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Thermal interaction analysis of isolated hemicellulose and cellulose by kinetic parameters during biomass pyrolysis
Journal Pre-proof Thermal interaction analysis of isolated hemicellulose and cellulose by kinetic parameters during biomass pyrolysis
Yanming Ding, Biqing Huang, Kaiyuan Li, Wenzhou Du, Kaihua Lu, Yansong Zhang PII:
S0360-5442(20)30117-1
DOI:
https://doi.org/10.1016/j.energy.2020.117010
Reference:
EGY 117010
To appear in:
Energy
Received Date:
16 November 2019
Accepted Date:
20 January 2020
Please cite this article as: Yanming Ding, Biqing Huang, Kaiyuan Li, Wenzhou Du, Kaihua Lu, Yansong Zhang, Thermal interaction analysis of isolated hemicellulose and cellulose by kinetic parameters during biomass pyrolysis, Energy (2020), https://doi.org/10.1016/j.energy.2020.117010
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Journal Pre-proof
Thermal interaction analysis of isolated hemicellulose and cellulose by kinetic parameters during biomass pyrolysis
Yanming Ding1, Biqing Huang1, Kaiyuan Li2, Wenzhou Du3, *, Kaihua Lu1, Yansong Zhang3, *
1.
2.
3.
Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, China
Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology,
Qingdao 266590,China
* Corresponding author. Tel: +86-15527115189.
E-mail address: [email protected] (Wenzhou Du); [email protected] (Yansong Zhang)
1
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Abstract Thermal interactions of isolated hemicellulose and cellulose were analyzed during biomass pyrolysis. Different from the previous researches focus on pyrolysis products, the current study paid attention to the kinetic parameters coupled with artificial intelligence optimization algorithm to explore whether the interactions of hemicellulose and cellulose could be ignored or not. A series of thermogravimetric experiments were conducted based on isolated hemicellulose, cellulose and their mixture at various heating rates. The experimental results showed that the peak locations of mixture pyrolysis exactly corresponded to the peak locations of isolated hemicellulose and cellulose. Furthermore, the kinetic parameters obtained from isolated hemicellulose and cellulose were applied to predict the pyrolysis behaviors of mixture, and the predicted results agreed well with experimental data. Eventually, it was speculated that the thermal interactions of isolated hemicellulose and cellulose could be ignored during the whole pyrolysis process from the point of kinetic parameters. Meanwhile, the pyrolysis behaviors of real biomass were compared with that of isolated hemicellulose and cellulose. Moreover, the effects of kinetic parameters on the thermogravimetric results were studied based on the global sensitivity analysis, indicating the activation energy and pre-exponential factor playing the most important role on the pyrolysis process of mixture.
Keywords Pyrolysis; Kinetics; Biomass; Hemicellulose; Cellulose 2
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1. Introduction It has received great attention to the thermochemical conversion of biomass over the past decades, especially for pyrolysis which plays a vital role to convert biomass into high value fuels [1-4]. Considering biomass is composed of three main components (hemicellulose, cellulose and lignin) whose contents vary with the biomass type, then it can be speculated that the total pyrolysis behavior of any biomass can be described by the each independent component pyrolysis [5-8]. In other words, since biomass pyrolysis includes complex reaction mechanisms, it can be simplified into the pyrolysis of three main components individually [6, 9, 10]. However, a critical question arises: whether the biomass pyrolysis can be represented by the simple physical addition of three main components or if the chemical or physical interactions among these components can cause the biomass pyrolysis to behave in a unique way [11]. In our current study, we will focus on two main components: hemicellulose and cellulose as well as their mixture to explore the thermal interactions during the pyrolysis process. Cellulose is highly crystalline while hemicellulose is amorphous [12]. In the previous studies, on one hand, Raveendran et al. [5] studied pyrolysis characteristics of main components with no detectable interactions among them. Yang et al. [13] found the negligible interactions for synthesized samples composed of two or three main components. Zhang et al. [14] also found that no interactions occurred between hemicellulose and cellulose by comparing the pyrolysis products under fast pyrolysis conditions. On the other hand, Hosoya et al. [15] reported the comparatively 3
