Pirolisis de penicilina para obtner biodiesel

Bioresource Technology 277 (2019) 46–54
Contents lists available at ScienceDirect
Bioresource Technology
journal homepage: www.elsevier.com/locate/biortech
Thermal characteristics and product formation mechanism during pyrolysis
of penicillin fermentation residue
T
⁎
Zhiqiang Wanga,b,c, Chen Honga,b, , Yi Xinga,b,c, Zaixing Lic, Yifei Lia,b, Jian Yanga,b, Lihui Fenga,b,
Jiashuo Hua,b, Haipeng Sund
a
Department of Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, University of Science and Technology Beijing, Beijing 100083, China
c
Department of Environmental Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
d
China Certification Centre for Automotive Products Co., Ltd., Beijing 100044, China
b
G R A P H I C A L A B S T R A C T
A R T I C LE I N FO
A B S T R A C T
Keywords:
Penicillin fermentation residue
Pyrolysis characteristics
Kinetics
Bio-oil
GC-MS analysis
This work studied thermal characteristics and product formation mechanism during pyrolysis of penicillin fermentation residue (PR). Results showed that PR pyrolysis proceeded in four stages. The kinetic triplet of each
stage was calculated using Flynn-Wall-Ozawa, Kissinger-Akahira-Sunose, and integral master-plot methods. The
kinetic model for stage 1 was the three-dimensional diffusion model, the simple reaction order model for stage 2
and stage 4, and the nucleation-growth model for stage 3. FTIR analysis suggested that the intensities of absorption peaks of NeH, C]O, CeH, CeN, and CeO in chars weakened gradually with increasing temperature,
corresponding to the production of CH4, CO, NH3, and HCN. GC-MS results indicated that the high protein
content in PR resulted in a high fraction of nitrogenated compounds (amides and amines, nitriles, and N-heterocyclic species) in bio-oil. The formation mechanism of these compounds was discussed. Besides, bio-oil also
contained large quantities of oxygenated compounds and a few hydrocarbons.
1. Introduction
Increasing energy demands motivate the development of new alternative energy because the use of fossil energy resources exhausts
⁎
finite reserves and causes severe environmental problems such as global
warming and climate change (Dhyani and Bhaskar, 2018; Yuan et al.,
2015). Biomass, which includes municipal and industrial wastes as well
as lignocellulosic and aquatic feedstocks, is a promising renewable
Corresponding author at: Department of Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
E-mail address: hongchen@ustb.edu.cn (C. Hong).
https://doi.org/10.1016/j.biortech.2019.01.030
Received 26 November 2018; Received in revised form 7 January 2019; Accepted 8 January 2019
Available online 09 January 2019
0960-8524/ © 2019 Elsevier Ltd. All rights reserved.
Bioresource Technology 277 (2019) 46–54
Z. Wang et al.
integral master-plot methods. The compositions of evolved gas and the
surface chemical structures of raw PR and char samples at different
temperatures were detected using FTIR spectrometer. The bio-oil
compositions were identified by GC-MS and the formation mechanism
of N-containing compounds in bio-oil were discussed in depth.
energy source. It can be converted into energy and fuel via a thermochemical conversion process. Pyrolysis is one of the most researched
thermochemical methods, and the process results in the conversion of
solid biomass to bio-oil which can be used in existing equipment such as
boilers or can be upgraded to produce liquid transportation fuels. Many
authors have conducted research on the distribution and formation
mechanism of products during lignocellulosic biomass pyrolysis
(Krishna et al., 2016; Mafu et al., 2017; Wang et al., 2017; Wang et al.,
2018b). However, several techno-economic studies showed that lignocellulosic biomass can only provide 30% of the world’s energy requirements (Maddi et al., 2017). Aquatic biomass such as algae can be
cultivated on non-arable land without competing with food crops and
can be grown quickly (Hu et al., 2017). The yield estimates of bio-oil
from algae are higher than those from any other biomass, but technical,
economical, and logistical problems must be settled before there is
large-scale production of liquid fuels (Georgianna and Mayfield, 2012;
Maddi et al., 2017). Thus, more attention has shifted to municipal and
industrial wastes containing large quantities of organics.
To date, most of the work has focused on sludge pyrolysis (Alvarez
et al., 2016; Fang et al., 2016; Lin et al., 2018; Magdziarz and Werle,
2014). These studies indicated that the organic matter in sludge at
about 473 K begins to decompose to form volatiles and chars through
various bond-breaking and -forming reactions. After cooling, the volatiles spilling out of the pyrolysis reactor condense to form bio-oil.
However, the composition of bio-oil reported in the literature varies
greatly, resulting in different characteristics of sludge pyrolysis oil between studies. This is because sludge pyrolysis involves numerous
complex reaction pathways, and many factors such as the temperature,
residue time, and sludge composition may affect this process and thus
the distribution and characteristics of pyrolytic products (Fonts et al.,
2012; Syed-Hassan et al., 2017).
