Apparent Kinetics of Fast Pyrolysis of Four Different Microalgae and

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Apparent Kinetics of Fast Pyrolysis of Four Different Microalgae and Product Analysis Using Py–FTIR and Py–GC/MS Ribhu Gautam, Akash Varma, and Ravikrishnan Vinu Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.7b02520 • Publication Date (Web): 04 Oct 2017 Downloaded from http://pubs.acs.org on October 5, 2017

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Apparent Kinetics of Fast Pyrolysis of Four Different Microalgae and Product Analysis Using Py-FTIR and Py-GC/MS Ribhu Gautam, Akash Kiran Varma and R. Vinu* Department of Chemical Engineering and National Center for Combustion Research and Development, Indian Institute of Technology Madras, Chennai- 600036, India.

*

Corresponding Author: Email: [email protected]. Phone: +91-44-22574187 1 ACS Paragon Plus Environment

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Abstract This study reports an experimental technique to obtain the isothermal mass loss profiles of four different microalgae (Chlorella vulgaris, Nannochlorpsis oculata, Schizochytrium limacinum and Arthrospira platensis) under fast pyrolysis conditions in the temperature range of 400-700 °C at short residence times of 2-60 s using an analytical pyrolyzer. The microalgae investigated in this study vary significantly based on elemental and biochemical composition. The apparent kinetic parameters of fast pyrolysis of the algae were evaluated using first order and multidimensional diffusion models. The apparent activation energy and pre-exponential factor evaluated under fast heating rate conditions were lower corresponding to those reported under slow pyrolysis conditions, which is attributed to the diffusion effects. The rate parameters were validated by constructing a kinetic compensation plot, which showed that ln(A)=0.19Ea+0.43. The product time evolution at short timescales was studied using in situ Fourier transform infrared spectroscopy, and a majority of the functional groups evolved in the range of 5–12 s. The evolution of C=O and aliphatic C-H stretching vibrations for lipid-rich algae was slow compared to that for protein-rich algae. The pyrolysates were characterized using gas chromatograph/mass spectrometry, and the yield of nitrogen-containing organics followed a linear trend with the elemental nitrogen content in the algae. Keywords Microalgae; fast pyrolysis; kinetics; apparent activation energy; kinetic compensation; Py–FTIR; Py–GC/MS

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1.

Introduction Economic growth and development is resulting in fast depletion of fossil fuel reserves,

and the mitigation of environmental impacts caused due to the excessive use of fossil fuels has gained importance. Over the last decade, research for replacement of conventional fossil fuels is gaining importance with focus on biomass, as it is the fourth most abundant energy source in the world.1 Terrestrial biomass competes with food crops in terms of land requirement, while on the other hand, marine biomass has advantages such as high growth rate, high yield per hectare and high photosynthetic efficiency.2 Marine biomasses have flexibility to grow in sea water, fresh water ponds, industrial effluents and waste water.3 Microalgae, one of the most prominent marine biomass, are abundantly distributed water plants. They consist of lipids, proteins and carbohydrates as their major constituents compared to cellulose, hemicellulose and lignin in lignocellulosic biomass. Some microalgae have lipid content as high as 80 wt.%, and are considered as potential feedstocks for biodiesel production.4 Lipid content in microalgae can be tailored by modifying the culture conditions.5 As microalgae are capable of fixing CO2, their cultivation and exploitation result in mitigation of greenhouse gases in the environment.6 Thermochemical process like pyrolysis can convert the entire microalgae feed into fine chemicals and fuel molecules in a single step.7,8 Composition of microalgae facilitates low temperature degradation during thermochemical treatment compared to lignocellulosic biomass.9 Fast pyrolysis, a technique that uses very high heating rates (>1000°C s-1) to decompose organic matter in the absence of air, can be used for converting microalgae into renewable fuel and speciality chemicals.7,10 Fast pyrolysis is advantageous over slow pyrolysis in terms of high liquid yields, which is attributed to short reaction timescales and minimal secondary decomposition reactions.11,12 Converting algal biomass into renewable fuels via thermochemical 3 ACS Paragon Plus Environment

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routes like fast pyrolysis is shown to be economical as compared to solvent extraction techniques. This is due to the high efficiency and utilization of whole organic content in pyrolysis.7,13 Evaluation of apparent kinetic parameters under fast pyrolysis conditions is imperative for better design of reactors for microalgae pyrolysis. Thermogravimetric analyser (TGA) is widely used to generate mass loss data for estimation of apparent kinetic parameters of thermal degradation of microalgae.14-16 The kinetic data obtained from TGA may not be applicable under fast pyrolysis conditions as they are usually obtained at low heating rates (5–100 °C min-1). Moreover, the apparent (or global) kinetic parameters are evaluated over a limited range of temperature or conversion. In general, differential and integral isoconversional methods, and nth order model fitting technique are used to calculate the apparent kinetic parameters using the TGA data obtained at single or multiple heating rates.17-19 Table 114,15,20-25 provides a summary of the various studies on pyrolysis kinetics of different microalgae. The salient operating conditions and model types are also indicated. It is evident that the global rate parameters, viz. apparent activation energy and pre-exponential factor, vary in a very wide range based on the method adopted to evaluate them and the operating conditions. Recently, Vo et al.26 developed a lumped model involving three reactions, viz. Algae→Bio-oil→Gases, Algae→Gases, to describe the pyrolysis of Aurantiochytrium sp. in a micro-tubing reactor, and determined the activation energy of bio-oil formation to be 71.16 kJ mol-1. In another study, Bach and Chen27 modeled pyrolytic mass loss profile of Chlorella vulgaris using single-, two-, three-, four- and sevenreactions, and determined the rate parameters by fitting. Although the seven-reaction model fitted well with the experimental data, the rate parameters are not unique and are expected to vary under different operating conditions.


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TGA coupled with Fourier transform infrared (FTIR) spectrometer is an important tool to evaluate the time evolution of functional groups in the pyrolysates with pyrolysis temperature. Plis et al.16 coupled TGA with FTIR and mass spectrometer and observed various functional groups in different temperature ranges during pyrolysis of Cladophora glomerata. However, owing to very short reaction timescales in fast pyrolysis, obtaining the concentration profile of the products or product functional groups is a challenging task. Recent studies from our group have shown that in situ fast pyrolysis–FTIR can shed valuable insights on temporal evolution of products from cellulose-polypropylene mixtures and lignin under isothermal conditions.28,29 This study is the first of its kind in the following respects: (a) to report mass loss data of microalgae under fast pyrolysis conditions using Pyroprobe® reactor at isothermal conditions, (b) to determine apparent kinetics of fast pyrolysis of microalgae and establish kinetic compensation effect, and (c) to report evolution of pyrolysate functional groups from microalgae at short timescales of seconds during fast pyrolysis. In this study, four microalgae, Chlorella vulgaris (C. vulgaris), Nannochloropsis oculata (N. oculata), Schizochytrium limacinum (S. limacinum) and Arthrospira platensis (A. Platensis) are subjected to isothermal fast pyrolysis in in a wide temperature range of 400–700 °C. The microalgae chosen for this study were previously studied and investigated for their feasibility as a renewable and useful resource for fuels and chemicals.8,30-33 The mass loss data are utilized to evaluate the apparent kinetic parameters using first order and multi-dimensional diffusion models. The validity of global kinetic parameters is ascertained by constructing the kinetic compensation plot. The time evolution of functional groups present in pyrolysis vapors is studied using in situ pyrolysis-FTIR spectroscopy. Pyrolysis coupled with gas chromatograph/mass spectrometry (Py–GC/MS) experiments are also conducted to analyse the pyrolysate composition from the four different algae species. 2.

