The combustion characteristics of biochar obtained from barley straw (BS) and brown algae (BA) are explored. Four different heating rates are utilized to determine the respective activation energies. A master plot analysis is used to identify the appropriate reaction model. The results show that the activation energies vary in the ranges of 6.86–48.36 and 46.34–77.51 kJ mol−1 for BS and BA biochar combustion, respectively. As the heating rate increases, most of the combustion characteristic parameters increase, while the combustion stability index decreases. These observations help provide a deeper understanding of the combustion of lignocellulosic and algal biomass-derived biochar.

Biomass is a promising carbon-neutral energy source and is generally divided into lignocellulosic biomass and algal biomass.1 Lignocellulosic biomass contains mainly hemicellulose, cellulose, and lignin, while algal biomass consists mostly of carbohydrates, proteins, and lipids.2 Raw biomass can be converted into different energy products or forms via various pathways, such as torrefaction, liquefication, pyrolysis, gasification, and combustion.3,4

Biochar is an important product of biomass pyrolysis that can be used as a fuel for combustion, as fertilizer in agriculture, and as a catalyst for fuel conversion.5,6 Liu and Han7 produced biochar from pinewood and coconut fiber. They observed an improvement in biochar combustion when the pyrolysis temperature was increased from 300 to 330 °C. Hou et al.8 presented a detailed review of bioethanol and biochar production from lignocellulosic biomass. Hoang et al.9 generated biochar from crop residues and found that slow pyrolysis produced ∼35% biochar. James et al.10 studied the life cycle and technical grades of biochar produced from agricultural residues. Danesh et al.11 reviewed recent developments, applications, and challenges during biochar production. Hamidzadeh et al.12 investigated the agricultural applications of biochar and noted that biochar could be used to regulate soil properties and fertility. Muzyka et al.13 tested several temperatures, heating rates, and residence times for the production of biochar and found that the optimum yield of biochar was obtained at 650 °C with a heating rate of 20 °C min−1 and a residence time of 5 min.

With regard to the use of biochar as a fuel, Wang et al.14 analyzed the combustion of biochar produced through torrefaction and slow pyrolysis of straw. Chen et al.15 explored the properties and combustion behavior of biochar obtained via low-temperature pyrolysis of sewage sludge. They observed that biochar combustion exhibited a lower degradation rate than that of raw sewage sludge. Vasudev et al.16 studied the combustion of algal biochar and obtained values for the combustibility index. Lim et al.17 produced biochar from palm kernel shells via microwave-assisted pyrolysis. They found that the addition of biochar to biodiesel improved the overall combustion efficiency owing to the oxygenated compounds present in biochar. Dang et al.18 examined the co-combustion behavior of bamboo char and waste plastics hydrochar. They found that bamboo char had a poor combustion performance owing to its high carbon structure and ash content.

However, studies of biochar combustion characteristics are still rare, and therefore in the work reported in the present paper, the combustion of lignocellulosic (barley straw) and algal (brown algae) biomass-derived biochar was investigated using a thermogravimetric analyzer (TGA). During combustion, four different heating rates (β = 9, 18, 27, and 36 °C min−1) were tested. A kinetic analysis was conducted to determine the activation energy E and reaction model f(α). The combustion performance was also evaluated by various indicators.

Barley straw (BS) and brown algae (BA) were provided by Xi’an Zhongkeda Biotechnology Co., Ltd., China. Both raw samples were sieved into fine powders. BS and BA biomass-derived biochar samples (henceforth referred to as BS-Biochar and BA-Biochar, respectively) were produced by slow pyrolysis in a horizontal tube reactor. For each biochar sample, the moisture content (MC), volatile matter (VM), ash content (AC), and fixed carbon (FC) were estimated by basically following the experimental procedure of Qin and Thunman.19 The higher heating value (HHV) was determined using an oxygen bomb calorimeter. Table I lists the properties of the two biochar samples.