The antibiotic residue studied in the present work originates from
the antibiotics production process and is generated continuously from
existing industries. It has been included in the China Hazardous Waste
List since 2008 because the residual antibiotics may threaten human
health through pollution of water and soil (Cai et al., 2017). Dried
antibiotic residue with high organic content has the potential to produce bio-oil via pyrolysis, and can be regarded as a special source of
bioenergy. The thermal behavior of antibiotic residue is different from
that of other organic wastes due to the different physicochemical
characteristics, but little research has been done. Meanwhile, this information can also provide a theoretical guideline to design and optimize manufacturing process of bio-oil from PR. Bio-oil has been considered as a promising solution to the issues of greenhouse gas emission
and energy security. For commercial utilization of bio-oil, its potential
environmental impacts and sustainability aspects need to be evaluated
by some methods such as life cycle assessment (LCA) (Liu et al., 2018).
A systematic study on the properties of pyrolysis products not only
contributes to further understand the pyrolysis mechanism, but also
provides basis data for LCA of bio-oil. For example, analyzing the
properties of gas and solid products is helpful to improve the yield and
quality of bio-oil; determining the bio-oil compositions provide a significant guidance for reducing the discharge of pollutants from the biooil combustion. It is noted that antibiotic residue shows a higher nitrogen content of up to 10 wt% which is far greater than the content in
other organic wastes such as sludge (Yang et al., 2015). Nitrogen element in antibiotic residue exists primarily as protein-N which can be
transformed into various N-containing compounds in pyrolytic oil
whose subsequent use will cause the secondary pollution (Zhu et al.,
2016). Thus, understanding the distribution and formation mechanism
of nitrogen in bio-oil is very essential to obtain high quality liquid fuels,
which will lay the foundation for the use of bio-oil as a transportation
fuel. However, all the information is not available.
In this work, the pyrolysis characteristics of penicillin fermentation
residue (PR) were studied using a thermogravimetric analyzer and the
kinetic triplet of this process was calculated via the FWO, KAS and
2. Materials and methods
2.1. Materials
In this study, the tested antibiotic residue was from the penicillin
production process in a pharmaceutical factory in Hebei Province,
China. The penicillin fermentation residue is denoted as PR. After being
dried to constant weight in an oven at 105 °C, the PR samples were
ground and filtered through a 100-mesh sieve for further analysis. The
contents of crude protein, crude lipid, and carbohydrate in PR were
measured according to GB 5009.5–2010, GB/T 5009.6–2003, and NY/T
1676–2008, respectively. The ultimate analysis for C, H, N and S content was carried out using a Vario EL cube analyzer (Elementar,
Germany). The proximate analysis was performed using a TGA2000
analyzer (Navas, America). The higher heating value (HHV) were calculated using the Dulong formula (Yu et al., 2011):
HHV(MJ/kg) = 0.3383 C+ 1.422(H - O/8)
(1)
where C, H, and O are the mass percentages of carbon, hydrogen, and
oxygen in PR, respectively. From the above analysis, contents of crude
protein, crude lipid, carbohydrate, and others were 45.4 wt%, 3.85 wt
%, 1.66 wt%, and 49.09 wt%, respectively. The proximate analysis and
ultimate analysis on a dry basis were as follows: 82.62 wt% volatile
matters, 9.34 wt% fixed carbon, 8.04 wt% ash, 44.88 wt% C, 6.27 wt%
H, 5.93 wt% N, 0.77 wt% S, and 34.12 wt% O (calculated by difference). The HHV of PR was 18.03 MJ/kg.
2.2. Experimental methods
2.2.1. Pyrolysis characteristics
The thermogravimetric analysis experiments were conducted on a
NETZSCH STA 449 F3 thermogravimetric analyzer (NETZSCH,
Germany) with a sensitivity of 10−7 g. To reduce the heat and mass
transfer limitations between solid particles, the initial weight of samples was kept at about 6 mg. The PR samples were heated up from room
temperature to 1300 K at heating rates of 10 K/min, 20 K/min, and
30 K/min. High purity argon (Ar) was used as the carrier gas with a
flow rate of 100 mL/min. Each run was repeated at least three times
under the same conditions to decrease experimental error.
The initial decomposition temperature (Ti, K), terminal decomposition temperature (Tf, K), and peak temperature (Tpeak, K) were directly obtained from the thermogravimetry (TG) and differential thermogravimetry (DTG) curves according to previously described methods
(Fang et al., 2016).
In addition, the comprehensive devolatilization index D was used to
assess the pyrolysis performance of PR (Chen et al., 2015), which could
be calculated by Eq. (2):
D=
(−DTGmax ) × (−DTGmean )
Ti × Tpeak × ΔT1/2
(2)
where −DTGmax is the maximum weight loss rate, %/min;
−DTGmean is the mean weight loss rate, %/min; ΔT1/2 is the temperature range of (−DTGmax)/(−DTGmean) = 0.5 (half-peak width) (Fang
et al., 2016).
