Pyrolysis characteristics and kinetics of algal biomass

Biomass Conv. Bioref. DOI 10.1007/s13399-015-0173-7

ORIGINAL ARTICLE

Pyrolysis characteristics and kinetics of algal biomass using tga analysis based on ICTAC recommendations M. Radhakumari 1 & D. Jaya Prakash 2 & B. Satyavathi 1

Received: 18 May 2015 / Revised: 19 June 2015 / Accepted: 22 June 2015 # Springer-Verlag Berlin Heidelberg 2015

Abstract In the present work, a kinetic study of the pyrolysis process of algal biomass was investigated using a thermogravimetric analyzer. The weight loss was measured in nitrogen atmosphere. The samples were heated over a range of temperature from 473 to 1273 K at three different heating rates of 278, 283, and 303 K/min at a constant N2 flow rate of 30 mL/min. The results obtained from thermal decomposition process indicate that there are three main stages such as dehydration, active, and passive pyrolysis. The maximum decomposition of the biomass samples occurred between 493–923 K, owing to release of 60–65 % of total volatiles. In the DTG thermograms, the temperature at maximum weight loss rate changed with increase in heating rate. The activation energy of the pyrolysis reaction was determined by model free Kissinger-AkahiraSunose and Ozawa-Flynn-Wall methods. The CoatsRedfern method was used to obtain kinetic parameters such as activation energy, pre-exponential factor, and reaction order. Pyrolysis process was simulated with the obtained kinetic parameters and results are in good agreement with experimental data.

Keywords Algae biomass . TGA . Pyrolysis . Kinetics

* B. Satyavathi [email protected] 1

Chemical Engineering Division, CSIR-Indian Institute of Chemical Technology, Hyderabad 500007, India

2

Chemical Engineering Department, University College of Technology, Osmania University, Hyderabad 500007, India

1 Introduction Energy sources and its utilization determine the economic status and growth of developing countries over the world. Unlimited consumption of fossil energy sources because of availability and accessibility causes the depletion of the natural reserves. The effect from the use of these in turn has damaged the environment. Therefore, global research is oriented towards the alternate sources of energy for human welfare. In recent years, in particular, a lot of attention is concentrated towards biomass energy sources which are inexhaustible and environmentally friendly. The earth is the house for a variety of lignocellulosic biomass resources like wood and agricultural crops and marine biomass that have proved to be a storehouse for energy tapping. Algae biomass, a marine biomass, has the potential to contribute to the bioenergy pool worldwide. Researchers [1] have showed the importance of algae over other sources for energy conservation and management. The most promising feature of algae biomass is that its oil content which is approximately 1, 00,000 L/hectare tops the list when compared to various plant biomass including castor with oil yield of 1413 L/hectare, coconut 2689 L/hectare, palm 5950 L/hectare, safflower 779 L/hectare, soy 446 L/hectare, sunflower 952 L/hectare, jatropha 2420–2970 L/hectare, and pongamia pinnata (known as karanja in India) 5500 L/hectare. Moreover, due to the ease of growth and incomparable oil yield, algae biomass can be processed for liquid fuels to solve the dependence on fossil fuels. Optimization and kinetic study of biodiesel from algal oil extracted from Enteromorpha compressa, a macro algae species, has been reported earlier [2]. As the energy consumed in biodiesel production exceeded the energy output [3] from algal biodiesel by 53 % at optimized conditions, an efficient technique for the conversion of algae biomass to fuels needed

Biomass Conv. Bioref.

