Effect of low-temperature catalytic hydrothermal liquefaction of Spirulina platensis

Journal Pre-proof Effect of low-temperature catalytic hydrothermal liquefaction of Spirulina platensis Sabariswaran Kandasamy, Bo Zhang, Zhixia He, Haitao Chen, Huan Feng, Qian Wang, Bin Wang, Veeramuthu Ashokkumar, Subramanian Siva, Narayanamoorthy Bhuvanendran, M. Krishnamoorthi PII:

S0360-5442(19)31931-0

DOI:

https://doi.org/10.1016/j.energy.2019.116236

Reference:

EGY 116236

To appear in:

Energy

Received Date: 1 July 2019 Revised Date:

25 September 2019

Accepted Date: 26 September 2019

Please cite this article as: Kandasamy S, Zhang B, He Z, Chen H, Feng H, Wang Q, Wang B, Ashokkumar V, Siva S, Bhuvanendran N, Krishnamoorthi M, Effect of low-temperature catalytic hydrothermal liquefaction of Spirulina platensis, Energy (2019), doi: https://doi.org/10.1016/ j.energy.2019.116236. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. 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. © 2019 Published by Elsevier Ltd.

Effect of Low-temperature Catalytic Hydrothermal Liquefaction of Spirulina platensis Sabariswaran Kandasamya, Bo Zhanga, Zhixia Hea,b*, Haitao Chenb, Huan Fengb, Qian Wangb, Bin Wanga, Veeramuthu Ashokkumard, Subramanian Sivac, Narayanamoorthy Bhuvanendrana, M. Krishnamoorthia a

b

c

d

Institute for Energy Research, Jiangsu University, Zhenjiang 212013, China

School of Energy and Power Engineering, Jiangsu University, Zhenjiang 212013, China

School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, China

Center of Excellence on Petrochemical and Materials Technology (PETROMAT), Chulalongkorn University, Pathumwan, Bangkok 10330, Thailand

Corresponding author: Zhixia He, Institute of Energy Research, Jiangsu University, No 301, Xuefu Road, Zhenjiang City, Jiangsu Province, 212013, China. Tel:+86 13776476205, E-mail address: [email protected] (Zhixia He) Abstract In this work, the cerium oxide (CeO2) nanocatalyst was employed as a catalyst to enhance the hydrothermal liquefaction (HTL) of microalgae to bio-oil conversion. The HTL optimized parameters were obtained from response surface methodology (RSM). The Spirulina Platensis is blue-green algae were used to convert into bio-oil. The major processing method for bio-oil conversion was designed based on three key parameters, such as temperature, residence time and catalyst concentration. A remarkable enhancement of bio-oil production was observed for 0.20 g of CeO2 catalyzed HTL at 250
for 30 min, and around 26% of conversion was achieved which is higher than catalyst-free HTL reaction (16%). The synthetic CeO2 nanostructure was characterized using scanning electron microscopy (SEM), field emission scanning electron microscopy (FE-SEM), high-resolution transmission electron microscopy (HR-TEM), brunauer-emmettteller surface area (BET), X-ray powder diffraction (XRD) and thermal gravimetric analysis (TGA). The chemical composition of bio-oil was analyzed by gas chromatography-mass spectrometry (GC-MS) and the functional group analysis was done using fourier transform-infra red spectroscopy (FT-IR). The obtained results clearly reveal that the major chemical constituents such as hydrocarbons (7.55%), amino acids (36.69%) and nitrogen compounds (21.58%) for the bio-oil increased during CeO2 catalyzed HTL reaction . This investigation depicts that, the CeO2 nanoparticle could be employed as a potential candidate to accelerate the bio-oil conversion through HTL at low temperature from Spirulina platensis.

Keywords: Hydrothermal liquefaction; Spirulina platensis; Low temperature catalytic HTL; Response surface methodology; Central composite design; Energy recovery

1. Introduction In the present scenario, energy demand needs to neutralize in the worldwide as well as environmental pollution. Generally, the consumption of fossil fuels caused major pollutants worldwide. However, petroleum product costs are increasing year by year [1]. Researchers are finding an enormous amount of alternative sources for replacing fossil fuels. Among these sources of biomass, the microalgae are considered the potential candidate for biofuel production in the future aspect. Algae are an autotrophic organism using CO2 and sunlight. It is having high energy content and a feasible feedstock for conversion to biofuel and valueadded chemicals [2]. It has been reported to that biomass can be processed through pyrolysis, liquefaction, and gasification for the production of biofuels and other value-added chemicals. Nevertheless, the hydrothermal liquefaction is the main thermochemical technique to degrade lignocellulosic biomass into biofuels. Hydrothermal liquefaction exactly converts the wet biomass into biocrude (bio-oil) in hot compressed water. Therefore, the HTL operating temperature is about (250–550 °C) and pressure (5–25 MPa). During the hydrothermal process, decomposition was occurring and produced various compounds depending upon operating parameters [3]. Also, it can avoid the unnecessary pre-drying process that leads to low-cost value and also a very rapid conversion of proteins, carbohydrates, and lipids. The major chemical compounds considered are monoaromatics, fatty acids, alkane/alkene, polyaromatic compounds, nitrogen compounds, and other oxygenated compound [4]. In HTL technologies, generally employed with biomass and water and generate the bio-oil. Regardless, many of the works have reported on high bio-oil yield by co-liquefaction methods and pretreatment methods, and base catalysts were used to improve the bio-oil yield and also its essential compounds [5]. Moreover, the previous reports mostly dicussed the homogenous base catalysts such as acid, alkali, alkali salts in catalytic HTL of biomass. At present, the nanocatalyst found the significant bio-oil conversion in hydrothermal liquefaction process due to the high catalytic effect of lower temperature [6]. Furthermore, a higher abundance of hydrocarbons and polyaromatic compounds were found due to the presence of a catalyst and also decreased the oxygenated compounds in bio-oil with the presence of a catalyst [7]. Additionally, the whole quality and quantity of bio-oil were increased due to the addition of nanocatalyst. The nanocatalyst has higher activity, low energy consumption due to the high surface-volume ratio and the HTL reaction efficiency is greater than blank HTL reactions [8]. Wang et al. reported the Ni-Tm/TiO2 catalyst, when the liquefaction conversion increased up to 89.61% in fresh human feces and the maximum conversion rate was about 53.16% at the reaction temperature of 330 °C and a resident time 30 min [9]. Xu et al. revealed that the Ni-Ru/CeO2+H2 catalyst enhanced the bio-oil yield (27 wt%) at 450
, 60 min resident time in Nannochloropsis sp [10]. Moreover, Annamalai et al (2016) displayed that the cerium oxide enhanced the complete combustion quality and reduced the NOx emission of biodiesel.