Experimental 5 ACS Paragon Plus Environment

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2.1

Characterization of Microalgae The microalgae samples were procured from Reed Mariculture Inc., U.S.A. The samples

were pre-dried in hot air oven at 120 °C for 2 h before the experiments. Dried microalgae samples were characterized by TGA, proximate analyzer, CHNS elemental analyser and bomb calorimeter. The TG mass loss profiles were obtained using a SDT Q600 (T.A. Instruments) TGA to understand the decomposition temperature range of microalgae. The experiment was conducted in N2 atmosphere with a flow rate of 100 mL min-1. 8±0.5 mg of microalgae samples were taken, and the heating rate was 10 °C min-1. Proximate analysis of microalgae samples was done in an automatic multiple sample TGA (TGA-2000A, Navas Instruments) with a sample mass of 0.5 g. In the presence of N2 at a flow rate of 100 mL min-1, the sample was heated to 110 °C to determine the physisorbed moisture content. The temperature was then raised to 900 °C at a heating rate of 80 °C min-1. The sample was held at this temperature to evaluate the volatile matter. The gas flow was then switched to air at 100 mL min-1, and the sample was maintained at 900 oC for 45 min to evaluate the fixed carbon content. The difference was noted as ash content in microalgae samples. CHNS content of the microalgae was analysed in Elementar Vario Micro CHNS analyser. Mass of the sample taken for CHNS analysis was 2.5–3 mg. The HHVs (higher heating values) of the microalgae were determined using IKA 2000 bomb calorimeter using 0.5 g sample. The biochemical or nutritional analysis of the microalgae data are from the literature.34,35,36 2.2

Fast Pyrolysis Experiments: Kinetic Parameter Evaluation Fast pyrolysis experiments were carried out in a Pyroprobe® analytical pyrolyzer (model

5150, C.D.S. Analytical, U.S.A.). The mass of the microalga taken for each experiment was 4.5±0.2 mg, and the samples were weighed in a microbalance with an accuracy of 1 µg. The sample was placed in a quartz tube blocked on both sides with glass wool to avoid any spillage. 6 ACS Paragon Plus Environment

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The quartz tube was then placed inside a resistively heated platinum coil. Argon gas was used to sweep the pyrolysates generated during the fast pyrolysis of microalgae samples. The flow rate of argon was maintained at 7 L h-1 throughout the experiment. The sample inside the quartz tube was instantaneously heated at a rate of 20,000 °C s-1 to the desired pyrolysis temperature. In the temperature range of 400–700 °C, mass loss data of the microalgae was generated at different residence times (2–60 s) at isothermal conditions via gravimetry. For example, in an isothermal experiment, say at 500 °C, multiple microalgae samples were pyrolyzed at hold times of 2, 4, 6, 8, 10, 15, 30, 40, 50, 60 s. The mass of empty quartz tube, quartz tube + sample, and quartz tube + residual char was measured in every experiment to determine mass conversion of microalgae. Each experiment conducted at a particular temperature and hold time was repeated 4–7 times, and the average and standard deviation are reported. The conventional rate equation for the conversion of a solid material such as microalgae is given by Vyazovkin et al.19 α

= α

(1)

where f(α) is the functional dependence of conversion on time, k(T) is the dependence of apparent reaction rate constant on temperature, T. Normalized conversion, 'α ', can be calculated using the formula, α = (m0-mt)/(m0-m∞), where m0, mt and m∞ are initial sample mass, sample mass at time t, and final steady mass obtained after 60 s, respectively. As the reaction proceeds to completion, α increases from 0 to 1. Importantly, this kinetic analysis is global and cannot be linked to specific elementary free radical or concerted reactions occurring during fast pyrolysis of microalgae. Equation (1) can be rewritten as follows: α α

α

α =

=

(2)


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The integral, g(α), depends on the reaction model, f(α). The functional forms of g(α) for first order, 1-dimensional (1D), 2D and 3D–diffusion models are given by –ln(1-α), α2, [(1-

α)ln(1-α)+α] and [1-(1-α)1/3] 2, respectively.19 The rate constants at different temperatures were determined by plotting g(α) vs t, and determining the slope of the straight line through the origin.

The rate constant, according to Arrhenius equation is given by = , where A is the preexponential factor in s-1, Ea is the apparent activation energy in J/mol, and R is the universal gas constant in J mol-1 K-1. The apparent rate parameters were calculated by fitting a straight line to

the equation, ln = ln − . It is worthwhile to mention that the conventional methods adopted for analysis of TGA data such as Kissinger, Flynn-Wall-Ozawa, Kissinger-Akahira-Sunose and Coats-Redfern cannot be used here because these methods require mass loss data as a function of temperature. However, this study involves isothermal mass loss measurements as a function of time. Moreover, our choice of integral method to determine the kinetic parameters is validated as less number of data points would result in significant error in the calculation of (dα/dt) in the differential method. 2.3

Py-FTIR Experiments: Time Evolution of Products The experiments were carried out in Pyroprobe® 5150 series pyrolyzer (C.D.S.

Analytical, U.S.A.) fitted with a stainless steel chamber called Brill cell. It comprised of two antireflection zinc selenide optical windows held in a cylindrical chamber by brass end caps. There were inlet and outlet vents in the chamber through which inert Ar gas was passed continuously at 7 L h-1 throughout the experiment. This flow rate was optimized after multiple experiments such that the vapors do not condense inside the cell, and also are retained for 8 ACS Paragon Plus Environment

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sufficient time to be scanned by the FTIR spectrometer. The Brill cell interface temperature was maintained at 200 °C so that the pyrolysates are not condensed inside. This whole set-up was placed inside the sample compartment of Cary 660 FTIR spectrometer (Agilent Technologies). The Brill cell was placed in such a way that the optical widows were in the beam path of FTIR to analyze the pyrolysates continuously. The schematic of this experimental set-up is given in Supporting Information, and also available elsewhere.28,29 8±0.5 mg of dried microalgae sample was used for each experiment. The sample preparation was same as discussed in the previous section. The pyrolysis time was 60 s and the experiments were conducted at 500 °C at a fixed probe heating rate of 20,000 °C s-1. The detector in FTIR was a linear, high sensitivity mercury cadmium telluride (MCT) detector. The pyrolysates were scanned in the wavenumber range of 4000 cm-1 to 600 cm-1 at a resolution of 2 cm-1. The total scan time was 40 s at a rate of 3 scans s1

. Gram Schmidt function was used to generate the time evolution of functional groups present in

the pyrolysates. 2.4

Py–GC/MS Experiments: Product Analysis This analysis was performed to determine the composition of pyrolysates obtained from

fast pyrolysis of various microalgae samples. The experiments were conducted in a Pyroprobe® 5200 pyrolyzer (C.D.S. Analytical, U.S.A.) interfaced with a gas chromatograph/mass spectrometer (Agilent 7890-5975C). The mass of the sample taken for each experiment was 500±50 µg. Py–GC/MS experiments were performed at 500 °C. The experiments were conducted in trap mode, in which the pyrolysates from the reactor were initially adsorbed on a Tenax® adsorbent cartridge at 45 °C. This was followed by desorption of the pyrolysates from the cartridge at a heating rate of 100 °C min-1 to 300 °C. The pyrolysates were carried by ultra high pure helium gas (99.9995%) at a column flow rate of 1 mL min-1 to the GC/MS. A HP-5