For the thermal decomposition composed of multiple stages, Eq. (2)
could be rewritten into Eq. (3):
D=
∑ ΔWi × Di
i
(3)
where ΔWi is the weight loss percentage of each stage in the total
47
Bioresource Technology 277 (2019) 46–54
Z. Wang et al.
β
AR
E
ln ⎛ 2 ⎞ = ln
(KAS )
−
EG (α )
RT
⎝T ⎠
weight loss (%); Di is the index D of each stage. The higher index D
represents the better pyrolysis performance.
The range of α is 0.2–0.8 with a step of 0.05. For α = const, E values
can be obtained from the slopes of the regression lines by plotting ln β
vs. 1/T and ln (β/T2) vs. 1/T.
2.2.2. Properties of pyrolysis products
The pyrolysis experiments were performed in a fixed-bed tube furnace. 10 g PR samples were loaded into the reactor and pyrolyzed for
3 h at 600 K, 700 K, 800 K, 900 K and 1000 K, respectively, with the
heating rate of 10 K/min.
The evolved gas was swept by high-purity argon at a flow rate of
500 mL/min. The condensable fraction was trapped by acetone to obtain the oil phase, whereas the non-condensable gas products was online analyzed by a Nicolet iS50 FTIR Spectrometer (Thermal Fisher
Scientific, USA). The oil phase was evaporated at 333 K under reduced
pressure using a rotary evaporator to remove acetone, resulting in biooil products. The residual solids after PR pyrolysis were denoted as
chars, and also examined using FTIR spectrometer over the wavelength
range of 4000–400 cm−1 at the resolution of better than 0.09 cm−1.
Two or three replicates were conducted for each pyrolysis experiment
to confirm the repeatability of results.
The chemical compositions of bio-oil were analyzed using an
Agilent 6890 N model Gas Chromatography–5973 model Mass
Spectrometry (GC-MS) equipped with a HP-5 capillary column (5%
phenyl 95% dimethylpolysiloxane, 30 m × 0.25 mm × 0.25 μm). The
bio-oil was diluted with chromatographic grade acetone, and then the
mixture was filtered through a 0.45 μm filter to remove all particulates.
1 μL of prepared sample was injected into the instrument with a split
ratio of 30:1. The GC oven temperature program was as follows: started
at 323 K (held 5 min); raised to 573 K at a rate of 4 K/min (held 5 min).
The compounds in the samples were identified by comparison of their
mass spectra with the NIST Database.
2.3.2. Determining the most probable mechanism function G(α)
Integral master-plot method was employed to determine G(α) of
each pyrolysis stage by comparing the shapes of theoretical masterplots and experimental master-plots (Chen et al., 2015). Since the decomposition rate of PR is quite slow at room temperature T0, Eq. (6) can
be approximated as:
G (α ) =
P (uα ) =
G (0.5) =
dα
=
f (α )
A
E
AE
⎞ dT =
exp ⎛−
P (uα )
β
βR
⎝ RT ⎠
(9)
(10)
AE
P (u 0.5)
βR
(11)
(12)
The 15 common kinetic mechanism functions G(α) are shown in
Table 1 (Vyazovkin et al., 2014). A series of theoretical master-plots can
be obtained by plotting G(α)/G(0.5) vs. α, whereas experimental
master-plots were obtained by plotting P(uα)/P(u0.5) vs. α. By comparing the deviation between the two kinds of curves, the most probable mechanism functions G(α) of various stages for PR pyrolysis can be
determined.
3. Results and discussion
(5)
3.1. Pyrolysis characteristics of PR
∫T
T
0
A
E
⎞ dT
exp ⎛−
β
⎝ RT ⎠
The TG and DTG curves of PR pyrolysis at different heating rates are
shown in Fig. 1. DTG curves contain four peaks, indicating that the
thermal decomposition process of PR can be divided into four stages. In
the first stage, water evaporated and light organic volatiles decomposed
at a low-temperature interval from room temperature to 440.0 K with a
weight loss of 4.0 wt%. The second stage which extended from 440.0 K
to 720.0 K with a weight loss of up to 40.0 wt% is the main stage of PR
pyrolysis. Most of the organics such as protein, carbohydrate and aliphatic compounds decomposed in this stage. The maximum weight loss
rate of approximately 3.0 wt%/min was reached at 595.6 K. Stage 3 was
from 720.0 K to 840.0 K with a weight loss of about 9.0 wt%. In this
stage, the residual organics with strong chemical bonds in PR continued
to decompose. In the meantime, secondary reactions such as repolymerization and condensation also occurred in this stage and formed
chars (Syed-Hassan et al., 2017). Stage 4 was from 840.0 K to 1100.0 K
with a weight loss of about 8.0 wt%, corresponding to the decomposition stage of inorganic salts such as carbonate. As the heating rate
(6)
where G(α) is the integral form of mechanism function f(α); T0 is the
initial temperature of pyrolysis reaction.