to be investigated. One of the alternatives is thermal treatment of algae biomass by pyrolysis into biooil. Production of biodiesel from algae has been hampered by energy intensive lipid extraction, dewatering, and processing. To overcome these problems, whole algae biomass can be processed under pyrolysis conditions to obtain biooil, rather than separately processing the lipid fraction. As the property of biooil from algal biomass is more suitable for fuel, many researchers have carried out pyrolysis of cultivated micro algae to obtain biooil. From the literature, it is observed that the yield of biooil varies with the type of micro algae species. The same can be observed from the work carried out by Miao and coworkers [4; 5] who had studied the fast pyrolysis of different micro algae for biooil and reported the biooil yield from Chlorella protothecoides (17.5 %), Microcystis aeruginosa (23.7 %), and heterotrophic C. protothecoides (57.9 %). It should be noted that biooil yields from pyrolysis of algal biomass with and without lipid extraction that depends on the composition of the biomass. In slow pyrolysis of microalgae without oil extraction [4], 24– 43 % biooil yield was reported. In another study, slow pyrolysis of algae biomass residue (algae with high lipid content) after oil extraction [5] gave a 31.1 % biooil yield. Biochar, a solid product from pyrolysis, is rich in mineral (ash) content due to the accumulation of minerals in algae biomass during its growth. The presence and concentration of mineral and trace elements in algae biomass is due to its biosorption properties and the environment where the biomass has been cultivated. Depending on the mineral and trace elemental composition profile of algae biomass, different elements can be extracted from the solid biochar or ash for various applications in the industry. Currently, research on macro algae is gaining attention for biofuel production as macro algae have numerous advantages including higher mass productivity and no need for internal transport of nutrients and water. Therefore, the present work gives an insight into the behavior of macro algae to thermal treatment under pyrolysis conditions at different heating rates. Thermal behavior of various biomasses is different from each other. Thus, a comprehensive knowledge of the pyrolysis mechanism and kinetics of different algae biomasses is required. The kinetics of pyrolysis is complex due to the simultaneous variations in the physical and chemical properties of the biomass. Pyrolysis is a promising technique for effectively converting lignocellulosic and algae biomass to liquid product at temperatures around 773–873 K under fast pyrolysis conditions [6]. Research on mixed algal residue as feedstock is scarce; therefore, the condensable volatile mass loss is studied to get an insight into the thermal degradation behavior. In the present work, algae biomass collected from a local lake, Nacharam Lake, located in Hyderabad, India, is

processed under pyrolysis conditions using a thermogravimetric analyzer.

2 Materials and methods 2.1 Materials Algae (macro) were collected from Nacharam Lake (fresh water lake) in Hyderabad, India. The composition of algae biomass can vary from batch to batch and for the harvesting season. Therefore, algae from the same batch were used in all the experiments. The solid content is cellulose, hemicelluloses, polysaccharides, proteins, lipids, chlorophylls, and other organic and inorganic compounds.

2.2 Methods 2.2.1 Thermogravimetric analysis Thermogravimetric analysis (TGA) is a new method to obtain the composition of lignocellulosic biomass [7]. TGA has been used to obtain the optimum temperature for pyrolysis of any type of biomass feed stock. Thermogravimetric analysis of algae biomass residue was performed in a Mettler Toledo TGA/SDTA 851e, Switzerland analyzer. TGA and DTA curves were obtained and sample weight loss as function of temperature was recorded continuously at three different heating rates at 278, 283, and 303 K/min ramping under nitrogen atmosphere with a flow rate of 30 ml/min to a final temperature of 1273 K.

2.2.2 Proximate analysis Of algal biomass for ash, volatile matter and fixed carbon content were carried out according to ASTM D-3175-07 and ASTM D-3174-04 standard methods [8]. Volatile matter content (VM) was determined by subjecting samples to pyrolysis conditions, N2 atmosphere at a flow rate of 80 ml/min, beginning at 383 K, and a 473 K/min ramp was provided till the temperature reached 1223 K and maintained for 7 min. Then the sample was cooled before recording the final weight. The difference of the final weight and initial dry weight of sample served as the VM content. The ash content was determined as the residue remaining after the following temperature program: 284 K/min increase from 383 to 1023 K, 276.5 K/min increased to 1223 K, isothermal hold at 1223 K for 120 min, and rapid cooling to 383 at 473 K/min. The ash method used zero-grade air at a flow rate of 80 ml/min.


