytic hydrothermal gasification of

TE

microalgae for producing hydrogen and methane-rich gas, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 39 (9) (2017) 851–860, doi.org/10.1080/15567036.2016.1270375.

EP

[33] J. A. Onwudili, A. R. Lea-Langton, A. B. Ross, P. T. Williams, Catalytic hydrothermal gasification of algae for hydrogen production: Composition of reaction products and potential for

AC C

nutrient recycling, Bioresource Technology 127 (2013) 72 – 80, dx.doi.org/10.1016/j.biortech.2012.10.020. [34] R. Samiee-Zafarghandi, J. Karimi-Sabet, M. A. Abdoli, A. Karbassi, Increasing microalgal carbohydrate content for hydrothermal gasification purposes, Renewable Energy 116 (2018) 710 – 719, dx.doi.org/10.1016/j.renene.2017.10.020. [35] CCAP, Culture collection of algae and protozoa, BG11 media, ccap.ac.uk/media/documents/BG11.pdf (accessed 10.02.18) (2017). [36] Sigma-Aldrich, A5 Solution, sigmaaldrich.com/catalog/product/sial/92949?lang=hu®ion=HU (accessed 10.02.18) (2018). [37] I. Ashdown, Photometry and Photosynthesis From Illuminance to PPFD, agi32.com/blog/2014/12/10/photometry-and-photosynthesis/ (accessed 10.02.18) (2014).

ACCEPTED MANUSCRIPT [38] M. C. Usselman, Liebig’s alkaloid analyses: the uncertain route from elemental content to molecular formulae, Ambix 50 (1) (2003) 71–89, dx.doi.org/10.1179/amb.2003.50.1.71. [39] U. M. Sainju, Determination of nitrogen balance in agroecosystems, MethodsX 4 (2017) 199 – 208, dx.doi.org/10.1016/j.mex.2017.06.001.

RI PT

[40] N. Sriperm, G. M. Pesti, P. B. Tillman, Evaluation of the fixed nitrogentoprotein (N:P)

conversion factor (6.25) versus ingredient specific N:P conversion factors in feedstuffs., Journal of the Science of Food and Agriculture 91 (2011) 1182–1186, dx.doi.org/10.1002/jsfa.4292.

[41] P. S. Schulze, H. G. Pereira, T. F. Santos, L. Schueler, R. Guerra, L. A. Barreira, J. A. Perales,

SC

J. C. Varela, Effect of light quality supplied by light emitting diodes (leds) on growth and biochemical profiles of nannochloropsis oculata and tetraselmis chuii, Algal Research 16 (2016) 387 – 398,

M AN U

dx.doi.org/10.1016/j.algal.2016.03.034.

[42] T. de Mooij, K. Schediwy, R. H. Wijffels, M. Janssen, Modeling the competition between antenna size mutant and wild type microalgae in outdoor mass culture, Journal of Biotechnology 240 (2016) 1 – 13, dx.doi.org/10.1016/j.jbiotec.2016.10.009.

[43] J. A. Onwudili, A. R. Lea-Langton, A. B. Ross, P. T. Williams, Catalytic hydrothermal gasification of algae for hydrogen production: Composition of reaction products and potential for

D

nutrient recycling, Bioresource Technology 127 (2013) 72 – 80,

AC C

EP

TE

dx.doi.org/10.1016/j.biortech.2012.10.020.

ACCEPTED MANUSCRIPT TITLE PAGE Title: Improvement of microalgae biomass productivity and subsequent biogas yield of hydrothermal gasification via optimization of illumination

RI PT

Name(s) of the author(s) with highest degree Daniel Fozera (MSc), Bernadett Kissb (MSc), Laszlo Lorincza (MSc), Edit Szekelya (PhD), Peter Mizseyac (DSc), Aron Nemethb (PhD) Word count (excluding abstract, references, tables and figures): 5400 Number of Tables: 5 Number of Figures: 12 Number of Photos: 0 Number of References: 43

M AN U

SC

Corresponding authors: Daniel Fozer and Aron Nemeth Postal address: Budafoki street 8., 1111-Budapest, Hungary Mobile number: +36 20 279 44 04 (Daniel Fozer), +36 1 463 1203 (Office, DF) Email address: [email protected] (Daniel Fozer), [email protected] (Aron Nemeth)

Affiliations of author(s): a Department of Chemical and Environmental Process Engineering, Budapest University of Technology and Economics, Budapest, Hungary b

c

TE D

Department of Applied Biotechnology and Food Science, Budapest University of Technology and Economics, Budapest, Hungary

AC C

EP

Department of Fine Chemicals and Environmental Technology, University of Miskolc, Miskolc, Hungary

ACCEPTED MANUSCRIPT

Yield (mol kg−1 )

25

RI PT

20 15 10

a

c

b

SC

5

d

M AN U

H2 CH4 CO2 CO

AC C

EP

TE D

Figure 10: Yields of H2 , CH4 , CO2 and CO biogas components at 550◦ C, 30.0 MPa and average 120 sec residence time. (a) 256.88(R) 102.10(B) µmol m−2 s−1 , 0.50 vvm; (b) 256.88(R) 102.10(B) µmol m−2 s−1 , 0.75 vvm; (c) 178.90(R) 64.82(B) µmol m−2 s−1 , 0.50 vvm; (d) 178.90(R) 64.82(B) µmol m−2 s−1 , 0.75 vvm.

1

ACCEPTED MANUSCRIPT

EP

TE D

M AN U

SC

RI PT

Biomass productivity can be significantly increased with optimization of light conditions. Throughout hydrothermal gasification high H2 yield is achieved (9.34 mol kg-1). Targeted cultivation can increase the gas yields of hydrothermal gasification.

AC C

• • •