Thus, the ceria has well known wide application and also very rare earth material [11]. The ceria (CeO2) demonstrated a high ability to store and release oxygen associated with the rich oxygen vacancies. It is stimulating to the activation of CeO bonds in organic oxygenates. The CeO2 catalysts may attain the maximum catalytic effects under mild condition [12]. In this study, CeO2 was chosen to investigate its reactivity during the low temperature HTL for bio-oil production. The properties of the bio-oil were analyzed through the FT-IR and GC-MS. While, the catalyst was characterized using scaning and XRD technologies. 2. Materials and methods 2.1. Materials The dry Spirulina platensis powder was used in this study. These fresh microalgae can be found anywhere in freshwater and marine. The whole chemical reagents used were ACS reagent grade. The elemental composition of the feedstock was carried out according to the literature [13]. Table 1 revealed the elemental composition of both blank and catalyst cases in 250
. 2.2. Nanocatalyst synthesis Cerium (III) nitrate hexahydrate (4.5 g), urea (3.6 g) and 0.6 g of polyvinylpyrrolidone were dissolved in 120 mL of distilled water. Then, it is carried out for ultrasonication and this cycle was repeated for several times until a clear the solution. After that, this solution was transferred into the hydrothermal autoclave reactor with Teflon chamber 250 mL capacity and kept in hot air oven for 6 hours at 90
. Then, it is cooled down to room temperature and kept for centrifugation for 10000 rpm/min for 10 minutes, and discarded the supernatant solution and collected the white precipitate. After this, the precipitate was sequential rinsing with distilled water and anhydrous ethanol for few times. The washed precipitate was kept at 60
for 4 hours to remove the moisture. Thus, the collected bio-oil yield was used in the HTL reactions [14]. The higher-heating value (HHV) of the blank and catalytic bio-oil was tested by an HKRL-4000 fully automation calorimeter. One gram of bio-oil sample was weighed and placed in a small crucible. Then, this sample was dried in a vacuum oven for 24 hours. The dried samples were placed in an oxygen bomb with 3 Mpa oxygen sealed condition for 15 seconds. The HHV of both samples was measured after setting the appropriate parameters. The HHV of samples were respectively calculated according to the Dulong equation [15]. HHV / (MJ/kg)=0.335[C]+1.423[H]-0.154[O]-0.145[N]

(1)

2.3. Hydrothermal liquefaction process Hydrothermal liquefaction converts the biomass to bio-oil with a temperature range 250
to 450
and pressure nearly 100-350 bar. The HTL process formed four product fractions such as aqueous, gas, solid residue and organic liquid [16]. Moreover, the HTL referred to as thermal depolymerization process and various types of biomass were adopted for HTL. The complete body of the HTL reactor was made up of 316 L stainless steel and the temperature was controlled by thermocouple inside the reactor and monitored in the outside digital meter. The pressure was controlled by pressure gauge which was located at the headspace of HTL equipment. The gas valve was anchored with heavy body clamp which was used to collect the gas phase after the HTL reaction. The lab-scale HTL equipment was the maximum feedstock loading up to 10 - 11 wt.% dry Spirulina platensis and sealed with six evenly distributed bolts. Consequently, the completion of the HTL reaction, the reaction produced the decomposed slurry was obtained and it contained only the three product fractions like organic liquid, aqueous and solid residue. The gas was released while the collection of the slurry phase and gases were analyzed based on the necessity. Table 1. Elemental analysis of Spirulina platensis bio-oil for blank and catalyst at 250


Biomass Spirulina platensis Blank Catalyst

Elemental content (wt% dry basis) HHV /

C

H

N

O

H/C

O/C

N/C

62.80 69.75

3.87

7.39

38.3

0.74

0.48

0.10

19.54

29.31

5.30

8.71

37.2

0.92

0.50

0.11

20.15

35.22

(MJ/kg)

Energy recovery rate (%)

2.4 Products separation procedure The dichloromethane (DCM) was used to filtrate the product slurry, using the Whatman filter paper (0.45 µm) and the filtered slurry was defined as “Solid residue”. Then this filtered solution was transferred to separating funnel for the separating of the aqueous phase and bio-oil phase. The top layer was the “aqueous phase” and the bottom was called “biocrude” or “Bio-oil”. The rotary evaporator was applied to recover the bio-oil from the solvent under 45
at 30 rpm speed. Finally, the yield of bio-oil was calculated in percentage according to Eq. (3) [17]. Reaction factors were obtained from the RSM software tool. Based on the variables matrix, the HTLs were performed and the final yields were obtained and defined as the response. The every single run was obtained different temperature, catalyst dosage and resident time and the total run was twenty. The RSM tool produced the ANOVA table, contour plot and 3D plots for the complete runs. Bio-oil yield = (weight of oil/weight of initial dry biomass) × 100

(2)

Energy recovery rate=HHVbio-oil×Ybio-oil/HHVbiomass×100%

(3)

2.5. Feedstock and products analysis

The chemical compositions of bio-oil were determined by the GC-MS (7890A-5975 C, Agilent, USA) with a slim part (30 m×0.25 mm×0.25 µm). Therefore, nearly 65 compounds have been identified. The chemical compounds were determined based on the National Institute of Standards and Technology mass spectral database. The bio-oil functional groups were determined by the FTIR spectrometer (Thermo Nicolet Nexus 4700 FT-IR Spectrometer, USA). Before the FTIR experiment, the KBr pellets were prepared and checked in the FTIR instrument. Then, the bio-oil liquid sample was mixed with kBr pellet in 2% weight and ground well with mortar and pestle. Then, the next step was transferred into the pressing tool to prepare a thin slice and then it was placed inside the instrument. The scanning wave number of IR spectra was in the range between 400-3500 cm−1. The resolution of the spectrometer was set at 0.1 cm−1. Consequently, the transmittances were recorded and compared the functional groups with standard reference. The elemental analysis were determined by the CHNS analyzer (Agilent technologies, model 7890A). 2.6. Design of experiments The Response Surface Methodology (RSM) is an empirical design, which helps to find out the multilevel factorials (quantitative information) by a regression method [18]. Furthermore, it can analyze the relationship between the input variables and output responses. The design matrix software version number 11 was used in this work. The input variables consisted of X1, X2, X3 and the output response (Y) through the regression analysis for the expected outcome response of input parameters [19]. Y= f(X1, X2, X3) + e

(4)

Where the ‘e’ represents the other sources of uncertainty and the ‘f’ represents the measurement error on the output response. The input optimization parameters such as temperature, reaction time and catalyst concentration were provided in the RSM. The maximum and minimum variables are required in the RSM tool to provide the multiple values of optimization parameters. The maximum and minimum values of 250 to 320
for the temperature, 30 – 60 minutes for the resident time and 0.15 to 1.25 g of catalyst concentration were given in the RSM multilevel factorial sheet. Moreover, the central composite design (CCD) can use the high number of experiments, more time and more materials. The CCD consists of 8 cubic points, 6 hub points and 1 center point with maximum 6 repeats for the center point. The center point was investigated by the trial and error with testing the lack of fit. The RSM showed, the maximum 20 runs were obtained after the run those parameters. According to the 20 runs, the HTL was performed and those results were again feed in the RSM response column (Bio-oil yield). After that, the analysis provided the analysis of variance (ANOVA) table, contour graphs and 3 dimension graphs. The ‘A, B, C’ in the figures denotes the temperature, catalyst and resident time, respectively [20].

