thermogravimetric analysis and isoconversional kinetic study of...

Thermogravimetric Analysis and Isoconversional Kinetic Study ofBiomass Pyrolysis Derived from Land, Coastal Zone, and MarineJie Li,† Yingyun Qiao,†,§ Peijie Zong,† Chengbiao Wang,† Yuanyu Tian,*,† and Song Qin*,‡

†State Key Laboratory of Heavy Oil Processing, China University of Petroleum (East China), Qingdao 266580, China‡Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264000, China

ABSTRACT: Three types of biofuels derived from land, coastal zone, and marine were pyrolyzed in a thermogravimetricanalyzer from room temperature to 1000 °C under different heating rates (50, 80, and 100 °C/min), and three isoconversionalmathematical models were established to analyze the kinetic properties of biomass. The results show that the pyrolysis processof biomass derived from different distributions included three main stages: drying and preheating stage, volatile matterevaporation stage, and carbonization stage, whereas the pyrolysis behavior of the marine seawater Spirulina (Sp) biomass issomewhat different from that of land [corn stalks (Cs)] and coastal biomasses [reed (Re)], stemming from the inherentdifference in their compositions. Cs and Re species have advantages over microalgae in terms of the difficulty of volatile mattersreleasing, whereas the pyrolysis process of Re and Sp is faster than Cs because of the catalysis of its high salt tolerance. Theheating rate has a significant effect on the performance of devolatilization profiles and maximum weight loss rate, regardless ofthe biomass type from different regions. The dynamics analysis indicates that Sp species is preferable to Cs and Re in terms ofthermochemical conversion because of the lower apparent activation energy for total conversion. From the verification test, weconcluded that the simulations for Friedman models presented a good agreement with the experimental conversions calculatedat three different heating rates for Cs, Re, and Sp pyrolysis, and that the kinetic simulation of the weight loss curve and kineticparameters obtained by pyrolysis of three biomasses is reasonable and effective.

1. INTRODUCTION

Biomass has a long history as a major energy source and isconsidered to be an approximately carbon neutral renewableand abundant energy resource.1 Biomass energy is mature intechnology and widely used. It plays an important role incoping with global climate change, contradiction betweenenergy supply and demand, and protecting the ecologicalenvironment.2 It is abundantly available, currently providesmore than 10% of the global energy supply, and ranks amongthe top four energy sources in terms of world final energyconsumption in 2011.3

Land biomasses are dominated by lignocellulose. Lignocel-lulosic biomass is believed to be the most promising fuelfeedstock, and its major constituents are polymeric carbohy-drates.4 They are usually composed of three majorconstituents: cellulose (42−49 wt %), hemicellulose (16−23wt %), and lignin (21−39 wt %).5 Land biomass selected cornstalks as experimental materials. In 2018, corn was cultivated inan area of approximately 532 million mu in China, and thetotal output was 215.891 million tons. The dry weight of cornstalk was higher than 259.2 million tons. The coastal zone is anarrow interface zone between marine and terrestrial areas.6 Itprovides rich agricultural lands, is typically held as a publicheritage, and connects land and sea.6 However, coastal wetlands and coastal agricultural zones are seriously affected bythe salinity issue. Coastal wetlands comprise various habitattypes, including salt marshes, mangroves, seagrasses, saltswamps, and sand dunes, because of their transitional situationbetween sea and terrestrial ecosystems, affected by salinity.7

Saline-alkali soils are a major area in the vicinity of seawater asin coastal regions. The crops living on saline-alkali soils have

high salt-tolerant content because salt inhibits plant photosyn-thesis, protein synthesis, and lipid metabolism.7 The commonreed (bulrush), Phragmites australis (Cav.) Trin. exSteud., isdescribed because it is one of the most widely distributed plantspecies on the planet, covering approximately 10 millionhectares, commonly found in coastal wetlands and along theupland edge of tidal marshes because of their higher salttolerance.8 Reed is an especially promising energy plant andchemical feedstock because it would lose its greenery andbecome dry under natural conditions throughout the late falland winter; this greatly reduces the cost of drying process.9

Marine biomass is a new form of energy in which marine plantsuse solar energy to store solar energy in the form of chemicalenergy.10,11 The main source of marine biomass is algae,including seawater microalgae and seaweeds. Seawater micro-algae have the characteristics of wide distribution, high oilcontent, strong environmental adaptability, short growth cycle,and high yield.12 It can be used to prepare biofuels (ethanol,biodiesel, fuel oil, or hydrogen) and has the effect of carbonemission reduction.13 At present, Qingdao has built the largestmarine microalgae industry production base in China. Theproduction will reach around 3000 tons in the year of 2020,and the annual output value will reach about 1 billion yuan. Asa typical representative of marine microalgae, seawaterSpirulina has high-efficiency photosynthesis and has higheryield than other marine microalgae.14,15

Received: January 31, 2019Revised: February 22, 2019Published: March 5, 2019

Article

pubs.acs.org/EFCite This: Energy Fuels 2019, 33, 3299−3310

© 2019 American Chemical Society 3299 DOI: 10.1021/acs.energyfuels.9b00331Energy Fuels 2019, 33, 3299−3310

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Pyrolysis has been popular in producing a fuel product sincethe late 1970s because of its advantages in its short process,strong adaptability, rapid response, high conversion rate, easycommercialization, and so on.16,17 Biomass is a highly complexpolymer with a complex pyrolysis mechanism.18 The pyrolysisprocess involves many serial and parallel chemical reac-tions.19,20 Thermogravimetric (TG) analysis (TGA) is animportant means of biomass pyrolysis kinetics.21 A TGanalyzer is widely used to generate mass loss of data for theestimation of apparent kinetic parameters of the thermaldegradation of biomass.22 The obtained thermal weight-losscurve is processed and deduced by a mathematical formula.The apparent activation energy of the decomposition reactioncan be estimated, and the decomposition reaction mechanismand influencing factors can be judged, which provide atheoretical basis for optimizing the reflection operation andbetter design of reactors. The reaction rate of pyrolysis isaffected by factors such as heating rate, temperature, andpyrolysis products. The heating rate is one of the importantparameters affecting pyrolysis reaction. In general, the heatingrate used for biomass pyrolysis can range from 0.1 to 100 °C/min because the mass transfer has an unwanted influence onTGA measurements when the heating rate is too high.23