For biochar production, the raw biomass sample was first put into a horizontal tube reactor in which an inert atmosphere was created by purging N2. Afterwards, the temperature was increased to 600 °C and maintained for 30 min. Finally, the remaining solid residue was considered as biochar and used for combustion experiments. The biochar combustion was carried out in a thermogravimetric analyzer (TGA55, TA Instruments, USA). Approximately 10 mg of each biochar sample was used for each test. The sample was first dried at 105 °C for 5 min in a N2 atmosphere. Then, combustion took place from 105 to 700 °C in an air environment. The holding time at 700 °C was 15 min. During combustion, to explore the effect of heating rate β, four different values, namely, β = 9, 18, 27, and 36 °C min−1, were used.

The fractional conversion α during combustion can be expressed as follows:20–22,
α=m0mim0mf,
(1)
where m0, mi, and mf are the initial, instantaneous, and final masses of the sample, respectively. Note that m0 and mf were set to be the sample masses at 105 and 600 °C, respectively. Generally, the Arrhenius equation is utilized to evaluate the reaction rate,23 i.e.,
dαdt=AexpERTf(α),
(2)
where A, t, T, and R are the pre-exponential factor, time, temperature, and universal gas constant, respectively. Taking the logarithm of each side of Eq. (2) transforms it to the following form:24 
lndαdt=ln[Af(α)]ERT.
(3)
The use of Eq. (3) is generally known as Friedman’s method. The values of E at each α can be obtained from the slopes of plots of ln(/dt) vs 1/T, while f(α) is determined using a master plot analysis. Table II shows some common solid-state kinetic models:24,25 the Avrami–Erofeev models with n = 2, 3, and 4 (labeled A2, A3, and A4, respectively) and the contracting cylinder and contracting sphere models (labeled R2 and R3, respectively). The y(α) master plot method was employed as follows:26,
y(α)=dαdtαexpE0RTα=Af(α),
(4)
where E0 is the average activation energy, taken in this work as the average value of activation energies for the range of α from 0.2 to 0.9. For comparison purposes, y(α) was normalized as follows:24 
y(α)y(0.5)=dαdtαexpE0RTαdαdt0.5expE0RT0.5=Af(α)Af(0.5).
(5)
A comparison between the theoretical and experimental y(α) curves and a minimization of the parameter χ2 were used to determine the appropriate f(α). χ2 was calculated as follows:27,28
χ2=[y(α)expy(α)th]2N1,
(6)
where N is the total number of experimental data points considered for comparison.
To analyze the biochar combustion performance, the flammability index F, ignition index Di, burnout index Db, combustion index Ci, and combustion stability index Cs were determined as follows:16,23,29,30
F=RpTi2,
(7)
Di=Rptitp,
(8)
Db=RptptbΔt0.5,
(9)
Ci=(Rp)(Rv)TbTi2,
(10)
Cs=RpTiTb,
(11)
where Rp, Rv, Ti, Tb, ti, tb, tp, and Δt0.5 are the maximum degradation rate, average degradation rate, ignition temperature, burnout temperature, ignition time, burnout time, time instant at maximum degradation rate, and time difference at half Rp values, respectively. Ti and Tb were determined using the tangent line method.16 Then, ti and tb were the time instants at Ti and Tb, respectively.

Figure 1 presents the results of thermogravimetric analysis for the combustion of both BS-Biochar and BA-Biochar samples. As shown in Figs. 1(a) and 1(b), two major stages can be identified during BS-Biochar combustion. Intense combustion leads to a maximum mass loss rate during the first stage, and complete burnout of the remaining sample occurs during the second stage.29 The final residue after completion of the combustion process is considered as ash. Similarly, BA-Biochar combustion can also be divided into two main stages. Moreover, close to 700 °C, an additional peak appears, particularly prominent at low heating rates, indicating the complex combustion behavior of algal biochar. Table III presents a quantitative summary of Tp, Rp, and ash for the combustion of BS-Biochar and BA-Biochar at all the heating rates studied. Note that Tp represents the temperature at the maximum degradation rate. Obviously, increasing the heating rate generally increases both Tp and Rp, as well as the ash content. BS-Biochar gave a smaller Tp and lower ash, but a larger Rp than BA-Biochar for all heating rates tested.