Table 2. The central composite matrix and responses by Design Expert (version 11) software

Factor 1 Std Run A:Temperature
15 3 9 16 13 12 18 17 11 1 6 5 7 2 10 4 19 14 20 8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

280 250 250 280 280 280 280 280 280 250 320 250 250 320 320 320 280 280 280 320

Factor 2 B:Catalyst g

Factor 3 C:Resident time mins

Response 1 Bio-oil yield %

0.7 1.25 0.7 0.7 0.7 1.6 0.7 0.7 0.2 0.2 0.15 0.15 1.25 0.15 0.7 1.25 0.7 0.7 0.7 1.25

45 30 45 45 30 45 45 45 45 30 60 60 60 30 45 30 45 60 45 60

26 25 22 24 28 25 25 25 22 26 33 20 24 30 34 32 26 29 26 34

3. Results and discussion

Fig. 1. XRD patterns of CeO2 catalyst 3.1. Powder XRD analysis The structure and composition of CeO2 nanoparticles was explored by powder XRD. The figure 1 shows the powder XRD patterns of CeO2 nanoparticles. It clearly exhibits typical peaks corresponding to 2Ɵ values of 16.02, 20.40, 23.82, 26.29, 30.26, 33.68, 38.33, 42.033, 44.09, 46.55, 49.16, 52.86, 54.49, 56.69,

57.91 and 67.37 attributed to orthorhombic lattice of CeO2, which is close to the reported data (JCPDS file No. 41-13). The Crystallite size was 5.9 ± 0.3 nm was calculated by the Scherrer Equation. The sharpness of the peaks indicated the well crystallized nature of the catalyst [21]. 3.2. SEM, TEM & FE-SEM measurements The structural morphology of the catatlyst based on SEM and TEM images are shown in fig. 2. From the fig. 2 (a), the elongated spindles have the length of 4.36 µm, width of 0.52 µm and average particle size of 125.03 µm are observed. Figure 2 (b) shows the clusters, spherical, and agglomeration nano-sized ceria of the catalyst. In addition, from fig. 2 (c), the TEM images represented the high crystalline nature of the catalysts is clearly observed from the lattice fringes (0.192 nm) of the catalyst. In fig. 2(d), it demonstrates the clusters of nanoparticles, the surface of spindle structure and local electron diffraction revealed the ceria phase. The FE-SEM images (fig. 2 e, f, g & h) were showing the clear spindle like structure and agglomeration was also observed.. The formation of spindle morphology is strongly depends on the reaction mixture, concentration of the reactants and preparation conditions. The other factors like hydrophobic attraction, crystal field attraction, hydrogen bonding, vander waals forces, electrostatic and dipolar fields, intrinsic crystal contraction, and ostwald ripening as well influence the morphology of the sample [22].

a

c

b

d

e

f

g

h

Fig. 2. SEM (a & b), TEM (c & d) images of CeO2 catalyst and FE-SEM (e,f,g & h)

3.3. BET measurement The surface area and the pore size distribution of the prepared samples were evaluated by Brunauer, Emmett and Teller (BET) nitrogen gas adsorption–desorption isotherm and the respective pore size distribution analysis. Figure 3 illustrates the BET isotherm and the corresponding pore size distribution curves obtained for the CeO2. It can be seen that the isotherm plot closely matches with the typical IUPAC defined type IV curve. It has been well established that one of the characteristic features of the type IV isotherms is the formation of final saturation plateau of variable length with occasional appearance of reduced inflection points [23]. It is obvious in the present case the isotherm curve for CeO2 sample having a hysteresis loop over the range of 0.7–1.0 relative pressure could be attributed to type IV nitrogen isotherm. This suggests that the prepared CeO2 samples are having mesoporous structure. The slightly uncharacteristic upswing of plateau in the high-pressure zone could be attributed to the presence of wide range of mesopore and narrow macropores in the CeO2. Khalil et. al., reported a similar kind of BET type IV nitrogen isotherm curve for CuO nanoparticles and suggested that the occurrence of irregular distribution of mixed meso/macropores could result in slight deviation from the type IV isotherm which is typical of pure mesoporous materials [24]. Figure 3 depicts the pore size distribution obtained relative to the quantity desorbed clearly suggests the wider distribution at a diameter of about 7-10 nm and a saturated tail after 50 nm. This clearly indicates the presence of both the mesopores and macropores while the former being more pronounced. The estimated specific surface area using BET method for the CeO2 is found to be 4.66 m2 g−1 and the average pore diameter is ∼10 nm. The obtained value significantly deviates from the earlier report obtained for hydrothermally derived CeO2 nanoparticles [25]. However, the surface area of 4.66 m2 g−1 obtained for the

CeO2 sample is found to be in good agreement with the metal oxides such as CuO (A24 h–C4 h) powder catalyst and CuO micro-balls (4.8 m2 g−1) [25,26]. In an earlier study, Kurajica et. al., reported that the CeO2 catalyst with smaller specific surface area turned out to exhibit better catalytic activity, indicating that the present CeO2 samples could exhibit better catalytic activity [23].

Fig.3. The Nitrogen adsorption-desorption isotherms of CeO2

3.4. Response Surface Models 3.4.1. Effect of process parameters on products distribution The RSM tool was applied to design the multilevel factor and provided in the form of contour and 3D plots. The results demonstrated the first-degree terms of temperature and catalyst concentration were very significant (p < 0.01), the holding time was significant (p < 0.05), and quadratic terms of the three variables were very significant (p < 0.01). The Model F-value of 25.55 implies the model is significant. There is only a 0.01% chance that an F-value this large could occur due to noise. The Lack of Fit F-value of 3.29 implies the Lack of Fit is not significant relative to the pure error. There is an 11.19% chance that a Lack of Fit F-value this large could occur due to noise. Non-significant lack of fit is good. The temperature and catalyst were significant factors on the RSM. In the beginning, the product distribution mainly depends on the temperature. The bio-oil yield was increased with the rise of temperature from 250
to 320
. Nevertheless, the bio-oil conversion rate was decreased with increasing the temperature. The two possible reasons are the higher temperature improves the cracking and the degradation reactions and the higher decomposition attains to the maximum conversion of bio-oil yield were [27]. Moreover, the catalyst influence was higher in the low-