Simultaneously, high heating rates may cause heat-transfereffects inside the biomass samples; thus, pure kinetic pyrolysismodels are not valid. Therefore, the kinetic study for above100 °C/min would lead to inaccurate results for kineticparameters. Additionally, biomass slow pyrolysis had beeninvestigated in numerous literature.24−26 Zhou et al.25 used aTG analyzer to study the pyrolysis kinetics of marinemicroalgae at a heating rate of 5, 10, 20, 30, and 40 °C/minin purified nitrogen. The results showed that the increasedheating rate also resulted in an increased total volatile matter.Wang et al.26 performed a pyrolytic kinetic of an agriculturalresidue feedstock, which is a mixture of plants (cotton, wheat,rich, and corn) stems at different heating rates (10, 20, and 30°C/min) with kinetic parameters such as an apparentactivation energy of 221.7 kJ mol−1 and a pre-exponentialfactor of 4.17 × 1016 s−1. More so, there is a research gap inpyrolysis behaviors and kinetic study of biomass pyrolysisunder 50−100 °C/min. In this study, we aim to fill this gapunder this range of heating rate (50, 80, and 100 °C/min) toevaluate pyrolysis kinetics of biofuels of different origin andcompare the fuel properties systematically.According to the law of mass conservation in chemical

reaction, the kinetic solution of biomass pyrolysis reaction issolved by an integral method or a derivative method.27 Manyscholars have proposed different methods for analyzing thepyrolysis kinetics of biomass; there are categorized into twotypes inductively: model-fitting and model-free ap-proaches.21,23,28 However, the use of model-fitting methodsyields a similar conclusion: almost any transfer function fitsexperimental data in a satisfactory manner at the expense ofestimating distinct kinetic parameter values. When usingmodel-free methods, it is possible to avoid the estimation ofuncertainty of the kinetic parameters brought by using themodel-fitting method.29,30 There are many model-free kineticmethods such as Friedman, Kissinger−Akahira−Sunose (KAS)method, and Ozawa−Flynn−Wall (OFW) method.20,31−33

The reaction mechanism function f(α) of the Friedmanmethod is considered to be fixed, whereas the OFWintegration method assumes that the reaction activation energy(Eα) is constantly changing throughout the reaction stage. The

Eα is calculated by model-free methods without consideringthe reaction mechanism function f(α). Therefore, it is usuallynecessary to analyze the decomposition curves under differentheating rates to obtain kinetic parameters such as Eα and ln A.The model-free procedure, which is only suitable for a singlereaction, is appropriate for the apparent activation energyestimation.34,35 Because the kinetic parameters calculated bythe Friedman model-free method have more simplicity andhigh accuracy; they are most commonly used in the pyrolysiskinetics of biomass and other polymers. Cai et al.23 reviewedthe overall procedure of processing TGA data for the kineticanalysis of corn-stalk pyrolysis by using the Friedman model-free method; results have shown that the effective activationenergies of corn-stalk pyrolysis vary from 148 to 473 kJ mol−1

when the conversion ranges from 0.05 to 0.85.Most available studies on the kinetics of biomass pyrolysis

have focused on a single type of land or coastal zone or marinebiomass.36−39 Only limited previous reports were available oncomparing the pyrolysis characteristics of land and marine(lignocellulosic and algal) biomasses.40,41 There is a researchgap in the pyrolysis behavior and kinetics of biomass derivedfrom land, coastal zone, and marine. In this article, corn stalks,reeds, and seawater Spirulina were used as samples, and thethermal characteristics of different biomass types and differentheating rates were analyzed to study the multiple effects ofreactant type and heating rate on the thermal reactionmechanism. Multiple typical isoconversional kinetic methodswere used to analyze the TG and differential TG (DTG) datato get more accurate kinetic parameters, including Firedmanmethod, FWO method, and KAS method.

2. MATERIALS AND METHODS2.1. Biomass Samples. Corn stalks (Cs) were obtained from the

farms in Tengzhou, Southwestern part of Shandong province, China.Yantai Institute of Coastal Zone Research, Chinese Academy ofSciences, provided high salt-tolerant plant reed (Re) samples; it wastaken at the wetland of the Yellow River Delta, Shandong province,China. The Institute of Oceanology, Chinese Academy of Scienceshas a commitment to provide the integrity structural seawaterSpirulina (Sp); it was taken at the marine microalgae breeding base,Shandong province, China. All biomass samples powdered (particlesize of ≈1 mm) were used as received and stored in a desiccator untilfurther analysis. The proximate analysis was calculated according tothe Chinese National Standards (GB/T 28731-2012),42 and theultimate analysis was measured by an Elementar AnalysensystemeGmbh (Vario MACRO cube, Germany). To investigate the differenceon the pyrolysis activity and the conversion between two types offuels, alkali and alkaline earth metallic (AAEM) species (Na+, K+,Mg2+, and Ca2+) contents were detected by inductively coupledplasma mass spectrometry (ICP−MS, 2030, Shimadzu Company,Japan).

2.2. TGA of Biomass Pyrolysis. The pyrolysis measurements forthe Re, Cs, and Sp species were conducted in a TG analyzer(NETZSCH Instruments, STA 449 F3 Jupiter, Germany) in acontinuous atmosphere of inert nitrogen (99.999 purity) at a flow rateof 100 mL/min to investigate the mass loss of biomass. The operationprocess of TGA was based as per the reported method.43 Threedifferent heating rates were applied to minimize the heat and mass-transfer effects in the calculations of kinetic parameters: 50, 80, and100 °C/min.

2.3. Determination of Conversion. Calculation method ofbiomass conversion rate (α) is

α =−−

m mm m

0 t

0 f (1)

Energy & Fuels Article

DOI: 10.1021/acs.energyfuels.9b00331Energy Fuels 2019, 33, 3299−3310

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where m0 is the sample mass in pyrolysis process when t = 0, m is thesample mass when t = t, and mf is the mass at the end of the process,respectively.2.4. Isoconversional Kinetics Analysis. The basic assumption

of the isoconversional kinetic methods is that the reaction activationenergy remains constant under different heating rates. There are manymethods for analyzing isoconversional solid-state kinetic data fromTGA currently. These methods can be divided into isothermal andnonisothermal methods, both reflected in Table 1. This article selectsthe representative kinetics methods of Friedman, FWO, and KAS tocalculate the kinetic parameters for typical biomasses.

2.4.1. Kinetic Theory. The pyrolysis conversation rate can beexpressed by the following typical equation

α α α= = − α

tk T f A

ERT

fdd

( ) ( ) exp ( )ikjjj

y{zzz (2)

where t is the reaction time, α is the conversion rate, A is pre-exponential factor, Eα is the apparent activation energy, and f(α) is thereaction equation, respectively. The three pyrolysis kinetic parametersA, Eα, and f(α) are often referred to as kinetic three factors, especiallythe reaction activation energy Eα, which is used to describe thepyrolysis reaction.

α α αβ

= =T t

tT t

dd

dd

dd

dd

1(3)

where dt/dT represents the reciprocal of the heating rate and 1/β,dα/dt is the isothermal reaction rate.Therefore, eq 2 can also be written as

αβ

αβ

α= = − α

tk T

fA E

RTf

dd

( )( ) exp ( )

ikjjj

y{zzz

(4)

2.4.2. Isothermal Method. It is assumed that the term ∫= αα

a

f0d( )

is

expressed as shown

∫ ∫α αα β

= = − αgf

A ERT

T( )d( )

exp da

T

T

0 0

ikjjj

y{zzz

(5)

The equations of the Friedman kinetic method can be obtained bytaking the natural logarithm of both sides of eq 5, as shown below.

