Catalytic of reaction parameters using response surface

Catalytic hydrothermal liquefaction of microalgae over Fe3O4 catalyst for the production of bio-oil: Optimization of reaction parameters using response surface methodology
Abstract
In this study, we summarized about the hydrothermal liquefaction of Spirulina platensis with the Fe3O4 nano catalyst for enhancing the bio-oil yield and quality by using response surface methodology. Our aim is to evaluate the interaction of different factors on the bio-oil production through HTL using microalgae that contains relatively low lipid content and high protein. Optimization of three key parameters concentration, reaction temperature, holding time and concentration of catalyst was carried out by response surface methodology (RSM). In this work, we used central composite design to conduct the experiment process. Graphical response surface and contour plots were used to locate the optimum point. The final results showed that the optimum concentration, temperature, and holding time were 0.45g of nano catalyst 272°C, and 24 min, respectively. The maximum bio-oil yield was obtained was 28% where as the blank was 20 %. In higher temperature, the maximum yield was obtained at 320 ? about 32.33 %. However, there is no significant difference between blank and catalyst in higher temperature. Interestingly, we observed the great influenced on nanocatalyst in lower temperature.
Introduction
It is assessed that by 2050 the total population will exceed 9 billion of people from the recent 7 billion one. This population development enhances demand, among other human needs, for energy, food, reused supplements and in addition water, which is predictable to augmentation by 50 % or more by the year 2030. The transportation division represents 21% of the current worldwide non-renewable energy source CO2 emissions to the climate, second now to emissions from power production. Bioenergy has been superficial as an enormous section in numerous future energy situations. At present, a few developed nations such as Brazil, the United States, Germany, Australia, Italy, and Austria are as of now utilizing biofuels, for example, bioethanol and biodiesel. It is common that this outline will proceed to develop and more nations will utilize biofuels. Worldwide biofuel production has tripled from 4.8 billion gallons in 2000 to around 16 billion in 2007, yet, accounts fall below 3% for the worldwide transportation fuel supply, as illustrated by US Department of Agriculture reports. In this manner, the search for ‘clean’ energy has turned out to be one of most overwhelming difficulties. Following this, few alternative sources of energy including solar oriented energy, hydroelectric, geothermal, wind, and biofuels are being considered and actualized. Of these potential sources of energy, biofuels are viewed as authentic methods for accomplishing the objective of surrogate non-renewable energy sources in here and now. From these aspects, researchers were searching the alternative crop for biofuel. One other option to the conventional crop is algae 2.
Microalgae are single-celled organisms that consist of both prokaryotic and eukaryotic cells and are accountable for almost 50% of global carbon fixation. Their single cell structure pertains them to simply change sunlight into chemical energy. In present years, look into on utilizing biomass for liquid fuels has been vigorous, going from investigations of pyrolysis and aqueous liquefaction (HTL) of lignocellulose material, gasification, and biomass to fluid innovations to the redesigning forms. Furthermore, conversion of biomass from its common solid form to liquid fuels isn’t an unconstrained procedure. The fluid energizes that people have burdened on a huge scale as non-renewable energy source took a numerous of years of geochemical process for conversion of biomass to crude oil and gas.
Biomass conversion technology innovations are extensively characterized into two classes in specifically, biochemical and thermoconversion. In most cases, the unprocessed bio-oil got from the biomass has low energy density, high moisture substance, and its physical form isn’t free flow that makes an issue as a feedstock for slagging engines. Thermochemical changes compared with biochemical conversions are prepared at several higher degrees of temperature in the presence of a suitable catalyst to get liquid products from various sources. Thermochemical conversions are much fast than the biochemical conversion. Thermochemical conversion, for the most part, suggested revamping biomass by heating under pressurized and oxygen denied fenced in area. It can be additionally characterized by burning, gasification, pyrolysis, and direct liquefaction. As such, hydrothermal liquefaction of biomass is the thermo chemical conversion of biomass into fluid fills by handling in a hot, pressurized water condition for adequate time to separate the solid polymeric structure to for the most part fluid components. Run of the hydrothermal liquefaction system conditions is 523– 647 K of temperature and working pressure from 4 to 22 MPa. The low working temperature, high energy productivity and low tar yield compared with pyrolysis are the key parameters that drive the consideration of researchers on the liquefaction procedure 6.
2. Materials and methods
2.1. Materials
Spirulina platensis is microalgae type and typically found in freshwater and marine systems. The Spirulina platensis powder was firstly dried in oven at 104 °C for 24 h, and then homogenized by passing a 100 ?m mesh. All chemical reagents used in experiments were analytical grade.