Journal Pre-proof weak interactions in hemicellulose and cellulose fast pyrolysis at 800oC gasification temperature. Couhert et al. [16] observed that the interactions happened among the three main components, and the pyrolysis process was influenced by mineral matter. Qu et al. [17] studied the fast pyrolysis of xylan, cellulose and lignin and proposed an additivity law to predict pyrolysis products based on the content of main components. Wang et al. [18] stressed that the interaction of hemicellulose and cellulose not only promoted the formation of 2, 5-diethoxytetrahydrofuran but also inhibited the formation of altrose and levoglucosan. It can be seen that although the interaction of hemicellulose and cellulose has been investigated by many researchers, the conclusions are still contradicted, wherein most of the previous researches are focused on the interaction from the point of pyrolysis products. Different from this standpoint, a systematic study is conducted from the point of kinetic parameters coupled with optimization algorithm in our current research. Namely, the experimental data of the mixture pyrolysis are compared to their predicted results by using the kinetic parameters obtained from isolated hemicellulose and cellulose to explore the existence of interaction during the whole pyrolysis process. Furthermore, the global sensitivity analysis is conducted to study the effect of obtained kinetic parameters on the final predicted results of mixture pyrolysis. Actually, the further aim of our current study is the application of three isolated main components on the prediction of pyrolysis behaviors of real lignocellulosic biomass. Although there are only two components involved here, their total pyrolysis 4
Journal Pre-proof behaviors are still compared with that of real biomass to undertake an initial attempt.
2. Materials and Methods 2.1 Kinetics Analysis Thermogravimetic experiment is widely used to explore solid pyrolysis and the pyrolysis decomposition rate can be expressed as:
d E A exp f dt RT
(1)
where α is the conversion rate, f(α) is the reaction mechanism function, A is the pre-exponential factor, E is the activation energy and R represents the universal gas constant. For biomass pyrolysis, the above equation based on the reaction-order model (n-th order) can be further written as:
d n E A 1 exp dt RT
(2)
Usually, A, E and n are called as the kinetic triplet. From a mathematical viewpoint, A and E determine the location and magnitude of the reaction, while n should be responsible for the shape and symmetry of the reaction [19]. Hemicellulose and cellulose, as the main components of biomass, their reaction mechanism can be represented by:
Hemicellulose 1char 1 1 volatiles Cellulose 2 char 1 2 volatiles where v is the yield of produced char. 5
(3)
Journal Pre-proof Furthermore, the reaction rates of isolated hemicellulose and cellulose can be expressed as: n
Y dY E Y0 A exp dt RT Y0
(4)
The reaction rate of produced char is referred as: dYchar dY v dt dt
(5)
Then based on the experimental heating rate β, the total mass loss rate (MLR) of pyrolysis can be written as:
MLR
d m / m0 1 dY dYchar dT dt dt
(6)
If the interaction of hemicellulose and cellulose can be ignored ,then the reaction mechanism of their mixture can be established: 1 Hemicellulose+ 2Cellulose 1 1 + 2 2 char 1 1 1 + 2 1 2 volatiles (7)
where χ is the mass fraction of hemicellulose or cellulose in their mixture. Then the reaction rates of their mixture can be represented as: ni
2 Y dYmixture E iYi ,0 i Ai exp i dt RT i 1 Yi ,0
(8)
2. 2 Shuffled Complex Evolution global optimum algorithm Recently, many global optimum algorithms are applied to biomass pyrolysis, such as Genetic Algorithm [19, 20], Particle Swarm Optimization [21], Shuffled Complex Evolution [22, 23] and so on. The more detailed comparisons about these optimum algorithms can be found in Ref. [24-26]. In our current study, Shuffled Complex Evolution (SCE) method is applied here. The advantages of SCE is based on the 6
Journal Pre-proof synthesis of four concepts that have proved successful for global optimization: a combination of probabilistic and deterministic approaches, a clustering algorithm, systematic evolution of a complex of points spanning the space in the direction of global improvement, and a competitive evolution algorithm [27]. This method is proposed by Duan et al. [28, 29] and successfully applied to optimize thermogravimetric data by Ding et al. [22]. In the optimization process, objective function value is a vital criterion to select the fittest individual. For our current study, the objective function Φ is defined by the differences of predicted values compared to experimental data (including cumulative mass loss (CML) and mass loss rate (MLR)):
cml mlr
(9)
N cml = wCML , j j 1
CMLpred,k CMLexp,k k 1 2 1 CMLexp,k CMLexp,p p 1 k 1
(10)
N mlr = wMLR , j j 1
k 1 2 1 MLRexp,k MLRexp,p p 1 k 1
(11)
2
MLRpred,k MLRexp,k
2
where the weighted value w is set to 1, N and λ represent the number of experiments and experimental data points, respectively. Subscript exp and pred are the experimental data and predicted results, respectively.