2.3.1. Calculating the activation energy E
The PR pyrolysis experiments were conducted at 10, 20 and 30 K/
min. To make the best of TG data at different heating rates, Flynn-WallOzawa (FWO) method and Kissinger-Akahira-Sunose (KAS) method
were used to calculate E values, and the corresponding expressions are
as follows (Vyazovkin et al., 2011; Wang et al., 2018a):
lnβ = ln
T
exp(−uα )
uα × (1.00198882uα + 1.87391198)
G (α )
P (uα )
=
G (0.5)
P (u 0.5)
where m0, mt and mf are is the initial weight, weight at time t and final
weight of sample at each stage, respectively, mg.
Eq. (6) is obtained by integrating Eq. (4):
α
∫0
Then dividing Eq. (9) by Eq. (11) to obtain the integral master-plot
equation:
(4)
m 0 − mt
m 0 − mf
∫0
A
E
⎞ dT ≈
exp ⎛−
β
⎝ RT ⎠
For a single-step reaction, the kinetic triple (f(α), E and A) is constant. Based on the previously obtained E values, a proper mechanism
function can be found to simulate TG data. The specific method is as
follows:
Selecting α = 0.5 as a reference, Eq. (9) can be expressed as:
where α is the conversion degree; f(α) is the differential mechanism
function; A is the pre-exponential factor, min−1; β is the constant
heating rate, K/min; E is the activation energy, kJ/mol; R is the universal gas constant, 8.314 J/(mol·K); T is the absolute temperature, K.
The conversion degree α of pyrolysis materials is defined as (Peng
et al., 2017):
G (α ) =
T
where uα = E/RTα, E is the average value of activation energy obtained
by FWO and KAS methods, Tα is the corresponding temperature at a
given α; P(uα) is the temperature integral.
In our study, Tang-Liu-Zhang-Wang-Wang approximate expression
was selected to solve P(uα) (Chen et al., 2015):
The pyrolysis of solid fuel usually belongs to heterogeneous reaction
system and proceeds under non-isothermal conditions. The pyrolysis
kinetic equation can generally be described as (Peng et al., 2017):
α=
∫T
0
2.3. Kinetic analysis
dα
A
E
⎞ dT
= exp ⎛−
f (α )
β
⎝ RT ⎠
(8)
0.0048AE
E
− 1.0516
(FWO)
RG (α )
RT
(7)
48
Bioresource Technology 277 (2019) 46–54
Z. Wang et al.
and 1.25 × 10−6 %2/(K3·min2) at 10, 20, 30 K/min, respectively. This
result shows that the pyrolysis performance of PR can be improved by
increasing the heating rate.
Table 1
Most commonly used mechanism functions for the solid-state reactions.
Symbol
Reaction
O1
O2
O3
Mechanism
order
First-order, n = 1
Second-order, n = 2
Third-order, n = 3
Power law
P2
n = 1/2
P3
n = 1/3
P4
n = 1/4
Random nucleation and growth
A1.5
Avrami-Erofeev equation
(n = 2/3)
A2
Avrami-Erofeev equation
(n = 1/2)
A3
Avrami-Erofeev equation
(n = 1/3)
Phase boundary reaction
R2
Shrink cylindrical model
(n = 1/2)
R3
Shrink spherical model
(n = 1/3)
Diffusion
D1
One-way transport
D2
Two-way transport (Valensi
equation)
D3
Three-way transport (Jander
equation)
D4
Three-way transport (G-B
equation)
G(α)
f(α)
-ln(1-α)
(1-α)−1-1
[(1-α)−2-1]/2
1-α
(1-α)2
(1-α)3
α1/2
α1/3
α1/4
2α1/2
3α2/3
4α3/4
3.2. Kinetic analysis
[-ln(1-α)]2/3
3.2.1. Calculating the activation energy
According to Eqs. (7) and (8), a linear regression analysis was performed on the TG data obtained at different heating rates via the least
square method (Figs. S1 and S2, Supplementary data). The slope of the
FWO method was −1.0516 E/R and the slope of the KAS method was
-E/R. The activation energies E of each stage at various conversion rates
were estimated from the slope of the regression lines. Results indicated
that the correlation coefficients R2 of all fitting curves exceeded 0.99
and the E values determined using the FWO and KAS methods were
basically identical (Table S1, Supplementary data), which suggests that
the calculation results were accurate and reliable.
The E values calculated using the two methods for stage 1 of the PR
pyrolysis were 87.10 kJ/mol and 84.92 kJ/mol, respectively. For stage
2, the E values were 168.85 kJ/mol and 166.90 kJ/mol, respectively.
For stage 3, the E values were 202.97 kJ/mol and 200.61 kJ/mol, respectively. For stage 4, the E values were 310.23 kJ/mol and 309.70 kJ/
mol, respectively. The order of the activation energy for various stages
was stage 1 < stage 2 < stage 3 < stage 4, which was related to the
percentage and the bond energy of activated molecules during PR
pyrolysis (Gai et al., 2013). In other words, the organics are more difficult to be decomposed, the reactions would occur at higher temperatures with a higher activation energy. This result is consistent with
the TG-DTG analysis in Section 3.1.