β α α= [ ] − α

TAf

ERT

lndd

ln ( )ikjjj

y{zzz (6)

2.4.3. Nonisothermal Methods. The theory of the FWO method isthat ln β is linear with 1/T in the curves of the TG differential atdifferent heating rates. The effect of the heating rate on the peaktemperature of the TG differential curve is subjected to the FWOmethod equation

β = − −α αAERg a

ERT

ln ln( )

5.3311.052i

kjjjjj

y{zzzzz (7)

where g(a) is the integral form of the dynamic mechanism function.The KAS method is a differential thermal analysis method which is

used for solving the activation energy of biomass pyrolysis at differentheating rates. When the reaction rate reaches the maximum, the effectof the heating rate on the peak temperature of the TG differentialcurve is subjected to the following equation:

β = −α

α

TAR

E g aERT

ln ln( )2

Ä

ÇÅÅÅÅÅÅÅÅ

É

ÖÑÑÑÑÑÑÑÑ (8)

3. RESULTS AND DISCUSSION3.1. Sample Characteristics. The proximate, ultimate,

and component results of Cs, Re, and Sp are provided in Table2. Among the proximate analysis results of feedstocks, the

value of moisture for Re and Sp is higher than that for Cs; thisis mainly related to the environment in which the crops grow.The volatile matter content of the Sp was higher in comparisonwith Re and Cs. The coastal and marine samples used in thisstudy contain higher volatile matter compared to land biomass.Another point to focus on is the amount of ash; the ashcontent of the samples ranges from 2.30 to 11.15%; and Spshowed the lowest ash contents. This may be because of theabsence of any contamination during the growth of seawaterSpirulina. There are controversies in the role of ash now. Manyresearchers believed that the ash content can limit heat andmass transfers while producing all kinds of problems such asagglomeration, slagging, and fouling in boilers.44 However,some experts hold a view that inorganic elements in the ash isalso a good catalyst and contributed to the pyrolysis process.45

The AAEMs species have a significant enhancement for thebiomass pyrolysis. From Table 2(a), we found that Re has ahigher AAEM (mainly K+, Na+, Ca2+, and Mg2+) content thanSp and Cs. Simultaneously, Re contains more Na+ because ofthe growing regions. Wang et al.46 found that these inorganicelements can be present in biomass in numerous forms, such as

Table 1. Summary of Isoconversional Kinetic Methods

isothermal nonisothermal

standard FWOFriedman VyazovkinAIC KAS

Table 2. Property Analysis of Feedstocksa

(a)

parameter Cs Re Sp

Proximate Analysisb (wt %, ad. Basis)moisture 4.36 5.89 5.42ash 11.15 8.47 2.30volatile matter 71.85 72.12 78.41fix carbond 12.64 13.52 13.87

Ultimate Analysisc (wt %, daf. Basis)C 39.61 42.78 45.74H 5.83 5.17 6.71Od 50.70 50.51 36.94N 1.14 1.33 9.75S 1.15 0.21 0.86HHV (MJ/kg) 15.44 16.16 18.59

AAEM (mg/g)K+ 1.86 4.44 6.03Na+ 0.23 0.91 0.31Ca2+ 3.25 1.92 0.81Mg2+ 1.18 0.37 0.32

(b)

component analysis (wt %) Cs Re Sp

cellulose 65.25 43.05hemicellulose 8.65 30.68lignin 9.2 20.34extractsd 16.90 5.93 8.48crude protein 63.41crude lipid 7.58total carbohydrates 20.53

aCs: corn stalks, Re: reed, and Sp: seawater Spirulina. bDry-free basis.cDry ash-free basis. dCalculated by difference.



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a free ions and salts. Furthermore, covalent bonds betweeninorganic elements and the organic biomass structure are seenas well (e.g., proteins). The HHV value of Cs, Re, and Sp is15.44, 16.16, and 18.59 MJ/kg, respectively.For the ultimate analysis, the N concentration of Sp was

higher in comparison with that of Cs and Re most likelybecause of higher protein content in microalgae. For allfeedstocks, the content of oxygen ranged from 36.94 to50.70%. According to Table 2(b), the main components inland and coastal biomass are cellulose, hemicelluloses, andlignin, whereas microalgae components are mainly proteins,lipids, and carbohydrates. Cs was primarily composed of

hemicellulose (65.25%), cellulose (8.65%), and lignin (9.20%).Moreover, the organic components in Re were distributed onthe order of hemicellulose (43.05%), cellulose (30.68%), andlignin (20.44%). Last, the organic matter in Sp was primarilycomposed of crude proteins (63.41%), carbohydrates(20.53%), and crude lipids (7.58%).

3.2. TG/DTG Analysis. 3.2.1. Thermal DegradationProcess. The pyrolytic decomposition curves of Cs, Re, andSp under different heating rates (50, 80, and 100 °C/min) areshown in Figure 1. The pyrolysis characteristic parameters (Ti:initial temperature, Tm: maximum degradation rate temper-ature, Tf : final temperature, and Rmax: maximum mass loss

Figure 1. Weight loss curves (a) and rate of weight loss curves (b) of Cs, Re, and Sp at different heating rates against temperature.

Table 3. Pyrolysis Performance Index for Biomass at Different Heating Ratesa

Cs Re Sp

parameters 50 80 100 50 80 100 50 80 100

Ti (°C) 232.4 261.5 266.0 230.7 239.9 249.1 212.4 213.8 220.8T1 (°C) 344.1 290.1 305.7 325.5 314.2 338.1Tm (°C) 400.0 414.0 408.0 362.8 371.2 378.4 360.8 370.6 386.3R1 (%/min) 60.5 63.9 175.8 301.2 64.6 118.1Rmax (%/min) 95.4 227.4 326.4 84.8 217.6 345.2 66.5 126.8 189.0Tf (°C) 465.9 482.5 524.7 477.2 508.5 511.5 547.3 557.2 595.4ΔT1/2 (°C) 86 113 135.6 106.1 117.4 131.5 114.5 122.9 130.0Di (10

6% min−1 °C−3) 11.9 18.6 22.2 9.6 20.8 27.9 7.6 13.0 17.1aCs: corn stalks, Re: reed, and Sp: seawater Spirulina.



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rate) at different heating rates from Figure 1 are shown inTable 3. To comprehensively evaluate the pyrolysis character-istics and difficulty of each biomass species pyrolysis, Wu etal.47 proposed the devolatilization index Di to characterize theperformance of volatile matters releasing. The calculationmethod is as follows