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MS capillary column (30 m length × 0.25 mm i.d.× 0.25 µm film thickness) was used to separate the pyrolysates, which were scanned in the range of 50-400 Da at 70 eV electron ionization voltage. The GC/MS interface, injector, ion source and transfer line temperatures were maintained at 300, 280, 300 and 280 °C, respectively. GC/MS total ion chromatograms (TICs) were analyzed and the pyrolysates were matched with the NIST MS library. The compounds with high match factor (> 85%) were considered in the analysis. The compounds were quantified using absolute area corresponding to each peak per microgram of algae sample pyrolyzed. The experiments were non-consequently repeated three times to ensure repeatability, and the standard deviation is reported. 3.

Results and Discussion

3.1

Characterization of Microalgae The characteristics of the microalgae are tabulated in the Table 2. From the proximate

analysis, it is evident that S. limacinum contains the highest ash (14.1 wt.%), while N. oculata is rich in fixed carbon (30.8 wt.%) among all the algae used in this study. C. vulgaris and A. platensis are rich in volatile matter (78.1 and 77.4 wt.%, respectively). Importantly the microalgae chosen in this study also vary significantly in terms of biochemical composition, i.e. protein, lipid and carbohydrate content, which leads to a systematic variation in their N–content. The N-content in the algae follows the trend: A. platensis (10.1 wt.%) > C. vulgaris (8.3 wt.%) > N. oculata (5.2 wt.%) > S. limacinum (1.5 wt.%). S. limacinum had the highest carbon (~ 50 wt.%) and hydrogen content (7.5 wt.%) owing to the high content of lipids, primarily myristic acid, palmitic acid, docosapentaenoic acid and docosahexaenoic acid.36 Owing to the low oxygen content in S. limacinum, it had high HHV (~26 MJ kg-1), while that of N. oculata was low (18 MJ kg-1).


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The mass loss and differential mass loss curves of the microalgae are depicted in Figure 1. Mass loss observed till 150 °C corresponds to physically bound moisture to the biomolecules of microalgae samples. The next major mass loss stage from 150 to 470 °C corresponds to the decomposition of biochemical contents like lipids, carbohydrates and proteins present in the microalgae. The biochemical components present in the microalgae undergo cracking and depolymerization reactions during pyrolysis.24 The mass loss recorded in this region followed the trend: S. limacinum (81.1 wt.%) > A. platensis ~ C. vulgaris (63.3 wt.%) > N. oculata (50.8 wt.%). The decomposition temperatures corresponding to maximum mass loss rate, Tmax, for various microalgae are depicted in Figure 1. The first peak corresponds to decomposition of proteins and carbohydrates, while lipids decompose at higher temperatures. The Tmax of the first peak was low for N. oculata (264 °C) compared to other species owing to the metal constituents which catalyze the thermal degradation.30,37. The Tmax of protein+carbohydrate decomposition for C. vulgaris and A. platensis occurred at 306 oC. The Tmax corresponding to lipid decomposition for S. limacinum occurred at 398 oC, and the high intensity of this peak substantiates the high lipid content in this alga. Although no significant mass loss was observed beyond 470 oC for a majority of the microalga, S. limacinum recorded mass loss in the high temperature regime (700-800 oC). This can be related, atleast partially, to the catalytic role played by ash in secondary transformation of char to gaseous products. The high ash content in S. limacinum also substantiates this effect. 3.2

Isothermal Fast Pyrolysis Experiments and Kinetics Figure 2 depicts the isothermal mass loss profiles of C. vulgaris, N. oculata, S. limacinum

and A. platensis under fast pyrolysis conditions. For all the microalgae samples, percent degradation or mass conversion increased with increase in temperature. This trend is expected and is in line with the past studies on pyrolysis of algae and biomasses.8,10,24,29 Rapid mass loss 11 ACS Paragon Plus Environment

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for all the four microalgae samples was observed in the first 15 s of pyrolysis after which the mass loss was steady and low. As the temperature increased, maximum extent of degradation was achieved within a shorter time. For example, at 30 s, the conversion of C. vulgaris at different temperatures followed the trend: 61 wt.% (700 °C) > 58 wt.% (650 °C) > 55 wt.% (550 °C) > 50 wt.% (500 °C) > 41 wt.% (450 °C) > 35 wt.% (400 °C). At (600 °C, 60 s), the final conversion of microalgae followed the trend: A. platensis (62 wt.%) > C. vulgaris (58 wt.%) > N. oculata, S. limacinum (49-50 wt.%). Each co-ordinate (t, wt.%) in Figure 2 represents an isothermal fast pyrolysis experiment conducted at a particular temperature for the residence time mentioned. It is worthwhile to note that the high heating rate of 20,000 °C s-1 corresponds to the Pt filament of the Pyroprobe®, and not that of the microalgae sample placed inside the quartz tube. Ojha et al.29 estimated the actual heating rate of the biomass inside the Pyroprobe® to be ~150 °C s-1, which is significantly higher than the typical heating rate in TGA. In order to assess the effect of initial sample mass on conversion, multiple experiments were performed by fast pyrolyzing 4.5 mg and 8 mg of A. platensis and C. vulgaris at 500 oC. Only minor difference in conversion was observed, which was within the error bar (data available in Supporting Information). Lower sample mass such as 2 mg resulted in large standard deviation and poor repeatability of conversion data, while higher sample mass (>10 mg) filled the entire sample tube, and led to inhomogeneous pyrolysis of the sample. This substantiates the choice of initial mass of sample for mass loss experiments. From the isothermal mass loss data, the function g(α) was calculated for first order, 1D, 2D and 3D–diffusion models. These models were chosen based on the nature of the mass loss or conversion profiles, which mimic decelerating kinetics, i.e., the conversion rapidly increases and finally reaches a steady value at long time periods. Other models like power law and Avrami-


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Erofeev were also tested, and the fits were found to be poor owing to accelerating and sigmoidal nature of conversion with time, respectively, with these models. The g(α) vs time data was fitted using a straight line passing through the origin to determine k(T) for all the four microalgae at different temperatures. The linear fits for all the reaction conditions are depicted in Supporting Information. The rate constants and the corresponding regression coefficient values (R2) for different microalgae at different temperatures are shown in Table 3. More than 80% of the experimental data points of (g(α), t) were considered by systematically eliminating the data with highest standard deviation. This is important to ensure that the calculated Arrhenius rate parameters using these rate constant values are reasonably accurate. It is evident that the regression coefficient values for a number of cases are more than 0.95. If R2 ≥ 0.95 is considered as the criterion for selecting a particular model to best describe the experimental data from Table 3, then for all the microalgae considered in this study, the first order model is reasonable. In addition, the diffusion models are also suitable at certain temperature regimes. For N. oculata, it is evident that all the decelerating models including first order, 1D, 2D and 3D-diffusion yield good R2 values, and hence can be used to describe the kinetics. However, in the case of C. vulgaris, 2D and 3D-diffusion models are suitable in the range of 450-600 oC and 500-700 oC, respectively, while the 1D-diffusion model is applicable only in the low temperature regime (400,450 oC). For S. limacinum, the 1D, 2D and 3D-diffusion models are valid only in specific temperature ranges of 400-550 oC, 500-600 oC, 600-700 oC, respectively. For A. platensis, except the first order model, other models lead to poor fits in the initial temperature regime. The Arrhenius plots showing the variation of ln(k) with 1/T for all the microalgae are available in Figure 3. From the Arrhenius plots, it can be observed that only those data in Table 3 with reasonable regression coefficient values are used. Moreover, it was ensured that each Arrhenius plot contains atleast 4 data points. 13 ACS Paragon Plus Environment