temperature reaction but not in higher temperatures due to the inactivation of the high amount of catalyst. When high temperature reaction, the catalyst may not perform well due to the enlargement of nano-sized particles and the reduced the higher surface-volume ratio [28]. The maximum yield was obtained at the temperature of about 320
, 1.25 g of catalyst and 60 minutes of resident time with a yield about 34%. Nevertheless, the blank reaction bio-oil yield was about 31% only and there is a less conversion obtained in the higher temperature condition due to the no catalyst effect. In the case of a low-temperature condition such as 250
, 0.25 g and 30 minutes of resident time favored a higher yield of 26%, whereas, the blank yield was about 18%. The significant HTL conversion rate was found in the lower parameter reaction at 250
of temperature, 0.20 g of catalyst and 30 minutes of resident time. Consequently, the optimum condition of bio-oil yield was obtained with a very low HTL temperature. As can be seen from the table, a similar trend is observed in the second run of the experiment, which ensures the higher amount of catalyst leads to the decline of the bio-oil yield due to the catalyst deactivation phenomenon [29]. 3.4.2. Statistical analysis Table. 3. Test of significance of the quadratic equation coefficient and ANOVA for reduced quadratic model Source

Sum of Squares

df

Mean Square

F-value

p-value

Model

0.0006 10

0.0001

25.55

A-Temperature

0.0002

1

0.0002

110.84

< 0.0001

B-Catalyst

0.0000

1

0.0000

12.50

0.0064

C-Resident time

0.0000

1

0.0000

8.31

0.0181

AB

3.856E-06

1

3.856E-06

1.78

0.2146

AC

0.0000

1

0.0000

20.88

0.0013

BC

0.0000

1

0.0000

6.41

0.0321

A²

6.252E-07

1

6.252E-07

0.2890

0.6039

B²

0.0000

1

0.0000

5.67

0.0411

C²

0.0001

1

0.0001

32.54

0.0003

ABC

0.0000

1

0.0000

5.39

0.0453

Residual

0.0000

9

2.163E-06

Lack of Fit

0.0000

4

3.527E-06

3.29

0.1119 not significant

Pure Error

5.362E-06

5

1.072E-06

Cor Total

< 0.0001 significant

0.0006 19

The ANOVA was performed and the results are shown in Table 3. The ANOVA results exhibited that the bio-oil yield variation was the measurable criticalness of the factors. The P-value was about under 0.0500, which indicates that the model is fit. Moreover, the “lack of fit” was 0.1119 (p > 0.05), suggesting that the model was not significant, which involves that the proposed model requires to fit the data well. Also,

the predicted R2 of 0.5414, which is not a match to the adjusted R² of 0.9402 as this expected one; i.e. the only difference is higher than 0.2. Thus, things to depictions are a model reduction, response transformation, and outlines. All experiment runs needed by confirmation test runs. The Adeq Precision value is greater than 4 is desirable and the ratio of 20.799, suggests an adequate signal. This model was moved to design space and the coefficient indicates the predictable change in response per unit change in factor value when all other factors are constant. 3.4.3. Optimization of process conditions using response surface plots The contour plot and 3 D surface diagrams illustrated the effects of catalyst vs. resident time (BC), temperature vs. resident time (AC), and temperature vs. catalyst (AB). To evaluate the interaction effect of two factors, each one plot, one variable was stable at this zero level while the rest of the two factors were changed over this whole operating RSM methodology. Also, the water and biomass were taken as a constant variable. The impact of catalyst and resident time on bio-oil yield is demonstrated in Fig. 4 (a) and (b). Nevertheless, the catalyst had a greater influence on biomass conversion. Contrarily, the resident time was not that much positive effect on the bio-oil conversion. Moreover, increasing the resident time in a low concentration of catalyst would lead to a decrease in the bio-oil yield. This was suggested that a huge amount of catalyst leads to the deactivation of further depolymerization processes due to the less catalytic reactivity [30]. In contrast, decreasing catalyst concentration with the decreased resident time (30 min) favored the bio-oil conversion with a yield of 26%.

a

b

Fig. 4. (a) Contour and (b) response surface plots of bio-oil yield as function of resident time and catalyst. The impact of temperature and resident time on the bio-oil conversion was shown in Fig. 5 (a) and (b). The temperature played a crucial role in the HTL and increasing the temperature with an increment of resident time was not able to provide significant conversion. Moreover, while diminishing the temperature with decreasing the resident time the significant conversion was obtained (250
). In contrast, the low temperature with a less concentration of catalyst (0.20 g) was attained the optimum bio-oil conversion. Whereas, compared to the blank the improvement of more than 8% was obtained. This phenomenon could be attributed to the different concentrations of the catalyst [31]. In parallel, to further corroboration this observation, test at 320
of temperature with 1.25 g of catalyst and 60 minutes was conducted and the conversion was about 34%. Furthermore, the blank bio-oil conversion was about 31% and has very less conversion (about 2%) obtained in the higher temperature reaction. The rising the temperature was in the low amount of catalyst leads to the increment of nanomaterial size and changed the properties of catalyst activity. [32]. Therefore, these contour plot and 3 D surface diagram findings show that no significant conversion was occurred in the higher temperature, whereas in lower temperature suggested the optimum bio-oil yield.

a

b

Fig.5. (a) Contour plot (a) and (b) response surface plot of bio-oil yield as function of temperature and resident time. Fig. 6 (a) and (b) shows the interactions of temperature and catalyst on bio-oil yield. As discussed earlier, the interaction between temperature and catalyst was similar to that showed in Fig. 4 (a) and (b). The bio-oil increased with decreasing temperature up to 250
when increasing the temperature and the bio-oil conversion declined gradually. Similar behaviors have been reported in the literature for the high temperature influences the maximum depolymerization rate and hydrolyzation of biomass molecules [33]. Also, when the temperature reached 320
there was no typical enhancement found in the conversion, contrarily, when decreasing the temperature to 250
the maximum conversion was found. Consequently, these 2 D and 3 D surface diagrams were suggested as the temperature displayed a great impact in this experiment. This might be due to the catalyst capability that could change the structure of biomass and help to emit the intracellular compounds such as carbohydrates and lipids and thus benefits the sequential liquefaction process towards bio-oil conversion [34].

a

b

Fig.6. (a) Contour plot and (b) response surface plot of bio-oil yield as function of catalyst and temperature.

3.4.4. Model confirmation The production of bio-oil was examined by using a residual plot method. As can be seen from fig.7, the normal plot illustrated that the points were clustering in the region of a straight line, therefore suggesting that the followed by a normal distribution. As a result, there was no major deviation observed from the straight line. Contrarily, the deeper residuals at the middle concealed that the data were distributed normally. Additionally, the bio-oil yield model could be potential predicting the actual values. There was no clear pattern obtained the residual versus predicted plot and results were shuffle distributed which can be affirmed the model adequacy.