1. Activation energy

Figure 2 shows the variation of E with α for the combustion of BS-Biochar and BA-Biochar. In the case of BS-Biochar combustion, E increases in the range of α = 0.2–0.45, decreases slightly in the subsequent range of α = 0.45–0.7, and increases again in the range of α = 0.7–0.9. In the case of BA-Biochar combustion, E decreases until α = 0.3, then remains nearly constant until α = 0.45. Subsequently, E increases until α = 0.6, remains nearly constant up to α = 0.75, and finally increases until α = 0.9. Quantitatively, the E0 values for BS-Biochar and BA-Biochar combustion are 27.97 and 55.15 kJ mol−1, respectively, suggesting that BS-Biochar combustion requires less energy than that of BA-Biochar.

2. Reaction model

Figure 3 presents a comparison of normalized y(α) master plots between experiment and theoretical models for both samples at a heating rate of 36 °C min−1. Possible kinetic models were initially selected on the basis of shape similarity, and the specific models were finally chosen using the minimum χ2-value criterion. This procedure indicated that the BS-Biochar combustion was described by the A3 model (χ2 = 0.0225) and the BA-Biochar combustion by the A4 model (χ2 = 0.0103).

Table IV shows the variation of the combustion performance indicators with heating rate. F, Di, Db, and Ci all increased with increasing β for both samples. The increase in F with increasing β indicated a lower volatility, an increase in Ci with increasing β implied a higher burning capability at higher heating rates. Meanwhile, the increase in Di and Db with increasing β revealed higher energy requirements. In contrast, Cs decreased with increasing β for both samples, indicating lower combustion stability at higher β. Moreover, BS-Biochar exhibited slightly higher combustion stability than BA-Biochar at most values of β.

In this study, the combustion characteristics of BS-Biochar and BA-Biochar were experimentally investigated. The activation energies were estimated at four different heating rates, and the average activation energy of BS-Biochar was found to be lower than that of BA-Biochar. A master plot analysis was used to identify the appropriate reaction models. BS-Biochar exhibited a smaller Tp and lower ash, but a larger Rp than BA-Biochar for all values of β that were tested. The majority of the combustion performance indicators increased with increasing β, although the combustion stability index decreased. BS-Biochar exhibited slightly higher combustion stability than BA-Biochar at most values of β. All these observations help provide a deeper understanding of the combustion of lignocellulosic and algal biomass-derived biochar.

This work is supported financially by the National Natural Science Foundation of China (Project No. 12172328) and the Zhejiang Provincial Natural Science Foundation of China (Project No. LXR22A020001).

The authors have no conflicts to disclose.

Shri Ram: Conceptualization (equal); Data curation (lead); Formal analysis (lead); Investigation (lead); Methodology (lead); Visualization (equal); Writing – original draft (lead); Writing – review & editing (equal). Vikul Vasudev: Conceptualization (equal); Methodology (equal); Writing – review & editing (equal). Xiaoke Ku: Conceptualization (equal); Funding acquisition (lead); Project administration (lead); Resources (lead); Supervision (lead); Writing – review & editing (equal).

The data that support the observations of this work can be available from the corresponding author upon reasonable request.