= ΔD R TT T/i max i m 1/2 (9)

where Rmax is the maximum weight loss rate; Tm is thetemperature at which the maximum weight loss rate is reached,Ti is the initial precipitation temperature of the volatile matter;and ΔT1/2 is the half width.In Figure 2, the trends of pyrolysis of various biomasses were

consistent and with similar process. Pyrolysis is mainly dividedinto three stages: the weight loss in the first stage is mainlybecause of the precipitation of free water and internalrecombination in the biomass, releasing small-molecular-weight compounds such as H2O, CO, CO2, and so on; thequality change at this stage is not large.40 Sp loses weight up to16% at this stage compared to that of Cs and Re. In the secondstage, the proportion of weight loss is the largest, which is themain stage of the pyrolysis process. In the interval of 350−420°C, the weight loss rate reaches the maximum. At this stage,the chemical reaction of generated small molecules of gas andcondensable volatiles of macromolecules is mainly caused byparallel or continuous decomposition of macromolecules suchas cellulose, hemicellulose, and lignin.38 The third stage is thefinal carbonization stage. It can be seen from Figure 2 that theweight loss curve tends to be gentle after 600 °C, whichindicates that the pyrolysis of cellulose and hemicellulose isbasically finished at this stage, mainly because of thedecomposition of carbonaceous materials retained in thechar residues and lignin. Lignin is difficult to be decomposedbecause its component contains benzene ring; therefore, itcovers a wider temperature range of 160−900 °C.24 As thetemperature increases, the internal volatiles slowly precipitate

and gradually form a loose porous structure, which lasts for along time.48

The first stage of the three biomass pyrolysis was basicallysimilar. The differences in the second phase of the threebiomass pyrolysis are mainly reflected in the following aspects:first, the main weight loss interval of Sp is between 200 and600 °C, whereas it is slightly smaller for Cs and Re. Also, inthis fast weight loss interval, the weight loss rate of Cs and Reis significantly larger than Sp, which may be because of thedifference in the composition of different biomasses. This isreflected in Table 2(b). Additionally, Cs and Re samplescontain significantly higher final yields compared to the Spbiomass, most likely because of the presence of ash content.Second, there are differences in the Tmax. Among them, Csreaches the maximum weight loss rate at around 400 °C,whereas Re and Sp reach the maximum weight loss rate ataround 370 °C. This phenomenon may be because of theactions of intrinsic metals (especially, K and Na) of inorganicsas catalysts during biomass pyrolysis.49 Re and Sp crops livingon the coastal zone and marine have a high salt tolerancebecause the salt inhibits plant photosynthesis, protein syn-thesis, and lipid metabolism. Finally, before reaching themaximum pyrolysis rate, Cs and Re showed smaller shouldersat 50 and/or 80 °C/min, which is generally believed to becaused by hemicellulose pyrolysis and the relative content ofdifferent components in the feeds.37 In the fast weight-lossinterval, as the temperature increases, the hemicellulose beginsto decompose. Because of the relatively low hemicellulosecontent of Cs, the decomposition rate is faster. After thehemicellulose was decomposed, the weight loss rate wouldhave a slight fall. As the temperature rises, the weight loss ratebegins to increase again and thus appears at the shoulder, asshown in the figure. However, for Sp, the small shoulders weremainly assigned to the thermal cracking of carbohydrates(monosaccharides and partial disaccharides) and lipids in theseawater Spirulina.20,50,51

Figure 2. Extent of conversion curves for the devolatilization process of Cs, Re, and Sp at different heating rates. Cs: corn stalks, Re: reed, and Sp:seawater Spirulina.



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Shift our focus on Table 3, the larger Di value, the bettervolatile precipitation performance. From Table 3, we canconclude that Cs and Re samples have significantly highermaximum weight loss rate compared to microalgae biomass Sp,and the temperature required for Cs to reach the maximumweight loss rate is higher than the other two biomasses. Fromthe pyrolysis reaction index (Di), when the heating rate is 50°C/min, the value of Di is Cs > Re > Sp, whereas Re > Cs > Spwhen the heating rate is 80 and 100 °C/min. The above tworesults concluded that the performance of volatile mattersreleasing Cs and Re is easier than Sp. In this study,lignocellulosic biomass has an advantage over microalgae interms of the difficulty of volatile matters releasing.3.2.2. Effect of Heating Rate. The effect of heating rate on

the pyrolysis properties of feedstocks is still referred to Figure1 and Table 3. The heating rate has a significant effect on thepyrolysis rate. The higher the heating rate, the faster thepyrolysis. The general shift to higher temperatures occurs asthe increase of the heating rate. As shown in the TG profiles ofSp, an increase in the heating rate from 50 to 100 °C /min ofthe sample causes displacement of pyrolysis conversion tohigher temperatures. In other words, to achieve the same massloss (TG wt %), the higher the heating rate, the higher thecorresponding pyrolysis temperature. Corresponding to theDTG profiles, the whole weight losses were shifted to highertemperature zones (50 °C/min to 360.8 °C; 80 °C/min to370.6 °C; and 100 °C/min to 386.3 °C). This trend alsopresents in other species. For the reason of this phenomenon,we believe that the heating rate generally has positive andnegative effects on the feedstock samples. As the heating rateincreases, the response time of the feedstock particles pyrolysisrequired temperature will be shorter, which facilitates to thepyrolysis process. However, the shorter reaction timeexperienced by the feedstock causes the decrease of reactiondegree, and the difference of temperature between the insideand outside of the feedstock particle becomes large, which maycause thermal hysteresis. Moreover, the pyrolysis products

adhering to the outer layer of the feedstock particles had lesstime to diffuse, which affected the process of internal pyrolysis.As a result of the superposition of the positive and negativeeffects, the curve moves toward the high temperature side.39

The heating rate also affects the maximum mass loss rate offeedstocks. At the lower heating rates, the maximum rates ofmass losses were relatively low. When the heating rate wasincreased, the maximum rates of mass losses also increased.This phenomenon reflected more intuition in Table 3. This ismainly because of whether the components in the biomass aresimultaneously decomposed. The components at a low heatingrate undergo pyrolysis separately, whereas at higher heatingrate, the reaction was completed instantly and differentcomponents were decomposed almost simultaneously.36 Onthe other hand, another effect of the heating rate is that highheating rates resulted in the formation of narrower sharppeaks.52

3.3. Isoconversional Kinetic Calculation. The isocon-versional kinetic calculation results acquired from TGA weredetailed to calculate the kinetic parameters for a given value ofconversion, α. The activation energy (Eα) was receivedexploiting Friedman, KAS, and FWO methods. Usually, therange value of conversion, α, is estimated from the α−T curves.The relationship of conversion α versus temperature of Cs, Re,and Sp at different heating rates is shown in Figure 2. Todetermine the isoconversional kinetic parameters, we chose amain reaction stage and a corresponding value of α ranging0.1−0.9 for Cs, 0.1−0.85 for Re, and 0.1−0.8 for Sp.

3.3.1. Isothermal Method Analysis. On the basis of theFriedman method, the linear plots on ln(β dα/dt) versus 1/Tof Cs, Re, and Sp are shown in Figure 3. The apparentactivation energy (Eα) obtained from the slope (−1000/RT)from Figure 3 is calculated in Table 4. It can be seen that thecalculated regression coefficient (R2) obtained by the Fried-man method derived from different regions of land, coastal,and marine biomass ranges from 0.908 to 1.000, whichindicates that the Friedman method achieved a goodness

Figure 3. Arrhenius plots of Friedman method at selected α values for Cs, Re, and Sp during different heating rates. Cs: corn stalks, Re: reed, andSp: seawater Spirulina.