A

B

Fig.7. (a) Normal plot of residual and (b) residual vs. predicted values plot of bio-oil yield model 3.5. GC-MS analysis The blank HTL (without catalytic bio-oil) and catalytic HTL (with catalyst) bio-oil were analyzed at 250
and the important compound was compared with the NIST library. The bio-oils are highly oxygenated and presence in the different product distributions [35]. Additionally, the bio-oil mainly consisted of hydrocarbons and N/O containing organic compounds and these outcomes might be due to the higher abundance of protein in Spirulina platensis. The fig.8 shows the predicted pathway of the HTL mechanism. The protein first catabolized and forms the amino acids with the presence of deamination process. Furthermore, the condensation and polymerization process result in intermediate compounds rearranged into other larger compounds [36]. Then, the lipid molecules further hydrolyzation and produce fatty acids and glycerol. In the HTL process, when the temperature was at the 100 – 200
it undergoes further decomposition and produces few compounds such as amino acids, fatty acids and sugars. Furthermore, the decarboxylation process leads to the presence of amino acids and it further produces a carboxyl group with the presence of carboxylic acids. Generally, the microalgae contain a large amount of the nitrogen due to the presence of ammonia from amine group. Moreover, the decarboxylation reaction might be occurred due to the high abundance of alkanes and

alkenes. Noticeably, when the temperature is above 200
it produces the aliphatic amine compounds [37]. Constantly, the long chain fatty acids could be reacted with alcohol and reduce the amino acids after the production of esters. Additionally, the Mailard reaction was a presence in the hydrolysis of the proteins and sugars and carbohydrates are hydrolyzes to produce some compounds such as pyrrole, thiazole, imidazole and their derivatives [38]. Nevertheless, few of the amino acids are leads to repolymerize to aromatic compounds. .

decarboxylation Alkanes

Alkenes Pyrrolidine derivatives of fatty

Amide hydrolysis Glycerol

Fatty acids Esters decarboxylation

Lipid

Carbon dioxide

Pyrazine Amine

hydrolysis

deamination

Amino acids

Protein

Pyrrolidinedione

Alcohols

Organic acid

Pyrrolidine

Indole

Ammonia

Indole derivatives

Carbon Oxygen Pyrrolidinone

hydrolysis

Quinoline

Pyridinol

Hydrogen

Degradation

R

Reducing sugar Carbohydrate Sugar

Cyclic oxygenates

Temperature (°°C) 0

Nitrogen

Phenol derivatives 100

200

250

Fig.8. Predicted pathway of HTL mechanism

300

320

Fig.9. GC/MS of the bio-oil at 250
for (a) blank: catalyst

Generally, the several compounds increased in the catalytic HTL. The higher abundance of alkanes (13.57%) and heterocyclic aromatic compounds (7.23%) were present in the catalyst reaction. This might be due to the C-C bond cleaving [39]. Moreover, the hydrocarbons (7.55%) increased in the presence of a catalyst. In contrast, a high abundance of oxygenated compounds (including alcohols, organic acids, esters, ketones and furans) was a presence in the blank run. Additionally, the nitrogenated compounds (including piperidines, indoles, pyridines, pyrazines, pyrrolidinones, amides, amines and nitriles) were increased (21.58%) with the presence of catalyst due to the higher decomposition degree of algal proteins. Also, monoaromatic compounds were increased (including, phenol, benzene, toluene, cholesterol, and vitamin E) with the presence of a catalyst. Likewise, acids (1.42%) were increased in the presence of catalyst. Remarkably, the higher abundance of amino acids (36.69 %) increased as the amount of catalyst increasing, which might be ascribed to the reaction between algal reducing sugars and amino acids [40]. Contrarily, the fatty acids (7.33%) were increased in the blank, as can be seen in fig.9.

Table 4. GC-MS major compounds in blank and catalyst at 250


RT(min)

Blank at 250 Peak (%)

Class of compound

13.2235

0.7413

25.3614

1.0254

29.7697

3.0379

37.7248

0.4548

31.847

8.484

32.1564

2.0351

32.4015

2.1989

18.5549 19.233 22.1662 24.6391 24.785 25.6244

0.6636 0.547 0.2244 0.3882 0.3973 0.3679

Heterocyclic aromatic compounds Pyrazine 3-ethyl-2,5dimethyl2(4H)-Benzofuranone, 5,6,7,7a-tetrahydro-4,4,7atrimethylThiophene, 2-methoxy-5methyl2-Oxo-3-methyl-cisperhydro-1,3-benzoxazine 2H-imidazole-2-thione, 1,3dihydro-4-(2-methylpropyl)2H-imidazole-2-thione, 1,3dihydro-4-(2-methylpropyl)2H-imidazole-2-thione, 1,3dihydro-4-(2-methylpropyl)Alkanes Hentriacontane Hentriacontane Hentriacontane Heptacosane Hentriacontane Hentriacontane

29.2194

1.6568

Heptadecane

29.4522 29.6343

1.0194 0.1701

30.3718

0.9606

Octacosane Hentriacontane Pentadecane, 2,6,10trimethyl-

31.339

0.3065

31.5642

RT(min)

Catalyst at 250 Peak (%)

Class of compound Alkanes

20.2566

0.6168

3-Ethyl-3-methylheptane

23.7202

0.7392

Hentriacontane

24.6446

0.7281

Pentadecane

27.0002

0.451

Dotriacontane, 1-iodo-

29.2259

1.6919

Heptadecane

29.4509

1.1654

Pentadecane, 2,6,10-trimethyl-

34.6669

0.3116

Hentriacontane

19.0091 24.4152 24.5545 25.629 28.9432 30.3809 24.9677

1.4482 0.6972 1.6575 0.4079 0.2311 0.5642 0.5091

33.5024

0.1149

11.0513

0.9853

Octane, 2-methylOctane, 2-methylHexadecane, 1-iodoNonane, 4,5-dimethylOctane, 2-methylUndecane, 2-methyl2,4-Dimethyl-7-chloroquinoline Quinoline-5,8-dione-6-ol, 7[[(4cyclohexylbutyl)amino]methyl]Nitrogen Compounds 1-Butylpyrrolidine