1.5(1-α)[-ln(1-α)]1/
3
[-ln(1-α)]1/2
2(1-α)[-ln(1-α)]1/2
[-ln(1-α)]1/3
3(1-α)[-ln(1-α)]2/3
1-(1-α)1/2
2(1-α)1/2
1-(1-α)1/3
3(1-α)2/3
1/2α
α+(1-α)ln(1-α)
α2
[-ln(1-α)]
[1-(1-α)1/3]2
1.5(1-α)2/3[1-(1α)1/3] −1
1.5[(1-α) −1/3-1] −1
1-2α/3-(1-α)2/3
−1
3.2.2. Determining the most probable mechanism function
The average E values for stages 1, 2, 3 and 4 obtained using the two
above mentioned methods were 86.01, 167.87, 201.79, and 309.86 kJ/
mol (Table 4), respectively, which were used in the integral masterplots method. For a given α, the temperature integral P(uα) can be determined according to Eq. (10). The experimental master-plots of P(uα)/
P(u0.5) vs. α for various stages of PR pyrolysis are shown in Fig. 2(a-d).
It is observed that the experimental master-plots at various heating
rates are almost identical, which indicates that each stage of PR pyrolysis can be described by a single kinetic model. The experimental
master-plots at a heating rate of 10 K/min were compared with the
theoretical master-plots, as shown in Fig. 2(e–f). The experimental
master-plot of stage 1 was almost identical to the theoretical masterplot D4. D4 was a three-dimensional diffusion model, and the corresponding mechanism function was G(α) = 1-2α/3-(1-α)2/3. The experimental master-plots of stage 2 and stage 4 were located between the
theoretical master-plots F1 and F2. Fn was a simple reaction order
model, and the corresponding mechanism function was
G(α) = [(1–α)1−n-1]/(n-1). The experimental master-plot of stage 3
increased, the TG and DTG curves shifted toward higher temperatures
due to the heat and mass transfer limitations.
The pyrolysis characteristic parameters and the comprehensive devolatilization index D for PR are listed in Tables 2 and 3, respectively.
As shown in Table 2, Ti, Tpeak, and Tf obviously shifted to higher temperatures with increasing heating rate. When the heating rate increased
from 10 K/min to 30 K/min, the maximum weight loss rates (−DTGmax)
in stages 1, 2, 3, and 4 increased from 0.52, 2.89, 1.19, and 0.60 wt
%/min to 1.45, 9.68, 3.74, and 1.77 wt%/min, respectively. This increase might be attributed to the poor thermal conductivity of PR
samples, which resulted in large temperature difference between the
inside and the surface of the sample particles as the heating rate increased (Chen et al., 2015). The rapid heat transfer in the sample
particles accelerated the release of volatiles. The heating rate had a
slight effect on the residual weight (Wr) after PR pyrolysis. From
Table 3, it is observed that the index D of each stage for PR pyrolysis
increased dramatically as the heating rate increased. The values of
index D of the whole pyrolysis process were 1.39 × 10−7, 5.40 × 10−7,
Fig. 1. TG and DTG curves of PR pyrolysis at different heating rates.
49
Bioresource Technology 277 (2019) 46–54
Z. Wang et al.
Table 2
Pyrolysis characteristic parameters of PR under different heating rates.
β
Ti
a
Tpeak1
K/min
10
20
30
K
376.7
390.5
401.2
K
411.7
424.4
425.8
a
b
c
d
e
−DTGmax1
b
%/min
0.52
1.05
1.45
c
Tpeak2
K
595.6
607.4
612.7
b
−DTGmax2
c
Tpeak3
%/min
2.89
6.04
9.68
K
761.7
772.3
773.7
b
−DTGmax3
%/min
1.19
2.31
3.74
c
Tpeak4
K
961.2
970.5
982.7
b
−DTGmax4
%/min
0.60
1.15
1.77
c
Tf
d
K
1159.4
1181.8
1194.3
Wr
e
wt.%
36.26
36.07
36.41
Ti, the initial decomposition temperature.
Tpeak1, Tpeak2, Tpeak3, and Tpeak4 are the peak temperature of Stage 1, Stage 2, Stage 3, and Stage 4, respectively.
−DTGmax1, −DTGmax2 −DTGmax3, and −DTGmax1 are the maximum weight loss rate of Stage 1, Stage 2, Stage 3, and Stage 4, respectively.
Tf, the terminal decomposition temperature.
Wr, the residual weight after PR pyrolysis.
vibration can also cause characteristic absorption peaks between 3600
and 3000 cm−1. Therefore, dehydroxylation may also occur during PR
pyrolysis.
The absorption peak near 2920 cm−1 was caused by the stretching
vibration of C–H bonds in the methyl or methylene groups. The two
peaks at 1450 cm−1 and 1400 cm−1 were attributed to the in-plane
bending vibration of the CeH bonds in the methyl or methylene groups.
The spectra suggest the possible presence of aliphatics and alkyl aromatics in the raw PR. As the pyrolysis temperature increased, cracking
of the methyl and methylene groups caused the intensities of these
characteristic absorption peaks to gradually weaken and disappear. CH4
could be generated during this process.