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The calculated global kinetic parameters for fast pyrolysis of all microalgae according to different models are tabulated in Table 4. The expressions for f(α) and g(α) are also provided in the table. The apparent activation energies of C. vulgaris, N. oculata, S. limacinum and A. platensis fall in the range of 19.8–24.5 kJ mol-1, 10.7–18.4 kJ mol-1, 10.8–18.5 kJ mol-1, 4.3–18.4 kJ mol-1 respectively. Based on goodness of linear fit (R2 ≈ 0.99), 2D-diffusion, 2D-diffusion, 1D-diffusion and 3D-diffusion models can be used to describe the apparent kinetics of mass loss of C. vulgaris, N. oculata, S. limacinum and A. platensis, respectively. The values of apparent activation energy and pre-exponential factor are significantly low compared to those reported under slow pyrolysis conditions (see Table 1). It is important to note that the value of the rate parameters depends on multiple factors, viz. the method adopted to evaluate them, nature of experimental data, i.e. dynamic or isothermal, and sample heating rate. Generally, the values of rate parameters evaluated under fast heating rate conditions are low due to diffusion limitations. Owing to thermal shock experienced by the sample at high heating rates, the diffusion of the pyrolysis vapors from the metaplast (or condensed) phase is usually the slow step compared to elementary reaction kinetics. Korobeinichev et al.38 conducted pyrolysis of pine bark and pine branch at 150 oC s-1 in differential mass spectrometric thermal analysis equipment, and evaluated the apparent activation energies for pine bark and pine branch pyrolysis to be 55.6 and 68.1 kJ mol-1, respectively. Ojha et al.29 also observed that the values of the global rate parameters obtained for isothermal fast pyrolysis experiment are low (Ea = 23 kJ mol-1 and A = 6.44 s-1) compared to that obtained using conventional TGA for lignin. Usually, integral or differential isoconversional methods used to analyze dynamic TGA data involve the calculation of apparent activation energy in specific conversion regimes. However, in this study, the rate parameters correspond to complete conversion of the microalgae upto char formation in a wide temperature range. The variation in apparent activation energy among the microalgae can be attributed to 14 ACS Paragon Plus Environment

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variation in their biochemical composition and the nature of products evolved from them during fast pyrolysis. The apparent activation energies of various species of algae with and without pretreatments were reported in the range of 69.8–209.9 kJ mol-1.23 In another study, distributed activation energy model was used to calculate apparent activation energies as 152.2 and 334 kJ mol-1 for N. oculata and Tetraselmis sp., respectively.21 Baltic algae sample was found to have first and second stage activation energies as 32 and 177 kJ mol-1, respectively, using a TGA.16 The activation energies in low temperature and high temperature ranges are reported to be low and high, respectively, during pyrolysis.39,40 In order to validate the rate parameters obtained in this study and to evaluate all the rate parameters from the literature on a common scale, a kinetic compensation plot was constructed as shown in Figure 4. The kinetic compensation plot, also called as the Constable plot, signifies the linear variation of Ea on the abscissa with logarithm of pre-exponential factor on the ordinate for a family of related processes. The expression is given by ln(A) = m × Ea + n.19,41,42 The error in the values of ‘m’ and ‘n’ can be minimized by considering a wide data set of Ea and A obtained using various models at different heating rates. It is well known that random errors in kinetic data induced by different approximations used in mathematical methods to determine the rate parameters can lead to an apparent or statistical compensation effect. Figure 4 depicts the kinetic compensation plot for algae pyrolysis, which includes data from the literature and this study. More than 40 data points from various studies on pyrolysis of different microalgae are included in the plot. Importantly, the rate parameters from the literature were evaluated using different methods as outlined in Table 1. It is evident that the linear fit has a high regression coefficient value, and the equation is given by ln(A) = 0.19 Ea + 0.43. It is worthwhile to mention that the origin of the compensation effect in this study is more statistical in nature and a 15 ACS Paragon Plus Environment

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physicochemical explanation cannot be provided. This is because the kinetic data from literature correspond to many different algae species and different techniques adopted to evaluate them. Moreover, the decomposition mechanism and decomposition temperature range for various biochemical components also differ for different algae species. Therefore, a detailed kinetic study of a specific alga species under different pyrolysis conditions is a must to establish an isokinetic relationship. Recently, Ranzi and co-workers43 reported a semi-detailed multistep mechanism and kinetic model of algae pyrolysis by describing the algae structure as a linear combination of proteins, lipids and carbohydrates. 3.3

Py-FTIR Experiments: Time Evolution of Products The FTIR analysis of pyrolysates from microalgae at 500 °C was performed to

understand the time evolution of major functional groups, and the differences in evolution among different algae types. The major functional groups identified in the spectra of pyrolysates include O–H stretch (3570 cm-1), N–H stretch (3330 cm-1), saturated aliphatic C–H stretch (2930 cm-1), CO2 (2350 cm-1), carbonyl stretch (C=O) (1767 cm-1), C–C skeletal vibrations (1341 cm-1), aromatic C–H in-plane bend (954 cm-1) and aromatic C–H out-of-plane bend (683 cm-1). Importantly, the flatness of the FTIR spectra in the entire range of 4000 – 600 cm-1 was ensured at the beginning (time = 0 s) such that no residual functional groups were present. Based on the absorbance at different wavenumbers these peaks were analysed for four microalgae. Figure 5 depicts the temporal evolution of the major functional groups. These functional groups represent a wide variety of compounds present in the pyrolysates like aliphatic and aromatic hydrocarbons, carboxylic acids, aldehydes and ketones, phenols and various N- containing compounds like indoles, pyrroles, amides and nitriles. It can be seen that the functional groups begin to evolve immediately after firing the Pyroprobe®. This observation also confirms that the sample inside the quartz tube attains the pyrolysis temperature instantaneously. The maximum vapour 16 ACS Paragon Plus Environment