11.109

0.2974

Pyrrolidine, 2-hexyl-1-methyl-

Hentriacontane

21.6899

0.8025

0.6833

2-Decene, 3-methyl-, (Z)-

22.6644

0.7097

37.1343

0.3101

Pentadecane, 2,6,10trimethyl-

28.7803

0.3797

37.8157

0.7238

Hentriacontane

29.7705

3.0017

42.2316

0.5241

Hentriacontane

30.3085

0.2325

43.806 45.3207 19.006

2.7941 1.8334 1.1162

Pentacosane Eicosane Dodecane, 1-iodo-

31.2229 31.3491 31.5685

0.379 0.2766 0.7777

20.2451

0.4054

Octane, 2-methyl-

31.8409

11.7526

23.4824

0.55

Octadecane

32.3891

1.9885

24.3463

0.2776

Undecane, 3,9-dimethyl-

24.411

0.3585

Hexadecane, 3-methyl-

25.373

1.5128

26.9949

0.3571

Undecane, 2,10-dimethyl-

22.1655

0.4607

28.7776

0.5353

Octane, 2-methyl-

22.4792

0.3622

32.2791

2.9603

24.777

0.3219

20.3342

0.2046

28.0279

0.5116

2-Hexadecene, 3,7,11,15tetramethyl-, [R-[R*,R*(E)]]Nitrogen Compounds 2-Pyridinamine, N-acetyl-3nitro2-Chloro-N-ethylacetamide

25.8399

0.0788

40.9392

0.1991

Pyrrolidine, 1-(15-methyl-1oxohexadecyl)2H-Pyrimido[1,2-a]pyrimidine, 1,3,4,6,7,8-hexahydro2-Methyl-8-nitroisoxazolizidine 2,5-Piperazinedione, 3-methyl6-(1-methylethyl)1-Ethyl-6-hydroxy-4methylhexahydropyrimidin-2thione 2-Chloro-N-ethylacetamide 2-Butenediamide, (Z)Aziridine, 2-heptyl-3-methyl3,6-Diisopropylpiperazin-2,5dione Pyrimidine, 4,6-dihydroxy-5acetyl-1,2-dihydroOxygenated Compounds 2(4H)-Benzofuranone, 5,6,7,7atetrahydro-4,4,7a-trimethyl-, (R)1-Azetidinecarboxaldehyde, 2,2,4,4-tetramethyl1-Azetidinecarboxaldehyde, 2,2,4,4-tetramethylDichloroacetaldehyde 25.2207 1-Azetidinecarboxaldehyde, 2,2,4,4-tetramethylBenzaldehyde, 2-nitro-, diaminomethylidenhydrazone

3.6. FT-IR Characterization The FT-IR transmittance of Spirulina platensis is shown in Fig.10. There was no major difference was found in the FT-IR spectra between the blank and catalyst run, except a small vibrate at 2360 cm-1 indicates the (P=H2 stretch) phosphorus due to the impurities or possibly a synergistic interaction during the catalytic HTL process. Besides, the bands within 3600 – 3200 cm-1, indicating such as phenols, polymeric – OH and water impurities (3214 cm-1) [41]. The C-H bending stretching peak at (2860, 2923 cm-1) and these peaks in the bio-oil become more concentrated, indicating that the structure of algal biomass was depolymerized by HTL reactions. Furthermore, the strong absorbance at 1678 cm-1 indicates the presence of ketones, aldehydes, and esters. Similar behavior was also observed at the C-H stretching characteristics of alkanes. Finally, two distinct bands were observed in the region of 698, 743 cm-1 which reveals that the presence of mono, polycyclic and substituted aromatic rings [42]. The FTIR functional groups of the bio-oil for blank and catalyst 250
were shown in Table 5. Table 5. FTIR functional groups of the bio-oil for blank and catalyst 250
Number

Frequency range [cm-1]

Frequency [cm-1]

Group

Class of compound

1

3600-3200

3214

O-H stretching

Phenols, Polymeric O-H, water impurities

2 3 4

3050-2800 2505-2222 1750-1650

2860, 2923 2360 1678

5

1475-1330

1457

C-H stretching P-H2 stretch C=O stretching C-H deformation

6

900-675

698, 743

O-H bending

N-Alkanes Phosphorus Ketones, aldehydes, esters Alkanes Mono, Polycyclic, substituted aromatic rings

Fig.10. FT-IR spectra of Spirulina platensis bio-oil from hydrothermal liquefaction under blank and catalyst at 250
.

3.7. TGA analysis The thermal stability properties of CeO2 nanocatalyst were investigated using thermogravimetric analysis (TGA) as shown in Fig. 11. The results exhibited that the loss of catalyst weight percentage depended on a different temperature level. Initially, we observed a slight degradation of CeO2 at 30 °C to 250 °C due to the removal of moisture. Further, we observed increased up to 2% weight loss at 250-280 °C, due to the decomposition of the nanocatalyst surface and which significantly affect the catalytic activity. Moreover, the catalytic best performance was up to 250 °C. In our study revealed that low-temperature HTL was maximum conversion was obtained due to the mild weight loss between the 30 ºC to 250 ºC. It is evident that the influence of low temperature with the CeO2 catalyst presents better activity results [43].

Fig.11. TGA curve for CeO2 nanoparticle

3.8. Energy recovery rate of bio-oil The bio-oil energy recovery rate was calculated for both blank and catalyst runs at 250
and results are presented in Table 1. The energy recovery (ER) rate was increased with the addition of catalyst and the ER was found to be 35.22%, whereas 6% increased than the blank HTL reaction (29%). Furthermore, the elemental molar ratio of H/C and O/C increased in the catalytic HTL. Nevertheless, a little improvement in the molar ratio of N/C was also observed. Therefore, the significant increment of the energy recovery rate of catalytic HTL reaction indicates the efficiency of CeO2 and suggested that the utilization of catalyst is most effective in energy reduction and could result in a significant bio-oil conversion of Spirulina platensis through HTL reaction [44].

Table 6. Comparison of HTL bio-oil production Reaction Holding Bio-oil temperature time Reference yield (%) (°C) (min.)

Sl.No

Catalyst

Algal Species

1

Fe3O4

Ulva fasciata macroalgae

300

15

32

[45]

2

Co/CNTs

Dunaliella tertiolecta

320

30

40.25

[12]

3

No catalyst

Spirulina platnesis

300

50

35

[46]

4

No catalyst

Spirulina platnesis

340

30

34

[47]

5

1M Na2CO3 or formic acid

Spirulina platnesis

350

30

18

[48]

6

1M KOH

Spirulina platnesis

350

60

9.0

[49]

7

1M CH3COOH

Spirulina platnesis

350

60

19.5

[49]

8

Na2CO3

Spirulina platensi

350

60

21

[50]

9

CeO2

Spirulina platnesis

250

30

26

Present study

In this paper, we report for the first time the influence of the CeO2 catalyst with Spirulina platensis on HTL bio-oil yield. The Spirulina platensis and other algal species of catalytic HTL reports were present in Table.6. In this perspective, we have seen the earlier reports and compared the bio-oil yield. However, the higher temperature HTL bio-oil yield was better than the low-temperature and this was economically contradictory. The HTL reaction temperature of 350 °C led to the highest bio-oil yield of 32%. Whereas, the minor variance was only 6% between the low and high temperature HTL bio-oil yield and also the temperature difference was 100 °C. Remarkably, the 250 °C was about 26% of bio-oil production and this was comparatively greater than blank HTL (9%). As can be seen from the fig.11. explicitly showed that the slight loss of weight (2%) was observed at the above 250 °C. Besides, a huge amount of catalyst was also affected the catalytic activity. The two significant reasons would be considered the low-temperature catalytic HTL. The first, ceria was low-cost material and better efficiency in a low concentration and the second one was low-temperature HTL had a potential conversion, which these improvements would greatly reduce the energy consumption in HTL reaction.