2.2 Nanocatalyst Preparation
Iron (II) sulfate heptahydrate and iron (II) chloride were dissolved at a molar ratio of 1:2 in 200 mL deionized water and stirred vigorously at 30 ? for 30 min, while stirring ammonium hydroxide (NH4OH) (25% in water v/v) was added drop wise to the mixture. The black precipitate Fe3O4 emerged was heated at 85 ? and isolated using a magnetic bar. These particles were then sequentially washed with deionized water and ethanol. The MNPs dried in hot air oven at 70 ? were preserved in a desiccator for further use (Thangaraj et al., 2016)
2.4. Hydrothermal liquefaction process
The four product fractions that are formed during HTL process are organic liquid, aqueous phase fraction, gas and solids. The HTL experiments were carried out in the liquefaction system, which comprised of an autoclave reactor and some auxiliary equipment. The whole body of autoclave was made of 316L stainless steel which ensures the maximum pressure of 40 MPa and temperature of 400 °C, also high corrosion resistance. Temperature was precisely controlled by the controller with a thermocouple inside the reactor. In a typical run, autoclave reactor filled with 33 mL slurry containing of 10–11 wt% dry microalgae (with or without pre-treatment) was tightly sealed with six evenly distributed bolts. Then the reactor headspace was purged with pure nitrogen at 50 mL/min for 5 min. As the reactor cooled down, the pressures and temperatures inside the reactor were recorded. The gas was vented and reaction mixture was collected carefully, and the reactor was rinsed with DCM for at least three times. The rinse solution and another 50 ml DCM were added into the reaction mixture. All of the mixtures were filtered with microporous membranes (0.45 ?m) and the filter residue after drying was defined as the solid residue. Then the filtrate with two phases was separated in a separating funnel. The water-soluble portion was defined as the aqueous products. The DCM soluble portion was defined as the biocrude, and was measured after filtration and evaporation of DCM. As per the central composite design parameters fixed the minimum and maximum value was set. The parameters such as temperature (260 – 320?), time (15 – 60 minutes) and catalyst concentration (0.25 – 1.25) were set respectively. After set these values, the RSM software given the 20 runs and each runs were consist of different temperature, time and catalyst concentration. According to the values, the HTL was performed by every run as mentioned in the table ( ). Then, bio-oil yield was calculated as per the equation ( ). Finally, the 2D graphical and contour plot was performed by RSM software.
2.5 Products separation procedure
After the HTL process, the reactor was cooled down at room temperature and placed with tab water for few minutes. Once cooled down the reactor, the gas was released and opened the reactor and collected the liquid phase. The liquid phase was mixed with equal proportion of DCM and extracted by Whatman No 1# filter paper paper with the help of vacuum pump connected with condensation chamber. Then the liquid phase was separated by departing funnel. The two phases were appeared like water phase and aqueous phase. The solid residue was obtained in filter. Then, It was keep it for 104 °C oven dried for 12 h the solid was denoted as “Solid residue”. The rotary evaporator operated at 45 °C was used to recover the “Biocrude” and DCM from the DCM-soluble mixture.
Biocrude oil yield = (weight of oil/weight of initial dry biomass) × 100
Design of experiments
Experimental design was performed using Response Surface Methodology (RSM). RSM is a statistical method for modeling and analysis of a problem using quantitative data from experiments to determine model equations by regression. This method optimizes the responses to variations of process parameters 18, 19. The Central Composite Design (CCD) is one of the most popular RSM designs useful for building second order (quadratic) and third order (cubic) models for the response variables where Y is the response, b0 is the constant coefficient, bi, bii and bij are the linear, quadratic and interaction coefficients, and Xi, Xj are the coded values of the independent variables, respectively 19. A general form of the quadratic equation can be expressed as following.