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evolution was observed in the range of 5–12 s for a majority of the functional groups, except for C–H stretch in the case of S. limacinum. The vapour evolution increased and then decreased signifying the conversion of the feedstock to residue, viz. char and ash. Carbondioxide was observed to be one of the major products from fast pyrolysis of the microalgae. This is primarily attributed to the decarboxylation reactions. The constituents of microalgae, viz. proteins, lipids and carbohydrates, yield CO2 as product upon pyrolysis. Marcilla et al.44 also observed CO2 as a major product at various temperature ranges during pyrolysis using a TG-FTIR. Amino acids present in proteins upon cracking result in the formation of ammonia which combined with other intermediates result in amides and nitriles. The thermal decomposition of microalgae involves reactions like dehydration and depolymerisation of long chains. This results in a variety of low molecular weight compounds like acetone, butanone, propanal etc. The repolymerization reactions result in high molecular weight compounds in the pyrolysates. The absorbance values for A. platensis and C. vulgaris are high (4.5–5×10-2 Au) in comparison to N. oculata and S. limacinum (~2.5×10-2 Au) owing to the high conversions at 500 °C. The carbonyl stretch (C=O) functional group was the second major peak, and it corresponds to aldehydes, ketones, carboxylic acids and amides in the pyrolysates. The highest relative content of aliphatic C–H stretch was observed in the pyrolysates from S. limacinum owing to the degradation of long chain fatty acids. The aromatic compounds present in the pyrolysates from microalgae include heterocyclic aromatic compounds like indoles, pyrroles and pyridines, which result in aromatic C–H stretch as well as N–H stretch.8,33 The N–H stretch can also be linked to products of decomposition of proteins like aliphatic amides. The O– H stretch was predominantly due to the dehydration reactions resulting in H2O and the formation of phenols.


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The maximum vapour evolution occurred in a short time interval of 5 s for protein-rich A. platensis, which can be partly attributed to the relatively easy decomposition of the protein biomolecules compared to carbohydrates and lipids. Contrastingly, the evolution of pyrolysates from lipid-rich S. limacinum was slow, and it can be observed that intensities of aliphatic C–H and C=O stretching vibration do not decrease even after 40 s of pyrolysis time, and both follow a similar trend in their evolution. This can be precisely related to the production of long chain carboxylic acids. Anand et al.33 observed that tetradecanoic and hexadecanoic acids are the major products from non-catalytic fast pyrolysis of S. limacinum in a wide temperature range of 350650 oC. The high intensity of the aliphatic C–H stretch compared to C=O stretch in S. limacinum is due to the formation of alkanes and alkenes as pyrolysates in addition to long chain carboxylic acids. In the case of N. oculata, slight shift in the maximum time evolution of C–H stretch compared to C=O stretch, and high residual concentrations of these two functional groups were observed at long time periods (30-40 s). This supports the fact that this microalga contains more lipids than C. vulgaris and A. platensis. Such a trend is not observed in the case of C. vulgaris and A. platensis, as all the functional groups evolve and get depleted at the same time. 3.4

Py-GC/MS of Microalgae Figure 6(a) depicts the major class of compounds obtained from fast pyrolysis of the

microalgae at 500 °C. The compounds were classified into the following categories: alkanes, alkenes, aromatic hydrocarbons, N-containing compounds, oxygenates and phenolics. It is important to mention that owing to varying composition of the microalgae considered in this study, more than 100 different organic compounds belonging to the above categories were obtained from fast pyrolysis. Therefore, the organics were not quantified by using calibration curves of pure compounds, which is a tedious task. Instead, absolute area of the peak was determined and normalized with respect to the mass of the sample. This is better than the relative 18 ACS Paragon Plus Environment

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area% measure used in a number of studies to quantify the pyrolysate composition.8,33 The GC/MS total ion chromatograms of pyrolysates from the microalgae are reported in Supporting Information. The individual organics and their composition (in absolute area/µg of algae sample) obtained from different microalgae are also listed in tabular form in Supporting Information. The fast pyrolysis of microalgae resulted in aliphatic (alkanes, alkenes) and aromatic hydrocarbons, esters, oxygenates (carboxylic acids, aldehydes, ketones and alcohols), phenolics, and N- and S-containing organic compounds. As microalgae contain lipids, carbohydrates and proteins, each of the components can be related to the family of compounds present in the pyrolysates. Cracking of proteins result in the formation of nitriles, amines, and other Ncontaining organic compounds.45 Pyrolysis of lipid fraction results in the liberation of long and short chain carboxylic acids at low temperatures, while at high temperatures, aliphatic hydrocarbons are formed via decarboxylation reaction. Ammoniation reaction, i.e. addition of NH3 from protein pyrolysis, of carboxylic acids can also occur to form acid amides. Carbohydrate fraction can pyrolyze to form a number of low molecular weight oxygenates like aldehydes and ketones. Moreover, due to dehydration and cyclization reactions, they can also form cyclopentanones, cyclohexanones and furan derivatives.11 The aliphatics present might undergo aromatization at high temperatures, and the amides formed dehydrate to form nitriles. Fast pyrolysis of amino acids present in the protein fraction results in their cyclization to form cyclic and polycyclic N-containing compounds (e.g. indoles, pyrroles, pyridines, etc.). Toluene (~2×104 absolute area/µg) and acetone (~1.5×104 absolute area/µg) were the major aromatic and oxygenated compounds among all products, respectively, from fast pyrolysis of C. vulgaris. The major N-containing compounds were pyrroles, pyridines, indoles and nitriles. Phenol and cresol were the major phenolics. Toluene, styrene, ethyl benzene and xylene were the major aromatic hydrocarbons accounting for 25% selectivity. Pyrrole and methyl-substituted 19 ACS Paragon Plus Environment

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pyrroles were the major N-containing compounds, and were produced at reasonable selectivity of 8%. The S-containing compounds included disulfide and trisulfides, which were observed at 7% selectivity. Maximum selectivity of oxygenates was obtained from this alga at c.a. 24% selectivity, and the major ones were acetone, 3-methyl-butanal, isobutyraldehyde, 3-methyl-3buten-2-ol and cyclopentanone. Fast pyrolysis of N. oculata resulted in nearly equal amounts of alkanes and alkenes (C8C16 range) in the pyrolysate with 18% selectivity each. In addition, aromatic hydrocarbons and N-containing compounds were also significant with 26% and 16% selectivities, respectively. Phenolics, primarily phenol, cresol, xylenol and ethyl phenol, were produced in low quantities with c.a. 5% selectivity. Acetone, pentanone, butanone, acetaldehyde and 2-cyclopenten-1-one were the major low molecular weight oxygenates produced by pyrolysis of carbohydrates. Fast pyrolysis of S. limacinum microalga resulted in high selectivities of aliphatic (42% selectivity) and aromatic hydrocarbons (32% selectivity) compared to that from any other algae used in this study. Anand et al.33 showed that hexadecanoic and tetradecanoic acids were the major compounds released from this microalga when fast pyrolysis was conducted in a drop cup micropyolyzer. Although long chain carboxylic acids were obtained at 12% selectivity, we find that in diffusion limited system like Pyroprobe®, there is a possibility of decarboxylation of these fatty acids to form linear chain alkanes and alkenes. The linear chain hydrocarbons can undergo aromatization reactions to form aromatics like benzene, alkyl-and alkenyl-substituted benzenes. Gang et al.31 also observed high aliphatic and aromatic hydrocarbon content in the pyrolysates from S. limacinum. Fast pyrolysis of protein-rich A. platensis resulted in amines, pyrazoles, pyrroles, pyrrolidine, nitriles, pyridines, amides, indoles and quinolines as major N-containing organics with highest selectivity of 28%. These N-containing organics were also observed in earlier 20 ACS Paragon Plus Environment