The band near 1310 cm−1 was attributed to eCOOH bonds. The
absorption peak at 1230 cm−1 was associated with the stretching vibration of C-N in the amide III group. The absorption peaks from 1200
to 1000 cm−1 were related to the CeO asymmetric stretching vibrations. As the pyrolysis temperature increased, the breakage of these
chemical bonds generated products such as CO, CO2 and NH3 (Chen
et al., 2015).
The absorption peaks in the fingerprint region (wavenumbers <
1000 cm−1) were associated with the out-of-plane bending vibration of
the aromatic CeH bonds, which indicates the presence of some substituted groups in aromatics and phenolics. As the pyrolysis temperature increased, the changing trends of these characteristic adsorption
peaks were consistent with those of other peaks.
was similar to the theoretical master-plot An. An was a nucleationgrowth model, and the corresponding mechanism function was
G(α) = [-ln(1-α)]n.
Based on the above results, the value of n and pre-exponential factor
A would be further determined. By inserting mechanism functions (D4,
Fn and An) into Eq. (9) could obtain:
G (α ) =
AE
2α
P (uα ) = 1 −
− (1 − α )2/3
βR
3
(13)
G (α ) =
AE
(1 − α )1 − n − 1
P (uα ) =
βR
n−1
(14)
G (α ) =
AE
P (uα ) = [−ln(1 − α )]n
βR
(15)
2/3
was plotted against EP(uα)/βR (Fig.
For stage 1, 1-2α/3-(1-α)
S3(a), Supplementary data). For stage 2 and stage 4, [(1-α)1−n-1]/(n-1)
was plotted against EP(uα)/βR at n = 1.0–2.0 with a step of 0.1. The
highest correlation coefficient of fitting curves was found at n = 2.0 and
n = 1.9, respectively (Figs. S3(b) and (c), Supplementary data). For
stage 3, [-ln(1-α)]n was plotted against EP(uα)/βR at n = 1/4, 1/3, 2/5,
1/2, 2/3, 3/4, 4/5, 1.0, and 3/2. The highest correlation coefficient of
fitting curve was found at n = 4/5 (Figs. S3(d), Supplementary data).
Next, the pre-exponential factor A was obtained from the slope of the
fitting curves. The kinetic triplets under heating rates of 20 K/min and
30 K/min were calculated using the same method, with the results
listed in Table 4. The mechanism functions for stages 1, 2, 3, and 4 were
f(α) = 3/2[(1-α) −1/3-1] −1, f(α) = (1-α)2, f(α) = 5/4(1-α)[-ln(1-α)]1/
5
, and f(α) = (1-α)1.9, respectively.
3.3.2. FTIR analysis of gaseous products
Gas products at different temperatures mainly included CH4, CO,
CO2, and NH3 according to the assignments of IR bands (Yang et al.,
2015; Zhang et al., 2014), which is consistent with our previous analysis in Section 3.3.1. The multiple peaks at 4000–3400 cm−1 and
2000–1200 cm−1 were related to the stretching vibrations of OeH from
H2O and phenol or alcohol structure. The typical “W” peak at
2400–2250 cm−1 and relative low intensity peak at 710–610 cm−1
were assigned to CO2. The intensity of these peaks was relatively strong
at 600 K and weakened sharply with the temperature increasing, indicating that dehydroxylation and decarboxylation mainly occurred at
low temperatures. The CeH stretching vibrations at 3200–2800 cm−1
revealed the presence of CH4 and C2H6 in gaseous products. Besides, the
absorption band at 1400–1200 cm−1 was caused by the in-plane
bending vibrations of CeH in CH4. CH4 was mainly produced at high
temperatures (greater than 800 K), which might be related to the
cleavage of eCH3, eCH2, and benzene ring. The bands between
2250 cm−1 and 1980 cm−1 was caused by the C^O stretching vibrations, which was indicative of the existence of CO. Carbonyls and ethers
decomposed to produce CO at low temperatures, and then the cracking
and reforming of C-O in chars promoted the production of CO with
increasing pyrolysis temperature (Hu et al., 2012; Wei et al., 2018). In
addition, the PR pyrolysis also produced large quantities of N-containing gases, such as HCN (CeH stretching vibrations at
3400–3200 cm−1) and NH3 (NeH out-of-plane bending vibration at
961 cm−1 and 937 cm−1). HCNO was represented by the peak at
3.3. Characterization of pyrolysis products
3.3.1. FTIR analysis of chars
The structure of PR and chars obtained at 600 K, 700 K, 800 K,
900 K, and 1000 K was characterized by FTIR spectrum. The assignments of absorption bands are based on the literatures (Zhu et al., 2016;
Zhu et al., 2015). It was observed that the intensities of all characteristic peaks in the spectra grew weaker as the pyrolysis temperature
increased. The FTIR spectra of the raw PR sample exhibited a broad
band in the range of 3600–3000 cm−1, which can be attributed to the
stretching vibration of NeH. The characteristic absorption peaks at
1640 cm−1 and 1540 cm−1 were primarily associated with C]O
stretching vibrations (amide I band) and NeH in-plane bending vibrations (amide II band), respectively. This result indicates the presence of