2.2.3 Ultimate analysis That is, C,H,N,S,O elemental composition of algal biomass was carried using a CHNS Analyzer-ELEMENTAR Vario micro cube model. 2.2.4 Kinetics study Isoconversional kinetic methods were first developed and used for thermal degradation of polymers. The model free isoconversional kinetics methods and Coats-Redfern method were chosen to study the kinetics of pyrolysis reaction using the data obtained from TGA of algal biomass. International Confederation for Thermal Analysis and Calorimetry (ICTAC) kinetics committee recommendations for performing kinetic computations on thermal analysis data were followed to evaluate all the reliable kinetic parameters [9, 10]. Kinetic parameters including activation energy (E) and pre-exponential factor (A) were calculated from standard isoconversional kinetic methods, Kissenger-Akahira-Sunose (KAS), Ozawa-Flynn-Wall (OFW) methods, and model fitting Coats-Redfern (CRF) method. The extent of conversion is calculated from the activation energies obtained from KAS and OFW methods. The details of the methods considered for evaluation are presented below. The conversion in pyrolysis reaction is defined as: Conversion; α ¼

mi −mα mi −m f

ð1Þ

Approximate solution of the above Eq. (5) can be E so that the equaexpressed in terms of the quantity x ¼ − RT tion takes the form: Z α Z 1 AE x f expðxÞ AE dα ¼ PðxÞ ð6Þ dx ¼ 2 f ð α Þ βR x βR xi 0 The asymptotic expansion of the exponential integral P(x) obtained from a single integration by parts is as follows: expðxÞ 2! 3! 4! P ð xÞ ¼ 1 þ þ 2 þ 3 ……: ð7Þ x2 x x x

2.2.5 KAS method Kissinger-Akahira-Sunose method is an integral isoconversional method based on the following approximation for P(x), Eq. (7): P ð xÞ ¼

expðxÞ x2

ð8Þ

With the above approximation according to KAS method, Eq. (6) is transformed to β AE E ln 2 ¼ ln ð9Þ − Rg ðαÞ RT T From the slope − ER of the plot between ln Tβ2 vs 1/T, activation energy of the reaction can be calculated at various conversions (α).

mi, mα, and mf are initial, instantaneous, and final mass of the samples, respectively, and the rate of reaction is: 2.2.6 OFW method dα ¼ k ðT Þf ðαÞ dt

ð2Þ

where k(T) is independent of α and f(α) is independent of T. By substituting dt ¼ β1 dT and separation of variables in Eq. (2) gives Z α Z Tf 1 1 dα ¼ ð3Þ k ðT Þ dT f ð α Þ β Ti 0 From Arrhenius law, rate constant is given as a function of temperature: E k ðT Þ ¼ A:exp − ð4Þ RT

Z

Substitution of Eq. (4) in Eq. (3) gives: Z Tf α 1 E 1 dα ¼ dT A exp − RT β Ti 0 f ðαÞ

Ozawa-Flynn-Wall method is also an integral isoconversional method which takes Doyle’s approximation [11]. To achieve accuracy greater than 95 % in Eq. (7), an empirical equation for P(x) was proposed by Doyle: log e−x x−2 ¼ −2:315 þ 0:457x ð10Þ AE E logðβÞ ¼ log −2:315−0:457 ð11Þ Rg ðαÞ RT −E R

The plot of log(β) versus 1/T yields straight lines with slope at different α values.

2.2.7 CRF method [12] In Coats-Redfern method, rate of reaction is defined as:

ð5Þ

dα ¼ k ðT Þð1−αÞn dt

ð12Þ


Logarithmic form of the solution of above equation gives CRF equation for n≠1: ! 1−ð1−αÞð1−nÞ AR 2RT E ln 1− ð13Þ − ¼ ln βE E RT T 2 ð1−nÞ Coats-Redfern method is used to find out pre-exponential factor at different conversions by making use of activation energies obtained from Friedman, KAS, and OFW methods. Where α β T t f(α) g(α) e A n

conversion constant heating rate, K/min temperature, K time, minute kinetic model integral f(α) activation energy, J/mole pre-exponential factor, J/mole order