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studies on fast pyrolysis of A.platensis.8,46 Maximum phenolic selectivity of 16% was observed from this alga, and the phenolic compounds included phenol, cresol and ethyl phenol. Toluene was the major aromatic hydrocarbon with 15% selectivity, and others included alkyl benzenes, ethyl benzene, xylene, styrene and naphthalene. Acetone was the major small molecule oxygenate (7% selectivity) formed due to ketonization reactions involved in microalgae pyrolysis.32 Other low molecular weight oxygenates included acetic acid, methyl butanal, isobutyraldehyde and dimethyl furan. A. platensis recorded the second maximum selectivity of 19.6% for oxygenates. Importantly, the sum total contribution of N-containing compounds from each microalga was observed to increase linearly with increasing elemental nitrogen content (Figure 6(b)). This shows that a majority of the organic nitrogen in the microalgae are captured in the condensable pyrolysis compounds, without significant conversion to other N-containing gases like ammonia and HCN. This is in line with another study on catalytic fast pyrolysis of C. vulgaris where these gases were produced when ZSM-5 catalyst was used at high catalyst:alga (C. vulgaris) mass ratios.45 Chen et al.47 studied the fate of organic nitrogen and its transformations during pyrolysis of microalgae. Nearly 90% of nitrogen in algae exists in the proteins.47 When microalgae are pyrolyzed in the temperature range of 400-500 oC, nitrogen in proteins are converted to pyridines, pyrroles, amines, nitriles and amides. These compounds are also observed in the pyrolysates in this study (refer Supporting Information for the detailed list of compounds). Very low evolution of ammonia and HCN was observed at these temperatures, which is partly attributed to ammoniation of fatty acids to form fatty amides.47 When temperature is increased, these fatty amides are cracked with the evolution of ammonia. Moreover, the formation of nitriles also occurs at lower pyrolysis temperature resulting from the cracking of proteins and dehydration of fatty amides curtailing HCN evolution. The secondary


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cracking of nitriles at higher temperatures results in HCN evolution.47 All these aspects favor the trapping of nitrogen in organic condensates at low pyrolysis temperatures ( S. limacinum, A. platensis (~18 kJ mol-1) > N. oculata (~14 kJ mol-1). The time evolution of major functional group vibrations under short residence times was probed using Py-FTIR, which showed that the vibrations corresponding to CO2, C=O stretch and aliphatic C-H stretch were significant in intensity. Importantly, the time evolution of functional groups was different for protein-rich and lipid-rich microalgae. For lipid-rich alga like S.limacinum, these functional groups evolved slowly over long time periods. From Py-GC/MS analysis, the yield of N– containing compounds in the pyrolysate is shown to correlate well with N-content in the microalgae. The methodology developed in this study to obtain the mass loss profiles and apparent kinetic parameters is quite generic, and can be applied for any solid feedstock pyrolysis.

Acknowledgements The authors thank Chevron Inc. for partly funding the study via alumni grant. The National Center for Combustion Research and Development is funded by Department of Science and Technology, Government of India. 22 ACS Paragon Plus Environment

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10. Harman-Ware, A.E.; Morgan, T.; Wilson, M.; Crocker, M.; Zhang, J.; Liu, K.; Stork, J.; Deblot, S. Microalgae as a renewable fuel source: fast pyrolysis of Scenedesmus sp. Renew. Energy 2013, 60, 625–632. 11. Vinu, R.; Broadbelt, L.J. A mechanistic model of fast pyrolysis of glucose-based carbohydrates to predict bio-oil composition. Energy Environ. Sci. 2012, 5, 9808–9826. 12. Liu, C.; Wang, H.; Karim, A.M.; Sun, J.; Wang, Y. Catalytic fast pyrolysis of lignocellulosic biomass. Chem. Soc. Rev. 2014, 43, 7594–7623. 13. Aysu, T.; Sanna, A. Nannochloropsis algae pyrolysis with ceria-based catalysts for production of high-quality bio-oils. Bioresour. Technol. 2015, 194, 108–116. 14. Bui, H.; Tran, K.; Chen, W. Pyrolysis of microalgae residues – A kinetic study. Bioresour. Technol. 2016, 199, 362–366. 15. Ali, S.A.M.; Razzak, S.A.; Hossain, M.M. Apparent kinetics of high temperature oxidative decomposition of microalgal biomass. Bioresour. Technol. 2015, 175, 569–577. 16. Plis, A.; Lasek, J.; Skawinska, A.; Zuwała, J. Thermochemical and kinetic analysis of the pyrolysis process in Cladophora glomerata algae. J. Anal. Appl. Pyrol. 2015, 115, 166–174. 17. Wang, X.; Li, X.; Yan, D. Thermal decomposition kinetics of thermotropic poly (oxybenzoate-co-trimethylene terephthalate). J. Appl. Polym. Sci., 2000, 78, 2025–2036. 18. Jiang, G.; Nowakowski, D.J.; Bridgwater, A.V. A systematic study of the kinetics of lignin pyrolysis. Thermochim. Acta 2010, 498, 61–66. 19. Vyazovkin, S.; Burnham, A.K.; Criado, J.; Pérez-Maqueda, L.; Popescu, C.; Sbirrazzuoli, N. ICTAC Kinetics Committee recommendations for performing kinetic computations on thermal analysis data. Thermochim. Acta 2011, 520, 1–19.


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20. Sukarni; Sudjito; Hamidi, N.; Yanuhar, U.; Wardana, I.N.G. Thermogravimetric kinetic analysis of Nannochloropsis oculata combustion in air atmosphere. Front. Energy 2015, 9, 125–133 21. Ceylan, S.; Kazan, D. Pyrolysis kinetics and thermal characteristics of microalgae Nannochloropsis oculata and Tetraselmis sp. Bioresour. Technol. 2015, 187, 1–5. 22. Lopez-Gonzalez, D.; Fernandez-Lopez, M.; Valverde, J.L.; Sanchez-Silva, L. Pyrolysis of three different types of microalgae: kinetic and evolved gas analysis. Energy 2014, 73, 33– 43. 23. Wang, X.; Guo, F.; Li, Y.; Yang, X. Effect of pretreatment on microalgae pyrolysis: kinetics, biocrude yield and quality, and life cycle assessment. Energy Convers. Manage. 2017, 132, 161–171. 24. Peng, W.; Wu, Q.; Tu, P.; Zhao, N. Pyrolytic characteristics of microalgae as renewable energy source determined by thermogravimetric analysis. Bioresour. Technol. 2001, 80, 1– 7. 25. Shuping, Z.; Yulong, W.; Mingde, Y.; Chun, L.; Junmao, T. Pyrolysis characteristics and kinetics of the marine microalgae Dunaliella tertiolecta using thermogravimetric analyzer. Bioresour. Technol. 2010, 101, 359–365. 26. Vo, T.K.; Ly, H.V.; Lee, O.K.; Lee, E.Y.; Kim, C.H.; Seo, J.; Kim, J.; Kim, S. Pyrolysis characteristics and kinetics of microalgal Aurantiochytrium sp. KRS 101. Energy 2017, 118, 369–376. 27. Bach, Q.-V.; Chen, W.-H. A comprehensive study on pyrolysis kinetics of microalgal biomass. Energy Convers. Manage. 2017, 131, 109-116. 28. Ojha, D.K.; Vinu, R. Fast co-pyrolysis of cellulose and polypropylene using Py-GC/MS and Py-FT-IR. RSC Adv. 2015, 5, 66861–66870. 25 ACS Paragon Plus Environment