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fitting effect. Owing to the large errors that would take placeunder high conversion (α = 0.85 and 0.90) by using theFriedman method, we chose a conversion rate range of 0.10−0.80 when calculating the activation energy.From Table 4, we inferred that (1) the value of Eα acquired

from the thermal decomposition of lignocellulosic biomassspecies and microalgae varied slightly. (2) The value of Eα

depends on the variety of α. (3) For Cs and Re, Eα graduallyincreases first, then slightly decreases, and finally increasessharply, whereas for Sp, Eα presents the tendency of graduallyincreasing continuously. (4) For Cs samples, Eα graduallyincreases from 206.4 to 262.2 kJ mol−1 when α increases from

0.1 to 0.35. Eα gradually decreases from 262.2 to 149.1 kJmol−1 in the α range between 0.35 and 0.80. Finally, Eα sharplyincreases from 209.9 to 386.6 kJ mol−1 when α was located at0.85 and 0.90. (5) For Re, the variety of the Eα value was in asimilar tendency, and the difference is that the demarcationpoint α = 0.4, respectively. (6) For Sp, Eα gradually increasesfrom 81.2 to 288.8 kJ mol−1 when α increases from 0.1 to 0.8.(7) A lower activation energy for the Sp pyrolysis was observedin this study. (8) The mean apparent value of Eα in conversionranges between 0.1 and 0.80 for Cs, Re, and Sp is 215.3, 237.4,and 190.2 kJ mol−1, respectively. These values were close tothe aforementioned literature.26,39,53

Cs and Re were typical lignocellulosic biomasses, andcellulose, hemicellulose, and lignin are the main components.The apparent activation energies varying with conversionsignificantly maybe attributed to the Eα of individual thermaldecomposition of the above biopolymer components.54 Thepyrolysis of biomass exists in multistep complex processes ofthose components. At the initial stage of the reaction, the Eα

increases with the α, which may be because of the release ofinherent moisture and other low-temperature decomposingcomponents. When the conversion rate is further increased,hemicellulose and cellulose starts to undergo pyrolysis becauseof the high heating rate. After α = 0.40, the Eα decreases withthe conversion increasing until about α = 0.80, which ispossibly attributed to the pyrolysis of cellulose crystal, whosedecomposed activation energy is decreased with the con-version increasing. Other similar downtrend results wereobtained from the pyrolysis of pine wood, rice husk, andbamboo (Bambusa chungii).24 Such a declining Eα demon-strates that there were mechanism changes happening in thecorresponding conversion range because of the high heatingrate.55 Because of the complexity of the biomass components,the pyrolysis reactions have both chemical bond cleavagereactions and accompanying processes of free radicalformation, reaction, and disappearance. When the chemicalbond breaks to form a radical, the radical reaction is relatively

Table 4. Result of Eα and Correlation Coefficients for Cs,Re, and Sp Obtained by the Friedman Methoda

Cs Re Sp

αEα

(kJ/mol) R2Eα

(kJ/mol) R2Eα

(kJ/mol) R2

0.10 206.4 0.996 182.3 1.000 94.0 0.9450.15 232.2 0.995 195.1 0.950 123.7 0.9990.20 235.2 0.967 199.0 0.971 135.4 0.9860.25 244.7 0.953 207.1 0.979 150.1 0.9980.30 257.2 0.948 234.6 0.962 168.0 1.0000.35 262.2 0.935 291.9 0.939 171.6 1.0000.40 259.5 0.951 324.8 0.924 174.3 0.9990.45 252.1 0.956 315.8 0.939 178.2 1.0000.50 241.5 0.976 292.0 0.918 180.2 1.0000.55 220.3 0.995 268.8 0.912 193.1 0.9990.60 198.1 1.000 243.9 0.908 211.6 0.9990.65 173.0 0.996 218.2 0.923 239.8 0.9990.70 152.4 0.992 201.0 0.927 268.2 0.9980.75 144.9 0.981 194.6 0.930 275.8 1.0000.80 149.1 0.974 191.9 0.940 288.8 0.9690.85 209.9 0.949 316.8 0.9410.90 386.6 0.954

aCs: corn stalks, Re: reed, and Sp: seawater Spirulina.

Figure 4. Arrhenius plots of FWO method at selected α values for Cs, Re, and Sp during different heating rates. Cs: corn stalks, Re: reed, and Sp:seawater Spirulina.



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easy and does not require a high reaction activation energy.Finally, sharp increases of Eα mainly caused the pyrolysis ofhigher thermal stability of lignin and higher ordered cellulose.The decomposition reaction of biopolymer components

(carbohydrates, proteins, and lipids) is still the reason of thevariety of microalgae Eα value. Bach and Chen56 studied thepyrolysis of microalgae biomass and component models,showing that the decomposition of the carbohydrates, proteins,and lipids allocated in the temperature ranges of 110−420,210−310, and 150−515 °C, respectively. Accordingly, thevariety of the value of Eα can be attributed to the successivethermal cracking of the carbohydrates, lipids, and proteins.

3.3.2. Nonisothermal Method Analysis. The Arrheniusplots of FWO and KAS methods at selected conversion αvalues for Cs, Re, and Sp during different heating rate areshown in Figures 4 and 5, respectively. Then, the apparentactivation energy values are calculated by the slopes (−1.052Eα/R) and (−Eα/R) corresponding to various conversion ratesof Cs, Re, and Sp using FWO and KAS methods, as shown inFigure 6. From the results involved in Figures 4−6, we canobtain that the perfect linear relationship for wholeconversions, which indicated that biomass samples fromdifferent sources achieved high-fitting results using both

Figure 5. Arrhenius plots of the KAS method at selected α values for Cs, Re, and Sp during different heating rates. Cs: corn stalks, Re: reed, and Sp:seawater Spirulina.

Figure 6. Eα as a function of conversion by FWO, KAS models for Cs, Re, and Sp. Cs: corn stalks, Re: reed, and Sp: seawater Spirulina.



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FWO and KAS kinetic methods. Afterward, the values ofactivation energy calculated by both methods varied slightly.The apparent activation energy of Cs is about 186.2−299.4

and 174.4−287.9 kJ mol−1 for FWO and KAS, respectively.The apparent activation energy of Re from the coastal zone isabout 204.7−314.5 and 193.7−304.2 kJ mol−1 for FWO andKAS, respectively. Whereas the apparent activation energy ofSp is about 141.6−277.1 and 132.6−277.2 kJ mol−1 for FWOand KAS, respectively. On the basis of this, we can observe thatthe values of activation energy calculated by both methodsvaried slightly at the same conversion. The reasons of thesevariation may be attributed to the multistep reactions in thedecomposition of complex components at each conversion.24

For the main thermal cracking devolatilization process,studies have shown that the changes in weight loss of thefeedstocks were mainly caused by the evolution of fragment,resulting in the breaking of chemical bond from the organicmatter.51 Therefore, the mean value of Eα for the entire processcan reflect the difference in composition and structure of eachspecies. The mean values of Eα for Cs, Re, and Sp speciescalculated by the three kinetic methods (Friedman, FWO, andKAS) throughout the reaction are listed in Table 5. For land

biomass Cs, the deviation from the highest Eα calculated byFWO with respect to the Friedman and KAS methods were22.9 and 11.6 kJ mol−1, respectively. For the coastal zonebiomass Re, the deviations were 17.3 and 10.5 kJ mol−1. The

Eα obtained by the FWO method was significantly higher thanthose from the Friedman and KAS methods. However,concerning the Sp, it can be observed that FWO and KASmethods (194.2 and 185.8 kJ mol−1, respectively) areconsistent with the Friedman method (190.2 kJ mol−1),which is attributed in part to the differences in the chemicalcompositions and structures of the Cs, Re, and Sp.52,53 On theother hand, differences in pyrolysis conditions and assessmentmethods used for different biomasses may also havecontributed to the observed variability.54 Concerning thecomparisons between the kinetic analysis of microalgaesamples and lignocellulosic biomass ones, it can be observedthat lignocellulosic biomass samples showed higher Eα valuesthan marine microalgae biomass, which confirmed the higherthermal resistance of their main components. This confirmedthat thermal degradation of Sp is faster than Cs and Re.