3 Results and discussion 3.1 Algae biomass characterization Algae biomass used in the present work is a carbohydrateyielding macro algae obtained from a fresh water lake. Proximate and ultimate characterization of algae biomass feedstock is shown in Table 1. The volatile matter in algae biomass processed in the present work was higher than that of corncobs and Polysiphonia elongata biomass. Ash and fixed carbon content of algae biomass were 19.89 and 20.53 %. From Table 1, it is evident that the fixed carbon content of lignocellulosic biomass is higher than that of the marine biomass. The composition of algae biomass varies from species type and by harvesting season [18]. The carbon content of algae biomass was lower than that of

Table 1

Chlorella vulgaris, Chlorococcum humicola, P. elongata, and Sargassum sp. but higher than corncob. Algae biomass sample and Sargassum sp. showed a similarity in its oxygen content, but the oxygen content was higher than the other biomass samples with an exception to corncob sample. 3.2 Thermogravimetric analysis Thermogravimetric experiments are carried out with 10 mg of algal biomass sample at three different heating rates (278, 283, and 303 K/min) with N2 flow maintained constant at 30 mL/min. Change in mass of sample with temperature at 278 K/min heating rate is shown in Fig. 1. The complete pyrolysis reaction proceeds in three stages with an initial dehydration step followed by decomposition of protein, hemicellulose, cellulose, and lignin decomposition [19]. In the initial dehydration step, free/bound moisture and volatile extractives of biomass are lost and carried away by the inert gas supplied. The dehydration step lasts up to a temperature of 473±10 K. Most of cellulose and hemicellulose degradation starts at above 473 K [7] and for proteins it is 573 K. Decomposition of carbohydrates and proteins continued up to 873 K temperature and above 873 K; a slight steep weight loss observed is due to the decomposition of biochar formed, shown in Fig. 1. The vigorous mass loss observed in the temperature that ranges 493–923 K in the thermal treatment of algae biomass can be considered as the active pyrolysis zone. From the thermogravimetric analyses, the final weights obtained are in the range of 46–49 % for the three heating rates at the end of pyrolysis stage. As is evident from Fig. 2, the heating rate influences the thermal degradation path. At lower heating rates, the samples reach the peak temperature at a higher time. The decomposition temperatures were shifted to lower values with respect to decrease in heating rate as the sample was exposed to heat treatment for longer time and vice versa [15].

Proximate and ultimate analysis of algae biomass and some other biomass samples

Sample

Algae biomass Chlorella vulgaris Chlorococcum humicola Corncob Polysiphonia elongata Sargassum sp. NA not available

Volatile mater (%)

59.57 55.37 57.57 55.12 54.49 49.08

Fixed carbon (%)

20.53 34.35 14.68 13.38 14.47 10.30

Ash

19.89 10.28 27.75 31.50 31.03 40.61

Element (%)

Reference

C

H

N

S

O

26.87 47.84 33.16 22.49 35.81 26.70

3.83 6.41 5.58 3.30 5.93 4.23

1.76 9.01 4.80 0.51 6.86 1.35

0.14 1.46 2.42 0.02 NA 0.19

67.54 25.00 27.24 73.68 51.40 67.53

Present work [13] [14] [15] [16] [17]


Fig. 1 TGA and DTA curves of algae biomass at 5 °C/min heating rate

3.3 Kinetics study The model free KAS, OFW methods and, model fitting CRF method were used to evaluate the kinetic parameters for algae biomass pyrolysis. Methods were employed at heating rates 278, 283, and 303 K/min, for conversions ranging from 0.1 to 0.9, to determine the change in activation energy with conversion during the thermal devolatilization process. The mass loss as a function of temperature, conversion, and heating rate using KAS and OFW methods are shown in Fig. 3a, b, respectively. The activation energies (E) and regression coefficients (R2) are given in Table 2. Regression coefficients were in the range 0.95 to 1 for all correlated lines shown in Fig. 3a, b and Table 2 and can be concluded that the methods resulted in reliable activation energies. The activation energy from KAS and OFW methods can be calculated from the slope of the straight line in the plot of ln(β/T2) vs. 1/T (Fig. 3a) and log(β) vs. 1/T (Fig. 3b), respectively. The activation energies determined by KAS and OFW methods were in good agreement except with a difference of ±2 in the value (Fig. 4). In all the methods used for evaluation,