4. Conclusion In the present study, the bio-oil was produced from hydrothermal liquefaction of Spirulina platensis by using ceria catalysts. The HTL parameters such as temperature, resident time and catalyst concentration were optimized by the RSM. The operating conditions were fitted well by RSM predictions. The optimal conditions were found to be 0.20 g of catalyst, 250
of temperature and 30 minutes of resident time and providing a bio-oil yield of about 26% which is more than 8% higher than the blank HTL reaction. Moreover, the GC-MS results revealed that the hydrocarbons, monoaromatic compounds, and organic acids were improved with the presence of a catalyst. Interestingly, whereas compared to other compounds, the most abundant of amino acids was observed in the catalytic reaction. The possible reason would be promoting the dehydrogenation process with the presence of the catalyst and the formation of amino acids was markedly enhanced. The FT-IR results showed that the peaks were similar with both blank and catalyst. Additionally, the elemental composition analysis revealed that the HHV and ER improved with the addition of a catalyst. Consequently, the lower temperature and lower catalyst of amounts were dominant in the yield of bio-oil. This will enhance the economically successful for the production of HTL bio-oil. Acknowledgement This research was supported by the National Natural Science Foundation of China (51876083, 51776088), a Project Funded by the Priority Academic Program Development of Jiangsu High Education Institutions and High-tech Research Key laboratory of Zhenjiang (SS2018002). References [1] Perera F. Pollution from fossil-fuel combustion is the leading environmental threat to global pediatric health and equity: Solutions exist. International journal of environmental research and public health. 2017;15(1):16. [2] Singh A, Nigam PS, Murphy JD. Renewable fuels from algae: an answer to debatable land based fuels. Bioresource technology. 2011;102(1):10-6. [3] Shuping Z, Yulong W, Mingde Y, Kaleem I, Chun L, Tong J. Production and characterization of bio-oil from hydrothermal liquefaction of microalgae Dunaliella tertiolecta cake. Energy. 2010;35(12):5406-11. [4] Gollakota A, Kishore N, Gu S. A review on hydrothermal liquefaction of biomass. Renewable and Sustainable Energy Reviews. 2018;81:1378-92. [5] Zhu Z, Toor SS, Rosendahl L, Yu D, Chen G. Influence of alkali catalyst on product yield and properties via hydrothermal liquefaction of barley straw. Energy. 2015;80:284-92. [6] Akia M, Yazdani F, Motaee E, Han D, Arandiyan H. A review on conversion of biomass to biofuel by nanocatalysts. Biofuel Research Journal. 2014;1(1):16-25.

[7] Jena U, Das K, Kastner J. Comparison of the effects of Na2CO3, Ca3 (PO4)2, and NiO catalysts on the thermochemical liquefaction of microalga Spirulina platensis. Applied Energy. 2012;98:368-75. [8] Biller P, Riley R, Ross A. Catalytic hydrothermal processing of microalgae: decomposition and upgrading of lipids. Bioresource technology. 2011;102(7):4841-8. [9] Wang W, Yang L, Yin Z, Kong S, Han W, Zhang J. Catalytic liquefaction of human feces over NiTm/TiO2 catalyst and the influence of operating conditions on products. Energy conversion and management. 2018;157:239-45. [10] Xu D, Guo S, Liu L, Hua H, Guo Y, Wang S, et al. Ni-Ru/CeO2 Catalytic Hydrothermal Upgrading of Water-Insoluble Biocrude from Algae Hydrothermal Liquefaction. BioMed research international. 2018; https://doi.org/10.1155/2018/8376127 [11] Annamalai M, Dhinesh B, Nanthagopal K, SivaramaKrishnan P, Lalvani JIJ, Parthasarathy M, et al. An assessment on performance, combustion and emission behavior of a diesel engine powered by ceria nanoparticle blended emulsified biofuel. Energy Conversion and Management. 2016;123:372-80. [12] Chen D, Ma Q, Wei L, Li N, Shen Q, Tian W, et al. Catalytic hydroliquefaction of rice straw for bio-oil production using Ni/CeO2 catalysts. Journal of analytical and applied pyrolysis. 2018;130:169-80. [13] Toor SS, Rosendahl L, Rudolf A. Hydrothermal liquefaction of biomass: a review of subcritical water technologies. Energy. 2011;36(5):2328-42. [14] Bakiz B, Guinneton F, Dallas J-P, Villain S, Gavarri J-R. From cerium oxycarbonate to nanostructured ceria: relations between synthesis, thermal process and morphologies. Journal of Crystal Growth. 2008;310(12):3055-61. [15] Volpe R, Messineo A, Millan M, Volpe M, Kandiyoti R. Assessment of olive wastes as energy source: pyrolysis, torrefaction and the key role of H loss in thermal breakdown. Energy. 2015;82:119-27. [16] Brand S, Hardi F, Kim J, Suh DJ. Effect of heating rate on biomass liquefaction: differences between subcritical water and supercritical ethanol. Energy. 2014;68:420-7. [17] Bok JP, Choi HS, Choi JW, Choi YS. Fast pyrolysis of Miscanthus sinensis in fluidized bed reactors: characteristics of product yields and biocrude oil quality. Energy. 2013;60:44-52. [18] Witek-Krowiak A, Chojnacka K, Podstawczyk D, Dawiec A, Pokomeda K. Application of response surface methodology and artificial neural network methods in modelling and optimization of biosorption process. Bioresource technology. 2014;160:150-60. [19] Rahimi-Gorji M, Pourmehran O, Hatami M, Ganji D. Statistical optimization of microchannel heat sink (MCHS) geometry cooled by different nanofluids using RSM analysis. The European Physical Journal Plus. 2015;130(2):22.