3.4. Verification. Whether the predicted activation energyresults fitted the experimental data well or not through theapplication of nonisothermal method analysis (e.g., FWO andKAS methods) is unknown. Additionally, FWO and KASmethods just only predict the activation energy. Therefore,how to perform the comparison between the predictedactivation energy results with the experimental data byisoconverison methods will be crucial. The Friedman methodprovides a possibility of reconstruction of the kinetic processby calculating the relationships of Eα and ln[Aα f(α)].On the basis of eq 6, we obtain

β α = α[ ]−α α

Tdd

e A f E RTln ( ) /(10)

Therefore, eq 10 can be calculated by the classical fourth-order Runge−Kutta method. Figure 7 shows the comparisonbetween the experimental data and the calculated data basedon the Eα and ln[Aα f(α)] values for Cs, Re, and Sp pyrolysis.From Figure 7, we can conclude that the simulations for

Friedman models presented a good agreement with theexperimental conversions calculated at three different heatingrates for Cs, Re, and Sp pyrolysis. It can be seen from the figurethat the simulated data of the Cs reaction peak agrees well with

Table 5. Mean Value of Apparent Activation Energy forLand, Coastal, and Marine Biomasses in Conversion Rangesbetween 0.1 and 0.80a

Friedman FWO KAS

samples Eα (kJ/mol) R2 Eα R2 Eα R2

Cs 215.3 0.974 238.2 0.981 226.6 0.974Re 237.4 0.941 254.7 0.952 244.2 0.948Sp 190.2 0.993 194.2 0.945 185.8 0.952

aCs: corn stalks; Re: reed; and Sp: seawater Spirulina.

Figure 7. Experimental (dot lines) and simulated (solid lines) curves of conversion rate at different heating rates based on Eα and ln[Aα f(α)] forcorn-stalk pyrolysis.



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the experimental data, and the temperature point correspond-ing to the peak was basically consistent. However, thetemperature at which the Re and Sp reaction peaks appearslightly shifts. The main reason for the shift is that it was notconsidered as the heat-transfer performance of the biomasssample in the calculation of the kinetic simulation. Overall,excluding the influence of the heat-transfer performance of thebiomass itself, the model is better for simulating the weight lossprocess of biomass pyrolysis. The kinetic simulation of theweight loss curve and kinetic parameters obtained by pyrolysisof three biomasses is reasonable and effective.

4. CONCLUSIONSOn the basis of TGA of land (Cs), coastal zone (Re), andmarine (Sp) biomasses pyrolysis, the process of pyrolysis wasstudied from the view of the kinetics. From the researchresults, the following conclusions were obtained: (1) Cs andRe samples have significantly higher maximum weight loss ratecompared to microalgae biomass Sp, and the temperaturerequired for Cs to reach the maximum weight loss rate ishigher than the other two biomasses. (2) Pyrolysis behavior ofthe marine microalgae biomass is somewhat different from thatof land and coastal lignocellulosic biomass, stemming from theinherent difference in their compositions. (3) In this study,lignocellulosic biomass has an advantage over microalgae interms of the difficulty of volatile matters releasing. Thepyrolysis process of coastal and marine biomasses is faster thanland biomass because of the catalysis of high salt tolerance. (4)The heating rate has a significant effect on the performance ofdevolatilization profiles and maximum weight loss rate,regardless of the biomass type from different regions. (5)The dynamics analysis indicates that Sp species is preferable toCs and Re in terms of the thermochemical conversion becauseof the lower apparent activation energy for total conversion.(6) The variation of Eα with α is attributed to differentpyrolysis kinetics behaviors of components corresponding tothe biomass types.

■ AUTHOR INFORMATIONCorresponding Authors*E-mail: [email protected] (Y.T.).*E-mail: [email protected] (S.Q.).ORCIDJie Li: 0000-0002-0048-4637Author Contributions§Y.Q. contributed to the work equally and should be regardedas co-first authors.Author ContributionsJ.L. developed the original structure of the draft. M.Q. andM.Z. revised the manuscript by substantially restructuring itand adding up critical insights. M.Q and M.T. are thecorresponding authors who communicated on behalf of allauthors. We further confirm that all authors have checked themanuscript and have agreed to the submission.NotesThe authors declare no competing financial interest.

■ ACKNOWLEDGMENTSThis work was supported by the National Natural ScienceFoundation of China [grant numbers: 2157060571, 21576294,and 21706287]; Shandong Province Major Science andTechnology Innovation Project (2018CXGC0301); Qingdao

People’s Livelihood Science and Technology Project [grantnumber 16-6-2-51-nsh and 18-6-1-101-nsh]; and IndependentInnovation Research Project of China University of Petroleum(East China) [grant numbers 18CX05022A andYJ201601066]. Two institutes jointly completed the presentstudy.