Fig. 3 Plots for activation energy calculation at various conversions by a KAS method and b OFW method

the variation of activation energy with progressive conversions was following the same trend. The lowest activation energy was observed at the very initial stage of dehydration followed by an increase due to the high energy requirement for degradation of hemicellulose and cellulose. It is also evident from the average activation energies from KAS and OFW methods; average activation energy was increased from Table 2 Activation energies and regression coefficients determined by Friedman, KAS, and OFW methods

Fig. 2 Mass loss of algae biomass with heating rates controlled at 278, 283, and 303 K/min

Conversion,

KAS method

OFW method

Α

E (KJ/mol)

R2

E (KJ/mol)

R2

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

98.82 135.05 129.77 128.63 117.92 104.00 119.75 202.0.67 190.97

0.9785 0.9824 0.9961 0.9876 0.9857 0.9863 0.9959 0.9999 0.9999

102.39 137.27 132.60 131.88 122.48 110.46 127.24 207.50 196.93

0.9817 0.9844 0.9966 0.9382 0.9740 0.9740 0.9999 0.9999 0.9950


The kinetic parameters obtained were used to simulate the pyrolysis process of algae biomass. Experimental and simulated data were in good agreement (Fig. 5). The average activation energy in the present work was compared with that obtained from other biomass (Table 3) including C. vulgaris [13], C. humicola [14], corncob [11], P. elongata [16], and Sargassum sp [17]. The activation energies were different for different biomass due to the differences in composition of biomass and experimental conditions employed. After poly

a Fig. 4 Comparison of activation energies from KAS and OFW methods

b

1 0.8 Conversion

100.6 KJ/mol at α=0.1 to 131.02 KJ/mol at α=0.3. Increase in activation energy till the initial 0.3 conversion is because of the requirement of startup energy for degradation of alga biomass constituents. A decrease in activation energy was observed after 0.3 conversion that lasted up to 0.6 conversion followed by a rise till the constant mass loss. The overall mean activation energy values from KAS and OFW methods were 136.4 and 140.9 KJ/mol. The kinetic parameters including activation energy, preexponential factor, and reaction order were determined from CRF method based on CRF equation. Kinetic factors at three different heating rates from CRF method are shown in Table 3. Activation energies increased with an increase in heating rate. Correlation coefficients for CRF plots were greater than 0.99 for all heating rates with the reaction order (n) of 11. Activation energies evaluated from CRF equation were in consistency with those of KAS and OFW methods except with a very small difference. Although it is known, the kinetic parameters, pre-exponential factor, and order of reaction, obtained from the method, have no physical meaning which can serve as fitting parameters, but the estimations are useful in better understanding of the thermal degradation operation. The results are good as working design model.

0.6 α at β=283 K/min

0.4

α from model

0.2 0 450

550

650

750

850

950

Temperature, K

c

Table 3 Kinetics parameters for algae biomass pyrolysis by CoatsRedfern method Heating rate, K/min n

E, KJ/mol A

R2

278

11 123.90

2.0777×1011

0.9968

283

11 130.00

5.1528×1011

0.9979

303

11 134.20

15.8345×1011 0.9944

Reference

Average

129.30

Present work

Chlorella vulgaris

332.60

[13]

Chlorococcum humicola 189.99

[14]

Corncob

254.11

[11]

Polysiphonia elongata

121.36

[16]

Sargassum sp.

305.69

[17]

Fig. 5 Comparison of experimental and simulated data from CRF equation at heating rate a 278 K/min, b 283 K/min, and c 303 K/min


elongate sample, lower activation energy was observed for the pyrolysis of algae biomass compared to other biomass samples. This can be attributed to higher volatile content and low fixed carbon in the biomass sample. From the results, it can be concluded that the thermal behavior and pyrolysis kinetics were mainly influenced by the composition of the biomass sample.

4.

5.

6.

7.