amides in the raw PR sample, which is consistent with the fact that PR
contains many proteins. As the pyrolysis temperature increased, the
intensities of these absorption peaks gradually decreased. This result
suggests that the NeH and C]O bonds continuously cracked during the
pyrolysis process, which may result in gaseous products such as CO and
NH3. All these characteristic peaks disappeared above 800 K, indicating
that the proteins were decomposed completely. This result could also be
found in other relevant work (Wei et al., 2018). Moreover, PR also
contains carboxylic acids, alcohols, and phenols. The O–H stretching
50
Bioresource Technology 277 (2019) 46–54
Z. Wang et al.
%2/(K3·min2)
1.39 × 10−7
5.40 × 10−7
1.25 × 10−6
%2/(K3·min2)
6.90 × 10−9
1.96 × 10−8
4.93 × 10−8
%
17.38
17.73
16.96
β
K/min
E
KJ/mol
A
min−1
f(α)
Stage 1
10
20
30
10
20
30
10
20
30
10
20
30
86.01
6.726 × 109
7.025 × 109
6.869 × 109
3.407 × 1014
3.149 × 1014
4.037 × 1014
2.239 × 1013
2.477 × 1013
2.599 × 1013
2.537 × 1016
2.742 × 1016
2.611 × 1016
3/2[(1-α)
Stage 3
Stage 4
167.87
201.79
309.96
R2
−1/3
-1]−1
(1-α)2
5/4(1-α)[-ln(1-α)]1/5
(1-α)1.9
0.99897
0.99908
0.99967
0.99577
0.99792
0.99924
0.99885
0.99956
0.99921
0.99947
0.99993
0.99949
around 2350 cm−1 (C]O stretching vibrations). However, this characteristic peak was covered by that of CO2, resulting in that it is difficult
to identify whether HCNO existed in gaseous products from FTIR
spectra.
3.3.3. GC-MS analysis of bio-oil
GC-MS results showed the bio-oil is an extremely complex mixture
and contains nearly 200 compounds. By comparing mass spectra with
the NIST database, about 50 compounds were identified based on their
chromatographic peak area that accounted for more than 3% of the
total area (Table S2, Supplementary data). The bio-oil was primarily
composed of hydrocarbons, nitrogen-containing species, and oxygencontaining species. The hydrocarbons included olefins and polycyclic
aromatic hydrocarbons (PAHs), and their content was the lowest (less
than 5%). Olefins might be generated by the pyrolysis of lipids in the
PR. PAHs were primarily produced by the secondary reaction of benzene derivatives such as phenol and cresol in the pyrolysis volatiles (Dai
et al., 2014). The nitrogen-containing species primarily consisted of
amides and amines, nitriles and N-containing heterocyclic compounds,
and their content exceeded 45% due to the high protein content
(45.4 wt%) in PR. Similar results were reported in previous studies on
algal pyrolysis (Yu et al., 2018). The oxygen-containing species mainly
consisted of alcohols, phenols, furans, ethers, and ketones, and their
content was approximately 50%. These undesirable oxygenated components not only led to the instability of bio-oil during storage and
transportation, but reduced its heating value. Thus, upgrading bio-oil to
transportation fuel through hydrodeoxygenation (HDO) is one of important topics for the future work. Besides, the content of isosorbide in
the oxygen-containing species was as high as 30%. This was probably
because the polyether polyol defoamer added during the penicillin
fermentation was pyrolyzed to sorbitol, which was further converted
into isosorbide by dehydration (Zhang et al., 2018).
Fig. 3 presents the relative peak area of the classified bio-oil compounds at different temperatures. Increasing temperature tended to
increase hydrocarbon content, especially PAHs. PAHs not only reduce
fuel quality but are also extremely harmful to the environment.
Therefore, it is recommended to use low temperature pyrolysis or catalytic pyrolysis to reduce the formation of PAHs in bio-oil. Amines and
amides in PR bio-oil were mainly generated by NH3/NH2·(due to the
dimerization reactions of amino acids in PR) reacting with fatty acid
(from lipids decomposition) (Chen et al., 2017). The content of these
compounds decreased gradually from 13.38% to 7.95% as pyrolysis
temperature increased from 600 K to 1000 K, which may be due in part
to the release of most of NH3/NH2·into gaseous products. In addition,
the dehydration or deamination reactions of amides were another
reason for this phenomenon. Nitriles generation mainly came from the
cleavage reactions of amino acids and dehydration reactions of amides.
Nitriles content initially increased with pyrolysis temperature; but the
value reached at the peak of 6.77% at 800 K, after which it started to
d
c
−DTGmean1, −DTGmean2, −DTGmean3, and −DTGmean4 are the mean weight loss rate of Stage 1, Stage 2, Stage 3, and Stage 4, respectively.