■ REFERENCES(1) Zhao, X.; Zhou, H.; Sikarwar, V.; Zhao, M.; Park, A.-H.; Fennell,P.; Shen, L.; Fan, L.-S. Biomass-Based Chemical LoopingTechnologies: The Good, the Bad and the Future. Energy Environ.Sci. 2017, 10, 1885−1910.(2) Gu, B.; Cao, J.-P.; Wei, F.; Zhao, X.-Y.; Ren, X.-Y.; Zhu, C.; Guo,Z.-X.; Bai, J.; Shen, W.-Z.; Wei, X.-Y. Nitrogen migration mechanismand formation of aromatics during catalytic fast pyrolysis of sewagesludge over metal-loaded HZSM-5. Fuel 2019, 244, 151−158.(3) Wang, J.-X.; Cao, J.-P.; Zhao, X.-Y.; Liu, S.-N.; Ren, X.-Y.; Zhao,M.; Cui, X.; Chen, Q.; Wei, X.-Y. Enhancement of light aromaticsfrom catalytic fast pyrolysis of cellulose over bifunctional hierarchicalHZSM-5 modified by hydrogen fluoride and nickel/hydrogenfluoride. Bioresour. Technol. 2019, 278, 116−123.(4) Luque, R. Algal biofuels: the eternal promise? Energy Environ. Sci.2010, 3, 254−257.(5) Sannigrahi, P.; Ragauskas, A. J.; Tuskan, G. A. Poplar as afeedstock for biofuels: A review of compositional characteristics.Biofuels, Bioprod. Biorefin. 2010, 4, 209−226.(6) Ramesh, R.; Chen, Z.; Cummins, V.; Day, J.; D’Elia, C.;Dennison, B.; Forbes, D. L.; Glaeser, B.; Glaser, M.; Glavovic, B.;Kremer, H.; Lange, M.; Larsen, J. N.; Le Tissier, M.; Newton, A.;Pelling, M.; Purvaja, R.; Wolanski, E. Land-Ocean Interactions in theCoastal Zone: Past, present & future. Anthropocene 2015, 12, 85−98.(7) Paul, D.; Lade, H. Plant-growth-promoting rhizobacteria toimprove crop growth in saline soils: a review. Agron. Sustainable Dev.2014, 34, 737−752.(8) Zhao, H.; Yan, H.; Zhang, C.; Liu, X.; Xue, Y.; Qiao, Y.; Tian, Y.;Qin, S. Pyrolytic Characteristics and Kinetics of Phragmites australis.J. Evidence-Based Complementary Altern. Med. 2011, 2011, 6.(9) Garrido, A. R.; Reckamp, M. J.; Satrio, A. J. Effects ofPretreatments on Yields, Selectivity and Properties of Products fromPyrolysis of Phragmites australis (Common Reeds). Environments2017, 4, 96.(10) Deniz, I.; Vardar-Sukan, F.; Yuksel, M.; Saglam, M.; Ballice, L.;Yesil-Celiktas, O. Hydrogen production from marine biomass byhydrothermal gasification. Energy Convers. Manage. 2015, 96, 124−130.(11) Neveux, N.; Magnusson, M.; Maschmeyer, T.; de Nys, R.; Paul,N. A. Comparing the potential production and value of high-energyliquid fuels and protein from marine and freshwater macroalgae. GCBBioenergy 2015, 7, 673−689.(12) Taleb, A.; Kandilian, R.; Touchard, R.; Montalescot, V.;Rinaldi, T.; Taha, S.; Takache, H.; Marchal, L.; Legrand, J.; Pruvost, J.Screening of freshwater and seawater microalgae strains in fullycontrolled photobioreactors for biodiesel production. Bioresour.Technol. 2016, 218, 480−490.(13) Zhao, L.; Qi, Y.; Chen, G. Isolation and characterization ofmicroalgae for biodiesel production from seawater. Bioresour. Technol.2015, 184, 42−46.(14) Schenk, P. M.; Thomas-Hall, S. R.; Stephens, E.; Marx, U. C.;Mussgnug, J. H.; Posten, C.; Kruse, O.; Hankamer, B. SecondGeneration Biofuels: High-Efficiency Microalgae for BiodieselProduction. BioEnergy Res. 2008, 1, 20−43.(15) Mary Leema, J. T.; Kirubagaran, R.; Vinithkumar, N. V.;Dheenan, P. S.; Karthikayulu, S. High value pigment production fromArthrospira (Spirulina) platensis cultured in seawater. Bioresour.Technol. 2010, 101, 9221−9227.(16) Yang, Z.; Cao, J.-P.; Ren, X.-Y.; Zhao, X.-Y.; Liu, S.-N.; Guo, Z.-X.; Shen, W.-Z.; Bai, J.; Wei, X.-Y. Preparation of hierarchical HZSM-



3308

mailto:[email protected]

mailto:[email protected]

http://orcid.org/0000-0002-0048-4637


5 based sulfated zirconium solid acid catalyst for catalytic upgrading ofpyrolysis vapors from lignite pyrolysis. Fuel 2019, 237, 1079−1085.(17) Ren, X.-Y.; Cao, J.-P.; Zhao, X.-Y.; Yang, Z.; Liu, T.-L.; Fan, X.;Zhao, Y.-P.; Wei, X.-Y. Catalytic upgrading of pyrolysis vapors fromlignite over mono/bimetal-loaded mesoporous HZSM-5. Fuel 2018,218, 33−40.(18) Demirbas, A. Mechanisms of liquefaction and pyrolysisreactions of biomass. Energy Convers. Manage. 2000, 41, 633−646.(19) White, J. E.; Catallo, W. J.; Legendre, B. L. Biomass pyrolysiskinetics: A comparative critical review with relevant agriculturalresidue case studies. J. Anal. Appl. Pyrolysis 2011, 91, 1−33.(20) Bui, H.-H.; Tran, K.-Q.; Chen, W.-H. Pyrolysis of microalgaeresidues - A kinetic study. Bioresour. Technol. 2016, 199, 362−366.(21) Ceylan, S.; Topcu, Y. Pyrolysis kinetics of hazelnut husk usingthermogravimetric analysis. Bioresour. Technol. 2014, 156, 182−188.(22) Gautam, R.; Varma, A. K.; Vinu, R. Apparent Kinetics of FastPyrolysis of Four Different Microalgae and Product Analyses UsingPyrolysis-FTIR and Pyrolysis-GC/MS. Energy Fuels 2017, 31, 12339−12349.(23) Cai, J.; Xu, D.; Dong, Z.; Yu, X.; Yang, Y.; Banks, S. W.;Bridgwater, A. V. Processing thermogravimetric analysis data forisoconversional kinetic analysis of lignocellulosic biomass pyrolysis:Case study of corn stalk. Renewable Sustainable Energy Rev. 2018, 82,2705−2715.(24) Hu, M.; Chen, Z.; Wang, S.; Guo, D.; Ma, C.; Zhou, Y.; Chen,J.; Laghari, M.; Fazal, S.; Xiao, B.; Zhang, B.; Ma, S. Thermogravi-metric kinetics of lignocellulosic biomass slow pyrolysis usingdistributed activation energy model, Fraser-Suzuki deconvolution,and iso-conversional method. Energy Convers. Manage. 2016, 118, 1−11.(25) Shuping, Z.; Yulong, W.; Mingde, Y.; Chun, L.; Junmao, T.Pyrolysis characteristics and kinetics of the marine microalgaeDunaliella tertiolecta using thermogravimetric analyzer. Bioresour.Technol. 2010, 101, 359−365.(26) Wang, X.; Hu, M.; Hu, W.; Chen, Z.; Liu, S.; Hu, Z.; Xiao, B.Thermogravimetric kinetic study of agricultural residue biomasspyrolysis based on combined kinetics. Bioresour. Technol. 2016, 219,510−520.(27) Cai, J.; Liu, R. New distributed activation energy model:Numerical solution and application to pyrolysis kinetics of some typesof biomass. Bioresour. Technol. 2008, 99, 2795−2799.(28) Burnham, A. K.; Dinh, L. N. A comparison of isoconversionaland model-fitting approaches to kinetic parameter estimation andapplication predictions. J. Therm. Anal. Calorim. 2007, 89, 479−490.(29) Sanchez-Jimenez, P. E.; Perez-Maqueda, L. A.; Perejon, A.;Criado, J. M. Limitations of model-fitting methods for kinetic analysis:Polystyrene thermal degradation. Resour., Conserv. Recycl. 2013, 74,75−81.(30) Huang, X.; Cao, J.-P.; Zhao, X.-Y.; Wang, J.-X.; Fan, X.; Zhao,Y.-P.; Wei, X.-Y. Pyrolysis kinetics of soybean straw usingthermogravimetric analysis. Fuel 2016, 169, 93−98.(31) Liden, A. G.; Berruti, F.; Scott, D. S. A Kinetic Model For TheProduction Of Liquids From The Flash Pyrolysis Of Biomass. Chem.Eng. Commun. 1988, 65, 207−221.(32) Zhang, X.; Lei, H.; Zhu, L.; Zhu, X.; Qian, M.; Yadavalli, G.;Wu, J.; Chen, S. Thermal behavior and kinetic study for catalytic co-pyrolysis of biomass with plastics. Bioresour. Technol. 2016, 220, 233−238.(33) Papari, S.; Hawboldt, K. A review on the pyrolysis of woodybiomass to bio-oil: Focus on kinetic models. Renewable SustainableEnergy Rev. 2015, 52, 1580−1595.(34) Chen, Z.; Zhu, Q.; Wang, X.; Xiao, B.; Liu, S. Pyrolysisbehaviors and kinetic studies on Eucalyptus residues usingthermogravimetric analysis. Energy Convers. Manage. 2015, 105,251−259.(35) Vyazovkin, S.; Chrissafis, K.; Di Lorenzo, M. L.; Koga, N.;Pijolat, M.; Roduit, B.; Sbirrazzuoli, N.; Sunol, J. J. ICTAC KineticsCommittee recommendations for collecting experimental thermal