A

pre-exponential factor (min−1)

BA

brown algae

BS

barley straw

Ci

combustibility index (K−3 min−2)

Cs

combustion stability index (K−2 min−1)

Db

burnout index (min−4)

Di

ignition index (min−3)

E

activation energy (kJ mol−1)

F

flammability index (K−2 min−1)

f(α)

reaction model

HHV

higher heating value (MJ kg−1)

m0

initial mass (mg)

mf

final mass (mg)

mi

instantaneous mass (mg)

n

reaction order

R

universal gas constant (J mol−1 K−1)

Rp

maximum degradation rate (wt % min−1)

Rv

average degradation rate (wt. % min−1)

T

temperature (°C or K)

Tb

burnout temperature (K)

Ti

ignition temperature (K)

Tp

temperature at Rp (K)

t

time (min)

tb

burnout time (min)

ti

ignition time (min)

tp

time instant at Rp (min)

Greek
Δt0.5

time difference at half Rp values (min)

α

fractional conversion

β

heating rate (°C min−1)

χ2

chi-square

A

pre-exponential factor (min−1)

BA

brown algae

BS

barley straw

Ci

combustibility index (K−3 min−2)

Cs

combustion stability index (K−2 min−1)

Db

burnout index (min−4)

Di

ignition index (min−3)

E

activation energy (kJ mol−1)

F

flammability index (K−2 min−1)

f(α)

reaction model

HHV

higher heating value (MJ kg−1)

m0

initial mass (mg)

mf

final mass (mg)

mi

instantaneous mass (mg)

n

reaction order

R

universal gas constant (J mol−1 K−1)

Rp

maximum degradation rate (wt % min−1)

Rv

average degradation rate (wt. % min−1)

T

temperature (°C or K)

Tb

burnout temperature (K)

Ti

ignition temperature (K)

Tp

temperature at Rp (K)

t

time (min)

tb

burnout time (min)

ti

ignition time (min)

tp

time instant at Rp (min)

Greek
Δt0.5

time difference at half Rp values (min)

α

fractional conversion

β

heating rate (°C min−1)