[20] Zhang F, Chan S-W, Spanier JE, Apak E, Jin Q, Robinson RD, et al. Cerium oxide nanoparticles: sizeselective formation and structure analysis. Applied physics letters. 2002;80(1):127-9. [21] Caruso RA, Antonietti M. Sol− gel nanocoating: an approach to the preparation of structured materials. Chemistry of materials. 2001;13(10):3272-82. [22] Gómez N, Banks S, Nowakowski D, Rosas J, Cara J, Sánchez M, et al. Effect of temperature on product performance of a high ash biomass during fast pyrolysis and its bio-oil storage evaluation. Fuel Processing Technology. 2018;172:97-105. [23] Kurajica S, Minga I, Guliš M, Mandić V, Simčić I. High surface area ceria nanoparticles via hydrothermal synthesis experiment design. Journal of Nanomaterials. 2016;2016. [24] Khalil A, Jouiad M, Khraisheh M, Hashaikeh R. Facile synthesis of copper oxide nanoparticles via electrospinning. Journal of Nanomaterials. 2014;2014:80. [25] Subalakshmi P, Ganesan M, Sivashanmugam A. Synthesis of 3D architecture CuO micro balls and nano hexagons and its electrochemical capacitive behavior. Materials & Design. 2017;119:104-12. [26] Sivakumar M, Sakthivel M, Chen S-M. One pot synthesis of CeO2 nanoparticles on a carbon surface for the practical determination of paracetamol content in real samples. RSC Advances. 2016;6(106):104227-34. [27] Valle B, Aramburu B, Olazar M, Bilbao J, Gayubo AG. Steam reforming of raw bio-oil over Ni/La2O3αAl2O3: Influence of temperature on product yields and catalyst deactivation. Fuel. 2018;216:463-74. [28] Yan C-F, Cheng F-F, Hu R-R. Hydrogen production from catalytic steam reforming of bio-oil aqueous fraction over Ni/CeO2–ZrO2 catalysts. International Journal of Hydrogen Energy. 2010;35(21):11693-9. [29] Galli P, Luciani L, Cecchin G. Advances in the polymerization of polyolefins with coordination catalysts. Die Angewandte Makromolekulare Chemie: Applied Macromolecular Chemistry and Physics. 1981;94(1):63-89. [30] Tian W, Liu R, Wang W, Yin Z, Yi X. Effect of operating conditions on hydrothermal liquefaction of Spirulina over Ni/TiO2 catalyst. Bioresource technology. 2018;263:569-75. [31] da Silva AG, Batalha DC, Rodrigues TS, Candido EG, Luz SC, de Freitas IC, et al. Sub-15 nm CeO2 nanowires as an efficient non-noble metal catalyst in the room-temperature oxidation of aniline. Catalysis Science & Technology. 2018;8(7):1828-39. [32] Zou S, Wu Y, Yang M, Li C, Tong J. Bio-oil production from sub-and supercritical water liquefaction of microalgae Dunaliella tertiolecta and related properties. Energy & Environmental Science. 2010;3(8):1073-8. [33] Marin-Batista J, Villamil J, Rodriguez J, Mohedano A, de la Rubia M. Valorization of microalgal biomass by hydrothermal carbonization and anaerobic digestion. Bioresource technology. 2019;274:395-402. [34] Demiral I, Şensöz S. The effects of different catalysts on the pyrolysis of industrial wastes (olive and hazelnut bagasse). Bioresource technology. 2008;99(17):8002-7.

[35] Sassano CEN, Gioielli LA, Ferreira L, Rodrigues M, Sato S, Converti A, et al. Evaluation of the composition of continuously-cultivated Arthrospira (Spirulina) platensis using ammonium chloride as nitrogen source. Biomass and Bioenergy. 2010;34(12):1732-8. [36] Zhang B, Chen J, He Z, Chen H, Kandasamy S. Hydrothermal liquefaction of fresh lemon-peel: Parameter optimisation and product chemistry. Renewable Energy. 2019;143:512-9. [37] Yang J, Corscadden K, Niu H, Lin J, Astatkie T. Advanced models for the prediction of product yield in hydrothermal liquefaction via a mixture design of biomass model components coupled with process variables. Applied Energy. 2019;233:906-15. [38] Xiao H, Zhai Y, Xie J, Wang T, Wang B, Li S, et al. Speciation and transformation of nitrogen for spirulina hydrothermal carbonization. Bioresource technology. 2019;286:121385. [39] Tu R, Sun Y, Wu Y, Fan X, Wang J, Shen X, et al. Effect of surfactant on hydrothermal carbonization of coconut shell. Bioresource Technology. 2019. [40] Watson J, Lu J, de Souza R, Si B, Zhang Y, Liu Z. Effects of the extraction solvents in hydrothermal liquefaction processes: Biocrude oil quality and energy conversion efficiency. Energy. 2019;167:189-97. [41] Yang W, Li X, Li Z, Tong C, Feng L. Understanding low-lipid algae hydrothermal liquefaction characteristics and pathways through hydrothermal liquefaction of algal major components: crude polysaccharides, crude proteins and their binary mixtures. Bioresource technology. 2015;196:99-108. [42] Chen H, He Z, Zhang B, Feng H, Kandasamy S, Wang B. Effects of the aqueous phase recycling on biooil yield in hydrothermal liquefaction of Spirulina Platensis, α-cellulose, and lignin. Energy. 2019;179:1103-13. [43] Tok AIY, Boey FYC, Dong Z, Sun X. Hydrothermal synthesis of CeO2 nano-particles. Journal of materials processing technology. 2007;190(1-3):217-22. [44] Saber M, Golzary A, Hosseinpour M, Takahashi F, Yoshikawa K. Catalytic hydrothermal liquefaction of microalgae using nanocatalyst. Applied energy. 2016; 183:566-76. [45] Rojas-Pérez A, Diaz-Diestra D, Frias-Flores CB, Beltran-Huarac J, Das K, Weiner BR, et al. Catalytic effect of ultrananocrystalline Fe3O4 on algal bio-crude production via HTL process. Nanoscale. 2015;7(42):17664-71. [46] Feng H, Zhang B, He Z, Wang S, Salih O, Wang Q. Study on co-liquefaction of Spirulina and Spartina alterniflora in ethanol-water co-solvent for bio-oil. Energy. 2018;155:1093-101. [47] Feng H, He Z, Zhang B, Chen H, Wang Q, Kandasamy S. Synergistic bio-oil production from hydrothermal co-liquefaction of Spirulina platensis and α-Cellulose. Energy. 2019;174:1283-91. [48] Biller P, Ross A. Potential yields and properties of oil from the hydrothermal liquefaction of microalgae with different biochemical content. Bioresource technology. 2011;102(1):215-25.

[49] Ross A, Biller P, Kubacki M, Li H, Lea-Langton A, Jones J. Hydrothermal processing of microalgae using alkali and organic acids. Fuel. 2010;89(9):2234-43. [50] Xu D, Lin G, Guo S, Wang S, Guo Y, Jing Z. Catalytic hydrothermal liquefaction of algae and upgrading of biocrude: A critical review. Renewable and Sustainable Energy Reviews. 2018;97:103-18.