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List of Figures Figure 1. Mass loss and differential mass loss profiles of the microalgae at 10 °C min-1. Figure 2. Isothermal mass loss profiles of (a) C. vulgaris, (b) N. oculata, (c) S. limacinum, and (d) A. platensis under fast pyrolysis conditions at different temperatures. Figure 3. Arrhenius plots for first order, 1D, 2D, and 3D-diffusion models for fast pyrolysis of (a) C. vulgaris, (b) N. oculata, (c) S. limacinum, and (d) A. platensis. Figure 4. Kinetic compensation plot for microalgae pyrolysis comprising kinetic parameters from literature and this study. The line denotes linear fit. Figure 5. Time evolution of major functional groups in the pyrolysates from fast pyrolysis of (a) C. vulgaris, (b) N. oculata, (c) S. limacinum, and (d) A. platensis at 500 °C. Figure 6. (a) Pyrolysate distribution from fast pyrolysis of microalgae at 500 °C. (b) Variation of total area of N-containing compounds with elemental N-content in microalgae. List of Tables Table 1. Kinetic parameters for microalgae pyrolysis obtained from literature as well as from current study. Table 2. Basic characteristics of microalgae used in this study. Table 3. Rate constant data obtained for fast pyrolysis of four different microalgae using various models. Table 4. Apparent activation energies and pre-exponential factors obtained using various models to describe the kinetics of fast pyrolysis of the microalgae.


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100 90

Mass remaining (%)

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40 30 20 10 0

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0.8 o

Derivative mass loss (%/ C)

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600

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800

o

398 C 0.7 o

0.6

306 C

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o

264 C

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0.2

453 C

0.1 0.0

0

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0

Temperature ( C)

Figure 1. Mass loss and differential mass loss profiles of the microalgae at 10 °C min-1.


Energy & Fuels

100

(a) C. vulgaris

90

0

400 C 0 550 C 0 700 C

80

0

450 C 0 600 C

0

500 C 0 650 C

70 60 50

(b) N. oculata

90

M ass rem aining (% )


100

40

80 70 60 50 40

30

30 0

10

20

30 40 Time (s)

100

50

60

0

10

20

30 40 Time (s)

50

60

100

(c) S. limacinum

(d) A. platensis 90


90


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 30 of 39

80 70 60 50 40

80 70 60 50 40

30

30 0

10

20

30 Time (s)

40

50

60

0

10

20

30 Time (s)

40

50

Figure 2. Isothermal mass loss profiles of (a) C. vulgaris, (b) N. oculata, (c) S. limacinum, and (d) A. platensis under fast pyrolysis conditions at different temperatures.


Page 31 of 39

-2.0

-2.4

(a) C. vulgaris

2

2

R = 0.97

-3.6

2

R = 0.93

-4.0 -4.4

First Order 1-D diffusion 2-D Diffusion 3-D Diffusion

-4.8 -5.2

-3.2 ln (k ), k in s -1

ln (k ), k in s -1

R = 0.96

2

R = 0.93

-2.8 -3.2

(b) N. oculata

-2.8

-2.4

-3.6 2

R = 0.96

-4.0 -4.4

2

R = 0.99

-4.8 2

R = 0.90

-5.2

2

R = 0.97

-5.6

-5.6 1.0

1.1

1.2 1.3 1000/T (K-1)

1.4

1.0

1.5

-1.6

-2.4

1.1

1.2 1.3 1000/T (K-1)

1.4

-1.6

(c) S. limacinum

-2.0

1.5

(d) A. platensis

-2.0

2

R = 0.92

ln (k ), k in s -1

2

ln (k ), k in s -1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

-2.8 -3.2 -3.6

2

R = 0.92 2

-4.0

R = 0.93

-4.4

-2.8 -3.2 -3.6

2

R = 0.94

-4.8

R = 0.97

-2.4

2

R = 0.97 2

R = 0.91

2

R = 0.99

-4.0 1.0

1.1

1.2 1.3 1000/T (K-1)

1.4

1.5

1.0

1.1

1.2 1.3 1000/T (K-1)

1.4

1.5

Figure 3. Arrhenius plots for first order, 1D, 2D, and 3D-diffusion models for fast pyrolysis of (a) C. vulgaris, (b) N. oculata, (c) S. limacinum, and (d) A. platensis.


Energy & Fuels

ln(A) = 0.19 Ea + 0.43

60

2

R = 0.97 50

ln(A), A in min-1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 39

40

30 20

20

Sukarni et al. 14 Bui et al. 21 Ceylan and Kazan 15 Ali et al. 22 Lopez-Gonzalez et al. 23 Wang et al. Current Study

Current Study

10

0 0

50

100

150

200

250

300

350

-1

Ea (kJ mol )

Figure 4. Kinetic compensation plot for microalgae pyrolysis comprising kinetic parameters from literature and this study. The line denotes linear fit.


Page 33 of 39

-1

-1

-1

Aromatic C-H out-of-plane bend (683 cm ) C-C skeletal vibrations (1341 cm ) C=O stretch (1767 cm ) -1 -1 -1 -1 Carbondioxide (2350 cm ) Aliphatic C-H stretch (2930 cm ) N-H Stretch (3330 cm ) O-H Stretch (3750 cm ) 6

-2

Absorbance (Au) [×10 ]

-2


4 3 2

2.0 1.5 1.0 0.5

1 0

(b) N. oculata

2.5

(a) C. vulgaris

5

0

10

20 Time (s)

30

0.0

40

0

10

5

20 Time (s)

(c) S. limacinum

2.5

30

40

(d) A. platensis 4

2.0

-2


-2


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Energy & Fuels

1.5 1.0 0.5 0.0

0

10

20

30

40

3 2 1 0

0

10

Time (s)

20

30

40

Time (s)

Figure 5. Time evolution of major functional groups in the pyrolysates from fast pyrolysis of (a) C. vulgaris, (b) N. oculata, (c) S. limacinum, and (d) A. platensis at 500 °C.


Energy & Fuels

5

N. oculata A. platensis

(a)

4

3

4

Composition ( × 10 Abs area/µg)

C. vulgaris S. limacinum

2

1

0

Alkanes

Alkenes

Aromatics N-containing Oxygenates compounds

45000

Absolute area/µ µg of algae

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 39

Phenolics

A. platensis

40000 35000 30000

C. vulgaris

25000 20000

N. oculata

15000

(b)

10000 5000

S. limacinum

0

0

2

4 6 8 N content in microalgae (wt.%)

10

12

Figure 6. (a) Pyrolysate distribution from fast pyrolysis of microalgae at 500 °C. (b) Variation of total area of N-containing compounds with elemental N-content in microalgae.


Page 35 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels Table 1. Kinetic parameters for microalgae pyrolysis obtained from literature as well as from the current study.

Algae

N. oculata

Heating rate (°C min-1)

Apparent activation energy (Ea, kJ mol-1)

ln (A) (A, min-1)

10–70

174.1 – 252.1

19.1 – 46.6

Chlamydomonas sp.