analysis data for kinetic computations. Thermochim. Acta 2014, 590,1−23.(36) Munir, S.; Daood, S. S.; Nimmo, W.; Cunliffe, A. M.; Gibbs, B.M. Thermal analysis and devolatilization kinetics of cotton stalk, sugarcane bagasse and shea meal under nitrogen and air atmospheres.Bioresour. Technol. 2009, 100, 1413−1418.(37) Li, Z.; Zhao, W.; Meng, B.; Liu, C.; Zhu, Q.; Zhao, G. Kineticstudy of corn straw pyrolysis: Comparison of two different three-pseudocomponent models. Bioresour. Technol. 2008, 99, 7616−7622.(38) Salema, A. A.; Afzal, M. T.; Bennamoun, L. Pyrolysis of cornstalk biomass briquettes in a scaled-up microwave technology.Bioresour. Technol. 2017, 233, 353−362.(39) Huang, L.; Chen, Y.; Liu, G.; Li, S.; Liu, Y.; Gao, X. Non-isothermal pyrolysis characteristics of giant reed (Arundo donax L.)using thermogravimetric analysis. Energy 2015, 87, 31−40.(40) Yuan, T.; Tahmasebi, A.; Yu, J. Comparative study on pyrolysisof lignocellulosic and algal biomass using a thermogravimetric and afixed-bed reactor. Bioresour. Technol. 2015, 175, 333−341.(41) Sanchez-Silva, L.; Lopez-Gonzalez, D.; Villasenor, J.; Sanchez,P.; Valverde, J. L. Thermogravimetric-mass spectrometric analysis oflignocellulosic and marine biomass pyrolysis. Bioresour. Technol. 2012,109, 163−172.(42) Liting Han, Y. L.; Chen, K. Proximate Analysis of Coal; ChineseNational Standards, 2008.(43) Zhao, J.; Xiuwen, W.; Hu, J.; Liu, Q.; Shen, D.; Xiao, R.Thermal degradation of softwood lignin and hardwood lignin by TG-FTIR and Py-GC/MS. Polym. Degrad. Stab. 2014, 108, 133−138.(44) Hu, M.; Wang, X.; Chen, J.; Yang, P.; Liu, C.; Xiao, B.; Guo, D.Kinetic study and syngas production from pyrolysis of forestry waste.Energy Convers. Manage. 2017, 135, 453−462.(45) Zhang, L.; Xu, C.; Champagne, P. Overview of recent advancesin thermo-chemical conversion of biomass. Energy Convers. Manage.2010, 51, 969−982.(46) Wang, S.; Li, Z.; Bai, X.; Yi, W.; Fu, P. Influence of inherenthierarchical porous char with alkali and alkaline earth metallic specieson lignin pyrolysis. Bioresour. Technol. 2018, 268, 323−331.(47) Wu, Z.; Wang, S.; Zhao, J.; Chen, L.; Meng, H. Synergisticeffect on thermal behavior during co-pyrolysis of lignocellulosicbiomass model components blend with bituminous coal. Bioresour.Technol. 2014, 169, 220−228.(48) Huang, H.-j.; Yuan, X.-z.; Zhu, H.-n.; Li, H.; Liu, Y.; Wang, X.-l.; Zeng, G.-m. Comparative studies of thermochemical liquefactioncharacteristics of microalgae, lignocellulosic biomass and sewagesludge. Energy 2013, 56, 52−60.(49) Eom, I.-Y.; Kim, J.-Y.; Kim, T.-S.; Lee, S.-M.; Choi, D.; Choi, I.-G.; Choi, J.-W. Effect of essential inorganic metals on primary thermaldegradation of lignocellulosic biomass. Bioresour. Technol. 2012, 104,687−694.(50) Li, F.; Lang, F.; Zhang, H.; Xu, L.; Wang, Y.; Zhai, C.; Hao, E.Apigenin Alleviates Endotoxin-Induced Myocardial Toxicity byModulating Inflammation, Oxidative Stress, and Autophagy. Oxid.Med. Cell. Longevity 2017, 2017, 1.(51) Kebelmann, K.; Hornung, A.; Karsten, U.; Griffiths, G.Intermediate pyrolysis and product identification by TGA and Py-GC/MS of green microalgae and their extracted protein and lipidcomponents. Biomass Bioenergy 2013, 49, 38−48.(52) Haykiri-Acma, H.; Yaman, S.; Kucukbayrak, S. Effect of heatingrate on the pyrolysis yields of rapeseed. Renewable Energy 2006, 31,803−810.(53) Aboyade, A. O.; Gorgens, J. F.; Carrier, M.; Meyer, E. L.;Knoetze, J. H. Thermogravimetric study of the pyrolysis character-istics and kinetics of coal blends with corn and sugarcane residues.Fuel Process. Technol. 2013, 106, 310−320.(54) Carrier, M.; Auret, L.; Bridgwater, A.; Knoetze, J. H. UsingApparent Activation Energy as a Reactivity Criterion for BiomassPyrolysis. Energy Fuels 2016, 30, 7834−7841.(55) Vyazovkin, S.; Burnham, A. K.; Criado, J. M.; Perez-Maqueda,L. A.; Popescu, C.; Sbirrazzuoli, N. ICTAC Kinetics Committee



3309


recommendations for performing kinetic computations on thermalanalysis data. Thermochim. Acta 2011, 520, 1−19.(56) Bach, Q.-V.; Chen, W.-H. A comprehensive study on pyrolysiskinetics of microalgal biomass. Energy Convers. Manage. 2017, 131,109−116.



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