χ2

chi-square

1.
T. J.
Morgan
,
A.
Youkhana
,
S. Q.
Turn
,
R.
Ogoshi
, and
M.
Garcia-Pérez
,
Energy Fuels
33
(
4
),
2699
2762
(
2019
).
2.
T.
Yuan
,
A.
Tahmasebi
, and
J.
Yu
,
Bioresour. Technol.
175
,
333
341
(
2015
).
3.
Y.
Patil
and
X.
Ku
,
Energy Sources, Part A
44
(
4
),
8860
8877
(
2022
).
4.
X.
Ku
,
T.
Li
, and
T.
Løvås
,
Chem. Eng. Sci.
246
,
2
11
(
2017
).
5.
I.
Michalak
,
S.
Baśladyńska
,
J.
Mokrzycki
, and
P.
Rutkowski
,
Water
11
(
7
),
1390
(
2019
).
6.
K. L.
Yu
,
B. F.
Lau
,
P. L.
Show
,
H. C.
Ong
,
T. C.
Ling
,
W. H.
Chen
,
E. P.
Ng
, and
J. S.
Chang
,
Bioresour. Technol.
246
,
2
11
(
2017
).
7.
8.
J.
Hou
,
X.
Zhang
,
S.
Liu
,
S.
Zhang
, and
Q.
Zhang
,
Energy Technol.
8
(
5
),
2000025
(
2020
).
9.
A. T.
Hoang
,
J. L.
Goldfarb
,
A. M.
Foley
,
E.
Lichtfouse
,
M.
Kumar
,
L.
Xiao
,
S. F.
Ahmed
,
Z.
Said
,
R.
Luque
,
V. G.
Bui
, and
X. P.
Nguyen
,
Bioresour. Technol.
363
,
127970
(
2022
).
10.
A.
James
,
A.
Sánchez
,
J.
Prens
, and
W.
Yuan
,
Curr. Opin. Environ. Sci. Health
25
,
100314
(
2022
).
11.
P.
Danesh
,
P.
Niaparast
,
P.
Ghorbannezhad
, and
I.
Ali
,
Fuel
337
,
126889
(
2023
).
12.
Z.
Hamidzadeh
,
P.
Ghorbannezhad
,
M. R.
Ketabchi
, and
B.
Yeganeh
,
Fuel
341
,
127701
(
2023
).
13.
R.
Muzyka
,
E.
Misztal
,
J.
Hrabak
,
S. W.
Banks
, and
M.
Sajdak
,
Energy
263
,
126128
(
2023
).
14.
W.
Wang
,
C.
Wen
,
C.
Li
,
M.
Wang
,
X.
Li
,
Y.
Zhou
, and
X.
Gong
,
Fuel
240
,
278
288
(
2019
).
15.
R.
Chen
,
Q.
Sheng
,
X.
Dai
, and
B.
Dong
,
Fuel
300
,
121007
(
2021
).
16.
V.
Vasudev
,
X.
Ku
, and
J.
Lin
,
ACS Omega
6
(
29
),
19144
19152
(
2021
).
17.
X. Y.
Lim
,
P. N. Y.
Yek
,
R. K.
Liew
,
M. C.
Chiong
,
W. A. W.
Mahari
,
W.
Peng
,
C. T.
Chong
,
C. Y.
Lin
,
M.
Aghbashlo
,
M.
Tabatabaei
, and
S. S.
Lam
,
Fuel
312
,
122839
(
2022
).
18.
H.
Dang
,
R.
Xu
,
J.
Zhang
,
M.
Wang
,
G.
Jia
,
Y.
Wang
, and
W.
Duan
,
Thermochim. Acta
722
,
179466
(
2023
).
19.
K.
Qin
and
H.
Thunman
,
Fuel
147
,
161
169
(
2015
).
20.
V.
Vasudev
,
X.
Ku
, and
J.
Lin
,
Bioresour. Technol.
288
,
121496
(
2019
).
21.
Y.
Patil
,
X.
Ku
, and
V.
Vasudev
,
ACS Omega
8
(
38
),
34938
34947
(
2023
).
22.
A.
Sharma
,
A. A.
Kumar
,
B.
Mohanty
, and
S.
Khanam
,
Bioresour. Technol. Rep.
22
,
101485
(
2023
).
23.
X.
Zha
,
Z.
Zhang
,
Z.
Zhao
,
X.
Li
,
C.
Luo
,
F.
Wu
, and
L.
Zhang
,
Fuel
333
,
126462
(
2023
).
24.
S.
Vyazovkin
,
A. K.
Burnham
,
J. M.
Criado
,
L. A.
Pérez-Maqueda
,
C.
Popescu
, and
N.
Sbirrazzuoli
,
Thermochim. Acta
520
(
1–2
),
1
19
(
2011
).
25.
S.
Ram
,
X.
Ku
, and
V.
Vasudev
,
Biofuels, Bioprod. Biorefin.
18
,
482
494
(
2024
).
26.
S.
Vyazovkin
,
A. K.
Burnham
,
L.
Favergeon
,
N.
Koga
,
E.
Moukhina
,
L. A.
Pérez-Maqueda
, and
N.
Sbirrazzuoli
,
Thermochim. Acta
689
,
178597
(
2020
).
27.
S.
Chokphoemphun
,
S.
Hongkong
, and
S.
Chokphoemphun
, “
Evaluation of drying behavior and characteristics of potato slices in multi–stage convective cabinet dryer: Application of artificial neural network
,”
Inf. Process. Agric
(published online)
(
2024
).
28.
S. H. M.
Ashtiani
,
A.
Salarikia
, and
M. R.
Golzarian
,
Inf. Process. Agric.
4
(
2
),
128
139
(
2017
).
29.
W.
Mo
,
Z.
Wu
,
X.
He
,
W.
Qiang
,
B.
Wei
,
X.
Wei
,
Y.
Wu
,
X.
Fan
, and
F.
Ma
,
Fuel
296
,
120669
(
2021
).
30.
D.
Rammohan
,
N.
Kishore
, and
R. V. S.
Uppaluri
,
Results Eng.
17
,
100936
(
2023
).