61.7 – 198.3

10.2 – 41.5

64.8 – 228.8

10.8 – 47.8

152.2

34.02

334.0

61.7

106.4 – 107.7

21.9 – 23.1

92 – 95.8

19.7 – 19.9

32.4 – 213.1

3.3 – 21.7

20 C. sorokiniana N. oculata 5 – 20 Tetraselmis sp. N. oculata C. vulgaris

5 – 20

N. gaditana Scenedesmus almeriensis

41.3 – 173.8

7.2 – 15.7

C. vulgaris

63.5 – 135.3

11.7 – 12.7

Isochrysis (without pretreatment)

112.5 – 209.9

26.9 – 38

Isochrysis (ultrasonication pretreatment)

69.8 – 191.9

18.7 – 34.8

Isochrysis (acid heating pretreatment)

73.5 – 166.1

20.2 – 35.7

Isochrysis (microwave pretreatment)

88.5 – 174

20.8 – 31.6

76 – 97

14.6 – 18.3

40

5 – 25

A. platensis 15–80 C. protothecoides Dunaliella tertiolecta

5 – 40

42 – 52

8.4 – 9.3

131 – 152

34.85

*- DAEM – Distributed activation energy model


Model details

Reference

Kissinger method Five pseudo component model

Sukarni et al.20 Bui et al.14

DAEM* (α = 0.1-0.8)

Ceylan and Kazan21

nth order model fitting

Ali et al.15

Multi-step model (devolatization and oxidation steps)

LopezGonzalez et al.22

Random nucleation followed by growth, 3Ddiffusion, random nucleation and subsequent growth

Wang et al.23

Freeman – Caroll

Peng et al.24

Kissinger and Flynn–Wall– Ozawa

Shuping et al.25

Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Page 36 of 39

Table 2. Basic characteristics of microalgae used in this study. Proximate Analysis a Microalgae

Ultimate Analysis a C

H

N

(%)

(%)

(%)

Nutritional Analysis 34,35,36 b

S (%) O (%)

Proteins

Lipids

Carbohydrates

(%)

(%)

(%)

HHV (MJ kg-1)

VM (%)

FC (%)

A (%)

C. vulgaris

78.1

17.4

4.4

44.9

6.9

8.3

0.5

35.0

51-58

12-17

14-22

19.5

N. oculata

64.9

30.8

4.3

35.6

6.7

5.2

0.5

47.7

62

18

9

18.0

S. limacinum

71.1

14.8

14.1

49.9

7.5

1.5

1.5

25.5

14

51

24

25.8

A. platensis

77.4

17.2

5.4

44.5

6.7

10.1

0.6

32.7

46-63

4-9

8-14

20.8

VM = volatile matter, FC = fixed carbon and A = ash. a Proximate and ultimate analysis data are in wt.% (dry basis). b Nutritional analysis data are in wt.% (dry basis) with ash constituting the balance for S. limacinum,36 and ash and nucleic acids constituting the balance for C. vulgaris, N. oculata and A. platensis.34,35


Page 37 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Energy & Fuels

Table 3. Rate constant data obtained for fast pyrolysis of four different microalgae using various models.

Model

400°°C k (s-1)

R2

450°°C k (s-1)

R2

500°°C k (s-1)

550°°C

R2

k (s-1)

600°°C

650°°C

700°°C

R2

k (s-1)

R2

k (s-1)

R2

k (s-1)

R2

C. vulgaris First order

0.04

0.96

0.06

0.97

0.08

0.99

0.11

0.98

0.11

0.99

0.12

0.99

0.12

0.94

1D-diffusion

0.02

0.96

0.02

0.98

0.02

0.93

0.03

0.92

0.03

0.94

0.05

0.90

0.06

0.92

2D-diffusion

0.01

0.93

0.02

0.97

0.02

0.97

0.03

0.97

0.03

0.98

0.03

0.92

0.05

0.94

3D-diffusion 0.004 0.92 0.008 0.93

0.01

0.96 0.013 0.92 0.015 0.96 0.015 0.98 0.016 0.96 N. oculata

First order

0.04

0.99

0.04

0.99

0.06

0.99

0.08

0.99

0.12

0.99

0.07

0.98

0.09

0.99

1D-diffusion

0.02

0.96

0.02

0.99

0.02

0.97

0.02

0.93

0.02

0.98

0.02

0.95

0.03

0.95

2D-diffusion

0.01

0.96

0.01

0.99

0.02

0.95

0.02

0.97

0.02

0.95

0.02

0.99

0.02

0.95

3D-diffusion 0.004 0.97 0.005 0.96 0.007 0.95 0.011 0.97 0.016 0.98 0.009 0.96 0.012 0.97 S. limacinum First order

0.06

0.99

0.07

0.96

0.05

0.97

0.09

0.95

0.09

0.95

0.12

0.98

0.19

0.92

1D-diffusion

0.02

0.94

0.02

0.96

0.02

0.99

0.02

0.96

0.03

0.92

0.03

0.97

0.06

0.90

2D-diffusion

0.02

0.97

0.02

0.93

0.01

0.96

0.02

0.95

0.02

0.95

0.02

0.93

0.02

0.93

3D-diffusion

0.01

0.95

0.02

0.91

0.02

0.95

0.01

0.93

0.02

0.98

0.02

0.96

0.02

0.96

A. platensis First order

0.06

0.94

0.08

0.93

0.11

0.92

0.11

0.97

0.14

0.97

0.15

0.97

0.17

0.98

1D-diffusion

0.02

0.96

0.02

0.93

0.03

0.90

0.03

0.93

0.02

0.97

0.02

0.91

0.02

0.91


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Page 38 of 39

2D-diffusion

0.02

0.93

0.02

0.91

0.02

0.93

0.03

0.94

0.02

0.98

0.03

0.94

0.03

0.92

3D-diffusion

0.01

0.74

0.01

0.78

0.02

0.93

0.02

0.92

0.02

0.97

0.02

0.97

0.02

0.98

Table 4. Apparent activation energies and pre-exponential factors obtained using various models to describe the kinetics of fast pyrolysis of the microalgae.

Microalgae

First Order

1D-diffusion

2D-diffusion

3D-diffusion

f(α) = (1-α);

f(α)= 1/(2α);

f(α)=-[ln(1-α)] -1;

f(α)=3/2(1-α)2/3[1-(1-α)1/3] -1;

g(α) = -ln(1-α)

g(α)=α2

g(α)=(1-α)ln(1-α)+α

g(α)=[1-(1-α)1/3] 2

A

Ea

(s-1)

(kJmol-1)

C. vulgaris

1.63

19.84

0.93 0.98

23.40

0.93 0.92

24.47

N. oculata

0.51

14.36

0.96 0.15

13.10

0.96 0.08

S. limacinum

1.54

18.37

0.92 0.12

10.77

A. platensis

1.69

18.38

0.97 0.18

12.62

R2

A

Ea

(s-1)

(kJmol-1)

A

Ea

(s-1)

(kJmol-1)

0.97

0.33

23.18

0.90

10.65

0.99

0.10

18.43

0.97

0.97 0.27

18.53

0.93

0.14

16.28

0.94

0.91 0.14

11.77

0.97

0.03

4.32

0.99

R2

A

Ea

(s-1)

(kJmol-1)


R2

R2

Page 39 of 39

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Energy & Fuels

Workflow involved in evaluating the fast pyrolysis kinetics of microalgae 262x163mm (150 x 150 DPI)

ACS Paragon Plus Environment

Apparent Kinetics of Fast Pyrolysis of Four Different Microalgae and

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