(ΔT1/2)1, (ΔT1/2)2, (ΔT1/2)3, and (ΔT1/2)4 are the temperature range of (−DTGmax)/(−DTGmean) = 0.5 at Stage 1, Stage 2, Stage 3, and Stage 4, respectively.
ΔW1, ΔW2, ΔW3, and ΔW4 are the weight loss percentage of Stage 1, Stage 2, Stage 3, and Stage 4 in the total weight loss, respectively.
D1, D2, D3, and D4 are the comprehensive devolatilization index of Stage 1, Stage 2, Stage 3, and Stage 4, respectively.
b
a
%/min
0.49
0.97
1.37
%2/(K3·min2)
1.02 × 10−7
3.56 × 10−7
7.08 × 10−7
%
10.03
9.11
8.00
K
54.6
56.8
62.8
%/min
1.34
2.64
3.69
%2/(K3·min2)
1.90 × 10−7
7.31 × 10−7
1.68 × 10−6
%
65.85
68.23
69.63
K
163.8
160.5
159.2
%/min
2.42
4.61
6.81
%2/(K3·min2)
2.81 × 10−8
9.31 × 10−8
2.44 × 10−7
%
6.75
4.93
5.41
K
59.6
52.4
44.1
%/min
0.50
0.77
1.27
10
20
30
Stage
Stage 2
K
117.7
140.6
124.8
D4d
−DTGmean4a
−DTGmean2a
(ΔT1/2)1b
−DTGmean1a
K/min
ΔW1c
D1d
(ΔT1/2)2b
ΔW2c
D2d
−DTGmean3a
(ΔT1/2)3b
ΔW3c
D3d
(ΔT1/2)4b
ΔW4c
D
Stage 4
Stage 3
Stage 2
Stage 1
β
Table 3
Comprehensive devolatilization index D of PR pyrolysis under different heating rates.
Table 4
Kinetic triplet for various stages of PR pyrolysis at different heating rates.
51
Bioresource Technology 277 (2019) 46–54
Z. Wang et al.
Fig. 2. (a–d) Experimental master-plots of P(uα)/P(u0.5) vs. α for various stages of PR pyrolysis at different heating rates. (a) Stage 1, (b) Stage 2, (c) Stage 3, (d) Stage
4. (e–h) Comparison of theoretical master-plots of G(α)/G(0.5) vs. α and experimental master-plots at 10 K/min. (e) Stage 1, (f) Stage 2, (g) Stage 3, (h) Stage 4.
pyrroles were mainly from the secondary cracking reactions of N-containing functional groups like pyridinic-N and pyrrolic-N in pyrolysis
chars (Debono and Villot, 2015; Lorenzetti et al., 2016; Tian et al.,
2014). N-heterocyclic compounds may also generate via the Maillard
reaction. High N-containing compounds in bio-oil can result in nitrogen-related pollution. Therefore, the removal of nitrogen from pyrolysis oil is another point for our follow-up studies. There is no specific
decrease rapidly. This was probably due to the secondary cracking reaction of nitriles, accompanying HCN release. N-heterocyclic compounds mainly included indoles, pyridines, pyrroles, pyrazines and piperidines, and its content increased significantly from 29.72% to
35.29% with increasing temperature. Indoles, pyrazines and piperidines
were mainly formed through dehydrogenation, decarboxylation, and
dehydration reactions of some amino acids, whereas pyridines and
52
Bioresource Technology 277 (2019) 46–54
Z. Wang et al.
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Fig. 3. Product selectivity (area %) of bio-oil compounds produced from PR
pyrolysis.
trend for the oxygenated compounds in bio-oil with rising temperature.
4. Conclusion
The thermal behaviors and product formation mechanism during PR
pyrolysis were investigated. The thermal decomposition of PR proceeded in four stages, and the higher heating rate can significantly
improve its pyrolysis performance. The most probable mechanism
functions for various stages were f(α) = 3/2[(1-α) −1/3-1]−1, f
(α) = (1-α)2, f(α) = 5/4(1-α)[-ln(1-α)]1/5, and f(α) = (1-α)1.9, respectively. FTIR results showed that the cleavage of NeH, C]O, CeH, CeN,
and CeO in chars resulted in the production of CH4, CO, NH3, and HCN.
The high protein content in PR resulted in a high fraction of nitrogenated compounds in bio-oil. Moreover, bio-oil also contained large
quantities of oxygenated species and a few hydrocarbons.
Acknowledgements
This work was supported by the National Natural Science
Foundation of China (No. 51508553 and No. 51478160), Beijing
Training Project for the Leading Talents in S&T (No. LJ201620), China
Postdoctoral Science Foundation Funded Project (No. 2016M591266),
and Fundamental Research Funds for the Central Universities (No. FRFTP-17-062A1).
Compliance with ethical standards
Conflict of interest
The authors declare no conflict of interest.
Ethical approval
This article does not contain any studies with human participants or
animals performed by any of the authors.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.biortech.2019.01.030.
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