4 Conclusions The introduction of algae biomass in bioenergy production depends on understanding of conversion mechanism. Thermal treatment of algae biomass is the best alternative over other processing technologies. Thermogravimetric analysis is a novel technique used in the present work to obtain the thermal behavior of algae biomass under pyrolysis conditions. Pyrolysis is a complex reaction process composed of degradation of protein, hemicelluloses, cellulose, and lignin in different temperature zones. Multistep decomposition process resulted variation in activation energy with progressive conversion. Volatile matter content of biomass sample had the most significant effect on the activation energy. The kinetic data obtained in the present work can be used to design and develop pyrolysis system for pilot scale operation.

8. 9.

10.

11. 12. 13.

14. 15.

References 16. 1.

2.

3.

Suganya T, Nagendra NG, Renganathan S (2013) Production of algal biodiesel from marine macroalgae Enteromorpha compressa by two step process: optimization and kinetic study. Bioresour Technol 128:392–400 Dassey AJ, Hall SG, Theegala CS (2014) An analysis of energy consumption for algal biodiesel production: comparing the literature with current estimates. Algal Res 4:89–95 Miao X, Wu Q (2004) High yield biooil production from fast pyrolysis by metabolic controlling of chlorella protothecoides. J Biotechnol 110:85–93

17.

18.

19.

Grierson S, Strezov V, Ellem G, Mcgregor R, Herbertson J (2009) Thermal characterization of micro algae under slow pyrolysis conditions. J Anal Appl Pyrolysis 85:118–123 Pan P, Hu C, Yang W (2010) The direct pyrolysis and catalytic pyrolysis of Nannochloropsis sp. residue for renewable biooils. Bioresour Technol 101:4593–4599 Trinch TN, Jensen PA, Dam-Johansen K, Knudsen NO, Sorensen HR, Hvilsted S (2013) Comparison of lignin macroalgae, wood, and straw fast pyrolysis. Energy Fuel 27:1399–1409 Carrier M, Loppinet-Serani A, Denux D, Lasnier JM, HamPichavant F, Cansell F, Aymonier C (2011) Thermogravimetric analysis as a new method to determine the lignocellulosic composition of biomass. Biomass Bioenergy 35:298–307 Keri B, Cantrell, Martin II HJ, Kyoung SR (2010) Journal of ASTM International 7: No. 3:Paper ID JAI102583 Vyazovkin S, Wight CA (1999) Model-free and model-fitting approaches to kinetic analysis of isothermal and nonisothermal data. Thermochim Acta 340–341:53–68 Vyazovkin S, Burnham AK, Criado JM, Pérez-Maqueda LA, Popescu C, Sbirrazzuoli N (2011) ICTAC Kinetics Committee recommendations for performing kinetic computations on thermal analysis data. Thermochim Acta 520:1–19 Doyle CD (1965) Series approximations to the equations of thermogravimetric data. Nature 207:290–291 Coats AW, Redfern JP (1964) Kinetic parameters from thermogravimetric data. Nature 201:68 Chen C, Ma X, He Y (2012) Co-pyrolysis characteristics of microalgae Chlorella vulgaris and coal through TGA. Bioresour Technol 117:264–273 Kirtania K, Bhattacharya S (2013) Pyrolysis kinetics and reactivity of algae-coal blends. Biomass Bioenergy 55:291–298 Yuan T, Tahmasebi A, Yu J (2015) Comparative study on pyrolysis of lignocellulosic and algal biomass using thermogravimetric analysis and a fixed bed reactor. Bioresour Technol 175: 333–341 Ceylan S, Topcu Y, Ceylan Z (2014) Thermal behaviour and kinetics of alga Polysiphonia elongata biomass during pyrolysis. Bioresour Technol 171:193–198 K i m S S , Ly H V, K i m J , C h o i JH , Wo o H C ( 2 0 1 3 ) Thermogravimetric characteristics and pyrolysis kinetics of alga Sagarssum sp. biomass. Bioresour Technol 139:242–248 Soriano EM, Fonseca PC, Carneiro MAA, Moreira WSC (2006) Seasonal variation in the chemical composition of two tropical seaweeds. Bioresour Technol 18:2402–2406 Kok MV, Ozgur E (2013) Thermal analysis and kinetics of biomass samples. Fuel Process Technol 106:739–743