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In the present work, a standard CCD design with three variables was applied in order to study the effects of three independent variables temperature, time and catalyst concentration.
The design contains 8 cubic points, 6 axial points, and 1 center point with 6 replicates for the center point. Thus a total of 20 experiments were performed. The center point replicates were chosen as a measure of precision. The variables levels were in the range of 260–320 °C for temperature, 15– 60 min for reaction time, and 0.25–1.25 g for catalyst concentration. The factors and levels are presented in Table 2. For statistical calculations, the variable xi was coded to Xi according to the following relationship:

Where Hi is the un-coded high level and Lo is the un-coded low level of a specific variable. The design matrix was analyzed using Design Expert (version 8) software and the optimization was performed to maximize the bio-oil yield.

2.5. Feedstock and products analysis
Element distribution of the feedstock and oils were obtained by using a CHN analyzer (EA-1112, Italy). Heating values were measured in an isoperibolic bomb calorimeter
(KDHW-800A, China) by taking 0.3–0.5 g sample. Proximate analysis was performed in thermogravimetric analysis (NETZSCH STA449C, Germany) by taking 10 mg dry sample for each measurement. The lipid, protein and carbohydrate content of the dry Spirulina platensis were determined using Soxhlet extraction method, micro-Kjedahl and the phenol-sulfuric acid method 20. Chemical compositions of the biocrude oil were quantitatively and qualitatively identified using GC–MS (Agilent 7890A-5975C, USA) with a HP-5MS capillary column (30m×0.25mm×0.25 ?m). Biocrude oil was firstly diluted by the chromatographic grade anhydrous ethanol to obtain a concentration about 5%, and filtered by 0.45 ?m PTFE filters before injection. 1 mL/min helium was introduced throughout the test as carrier gas. For each test, 1 ?L of liquid sample was injected to the injector which was maintained at 300 °C with split ratio of 50:1. The GC was programmed to hold at 50 °C for 1 min, then ramp by 10 °C/min to 300 °C and keep isothermally for 5 min. Temperature of ion source for mass spectrometer was hold at 250 °C, and scan mass range was 40–400 m/z lasting for 1 s. The chemical compounds were identified by comparison with National Institute of Standards and Technology (NIST) mass spectral database. Functional groups of biocrude oil were analyzed by Thermo Nicolet Nexus 4700 FT-IR Spectrometer (USA). The sample for FT-IR test was prepared by using KBr pellets technique with 2 wt% of oil sample. The scanning wavenumber of IR spectra was in the range of 400–4000 cm?1. The resolution of the spectrometer was 0.1 cm?1. Distillation behaviors of biocrude oil were investigated through thermogravimetric analysis using NETZSCH STA449C (Germany). Approximately 5–6 mg of sample was used in each test with temperature raising from ambient temperature to 900 °C at a constant heating rate of 10 °C/min with 50 mL/min nitrogen flow. Weight loss during heating was persistently recorded and TG curves were reproduced.
Results and discussion
The GC analysis showed that the major portion of the gaseous products was carbon dioxide with traces of hydrogen and ethylene

The results show that temperature was the most important factor affecting the yields of the products. Higher temperatures resulted in lower bio-oil but in lower temperature in higher bio-oil yield. (Comparing points reference)

Effect of process parameters on products distribution
We used the Design Expert software to analyze the experimental results by multiple regressions fitting analysis. Following is the quadric multiple regression equation of yield:

After that, we carried on a significance test of the regression equation. From Table 5, we can see that first degree terms of temperature and concentration are very significant (p < 0.01), the holding time is significant (p < 0.05), and quadratic terms of the three variables are very significant (p < 0.01). The Model F value of 23.02 implies the model is significant. +ere is only a 0.01% chance that a “Model F value” this large could occur due to noise. +e “Lack of Fit F value” of 2.70 implies the Lack of Fit is not significantly relative to the pure error. +ere is a 15.02% chance that a “Lack of Fit F value” this large could occur due to noise. No significant lack of fit is good, and we want the model to fit.
3.2. Graphical Interpretation of the Response Surface Models.
3.3 Effect of process parameters on products distribution
Fig. 1a and b shows the main effects plots of three independent variables on responses (bio-oil and solid yields). These plots depict the mean response for each factor level connected by a line. Temperature is found to be the most important parameter affecting the products yields. It shows a positive main effect for bio-oil and a negative main effect for solid residue yields, implying that at constant reaction time and solids concentration, changing temperature from
200 ? to 350 ? results in a considerable increase in the oil yield accompanied by a drastic decrease in solid yield. However, the steepness of the lines decreases as temperature increases from 320 to 350 ? and there is no considerable difference in the oil yields at 320 and 350 ?. This indicates that there is an optimum temperature beyond which the oil yield remains constant or even starts to decrease. Higher temperatures enhance cracking and dehydration reactions which result in formation of gases, water, and condensation reactions to form more solid products or char Earlier researchers 23,24 indicate lower bio-oil yields at higher reaction times, which can be seen in Fig. 1 as well. Increasing reaction time has caused a decrease in the oil yield; however, it has not affected the solid residues. During hydrothermal liquefaction, longer residence time may lead to decomposition or condensation of bio-oil to low molecular weight chemicals and solids by secondary or tertiary reactions 23,24. Considering that the solid residues formation was independent of reaction time in our experiments, the decrease in bio-oil yields at higher residence times was attributed to the formation of water soluble products. This can also be seen in Table 3 by comparing the experiments at constant temperature and solids concentrations. Solids concentration did not affect the bio-oil yield until 10 wt %; however, it resulted in higher solid residue production. Increasing solids concentration from 10 to 15 wt% led to a slight decrease and then an increase in the oil yields. The variation in the oil yields was attributed to the conversion of bio-oil to solid residues as shown in Fig. 1. Although, there are some fluctuations in solids data, as a general trend, increasing solids concentration had a small effect on increasing bio-oil yield but resulted in more solid residue production.
Statistical analysis
The goal of the optimization was to maximize the bio-oil yield while maintaining the solid residue at a low level. The models of the CCD design were selected based on the highest order polynomials where the additional terms were significant and the models were not aliased. A reduced cubic model based on the coded values was found to best fit the responses. The significance of the coefficients was evaluated based on a confidence interval of 95% where the corresponding p-value is greater than 0.05. The models for prediction of bio-oil and solid residue yields are given in Eqs. (3) and (4), respectively.

where Y1 and Y2 are the bio-oil and solid residue yields (wt %), respectively, X1 is temperature (_C), X2 is reaction time (min), and X3 is solids concentration (wt%). The analysis of variance (ANOVA) for bio-oil and solid residue yields is presented in Tables 4 and 5, respectively to test the statistical significance of the variables. The P-value of the models is less than 0.05, which indicates that the models are significant. The pure error of the models is also presented in the Tables. Pure error mean square is the variance associated with error of replication indicating how well a design point can be repeated obtaining the same result. For both models the pure error mean square is rather small indicating good reproducibility of the experiments. The p-value of lack of fit for both models is larger than 0.05 indicating that the lack of fit is not significant suggesting good fitting of the model to the experimental data. For bio-oil
model, solids concentration and residence time square are non significant terms. As it is shown in Fig. 1a, solids concentration had a very small effect on bio-oil yield compared to temperature and residence time. This is also clear by comparing the coefficients of these terms in Eq. (3). The only non-significant term for solid residue model is the residence time, which was earlier seen in Fig. 1b that solid residue concentration is independent of residence time. Residence time also has the smallest coefficient in Eq. (4). The normal probability and residual plots of the bio-oil and solid residue are shown in Figs. 2 and 3. These plots were examined in order to check the model adequacy and validity. According to the normal probability plot in Fig. 2, the data points appear on a straight line which shows the normal distribution of the errors, therefore, no transformation of the response for the oil was required. Response transformation in the form of power was suggested by the software for solid residue, thus the response for solids is in the form of ðY2Þ_1:72. The residual plot shown in Fig. 3 has a random scatter indicating that the variance of the data is constant for all values of the response. The actual versus predicted bio-oil and solid residue yields are shown in Fig. 4. The actual values are the ones measured after each experiment and the predicted values are calculated using the fitted equations (Eqs. (3) and (4)). The values of R2 and R2adj were found to be 0.988 and 0.974 for bio-oil, and 0.998 and 0.997 for solid residue yields, respectively which show a good approximation of the results by the fitted equations. Also the R2 predicted for the bio-oil and solid residue are 0.811 and 0.991, which are in reasonable agreement with their R2adj.
Response surface plots and optimization of process conditions
In order to determine the effect of the independent variables on the yield of biocrude, a three-dimensional diagram and contour plot for each response were generated as a function of two variables, while the other one variable was held constant. Figure 3 shows the response surface and contour plots for biocrude yield as a function of concentration (x1) and temperature (x2) with a holding time of 40 min. As can be seen from Figure 3, with the increase of temperature, the yield increases and finally tends towards stability. The yield firstly increases and then decreases with the increase of the concentration. Thee highest yield (44.4%) occurs when the concentration and temperature are kept at about 10.5% and 355°C, respectively. Figure 4 shows the response surface and contour plots for biocrude yield as a function of temperature (x2) and holding time (x3) with the concentration of 12.5%. Under this condition, the yield firstly increases and then decreases with the increase of temperature or holding time. The yield reaches to the highest (44.4%) when the concentration and holding time are kept at about 354°C and 38.5 min, respectively. Figure 5 illustrates the response surface and contour plots for bio-oil yield as a function of concentration (x1) and holding time (x3) with specific temperature. The yield also shows increase at the beginning and then decrease with the increase of concentration or holding time with the temperature of 345°C. The highest yield is 44.4% when the concentration and holding time are kept at about 11% and 38 min, respectively.
Conclusions
The optimum operating conditions found in this research could be used to effectively
co-liquefyWAS and sawdust into bio-oil with a relatively high yield and low solid production. The co-feeding has the advantage of treating two types of waste biomass at the same time and thus could enhance the process economy by increasing the substrate concentration. However, there seems to be a maximum concentration (10–15%) beyond which there is no increase in bio-oil production. More interestingly this work demonstrated for the first time that the bio-oil produced from the co conversion has much lower molecular weight (hence less viscous) compared to other bio-oils produced from lignocellulosic biomass at the same operating conditions indicating the synergetic effect of wastewater sludge and lignocellulosic biomass in HTL. This work also showed that while utilization of WSP as the largest fraction of the HTL by-products is a major challenge for HTL operations, using WSP as a feedstock for biogas production through anaerobic digestion provides a novel strategy for recovering energy from this major by-product and makes the co-production of biogas and biocrude oil from a waste stream feasible. The BMP test showed that 800 mL bio-methane was produced cumulatively in 30 days per 0.816 g of TOC or 2.09 g of COD of water-soluble products. The methods and experiments presented in this study can also be used for co-processing of similar types of biomass and WAS, extending the applicability of the research. Once the bio-crude oil is produced, it is possible to refine the bio-oil to biofuels such as heating oil, gasoline, and diesel. This is achieved with a very low carbon footprint due to the biogenic nature of sludge. The wastewater treatment plants, sludges/biosolids management industries and municipalities in Canada or worldwide will be the potential end-users of this technology.

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