2.3 Global sensitivity Analysis There are four parameters involved in the reaction of isolated component, including activation energy E, pre-exponential factor A, reaction order n and char yield v. To 7
Journal Pre-proof compare their effects on the final thermogravimetric results, global sensitivity analysis is conducted based on Latin Hypercube Sampling (LHS) and their rank transformation. Compared with the common local sensitivity analysis, global sensitivity analysis can examine the global response of a model over the variation of all input parameters, rather than the local response by varying one input parameter at a time with holding other parameters [30]. LHS is a typical stratified sampling approach by extracting a large amount of uncertainty and sensitivity information from a relatively small sample size, which is originally developed by McKay et al. [30-32]. Moreover, rank transformation, which can be used to linearize the underlying relationships between input parameters and output results, is coupled with the above samples. Furthermore, the quantitative relation between input parameters and output results is expressed by Spearman rank correlation coefficient as follows:
1 s 1 s R R Q i s i i s Qi i 1 i 1 i 1 s
1 s R i s Ri i 1 i 1 s
2
1 s Q i s Qi i 1 i 1 s
2
(12)
where s is the sample number. R and Q is the rank of input parameters (kinetic parameters) and output results (objective function Φ), respectively. This equation can be simplified as:
1
s
6
s s 2 1
R Q i 1
i
i
2
(13)
2.4 Thermogravimetric Experiment The used materials in the current study were xylan-based hemicellulose (C5H10O5, 8
Journal Pre-proof CAS-No.: 9014-63-5), cellulose microcrystalline (C6H10O5, CAS-No.: 9004-34-6) and their mixture with mass fraction of 1:1. All the materials were dried for 24 hours at 80°C before the experiment. Elemental analysis was conducted based on Vario EL cube by Germany Elementar and the results were listed in Table 1. Table 1 Elemental analysis of hemicellulose and cellulose (% mass, dry basis)
a Result
Elemental analysis
Hemicellulose
Cellulose
C
42.29
42.89
H
6.54
6.53
N
0.08
0.1
Oa
51.09
50.48
of O element is obtained by difference.
Thermal analyzer SDT Q600 was employed in the thermogravimetric experiment from 300 K to 1000 K at five heating rates (5, 10, 20, 40 and 60 K/min). Alumina cup without a lid was used to put the powdery sample (size less than 0.1 mm) with a pure nitrogen flow (100 mL/min) in all the experimental runs.
3. Results and Discussion 3.1 Experimental analysis The effects of heating rate on conversion rate and mass loss rate of hemicellulose, cellulose and their mixture in the thermogravimetric experiment are shown in Figure 1.
The main pyrolysis regions of hemicellulose and cellulose are in the temperature
range of 480-640 K and 520-700 K, respectively. For the pyrolysis of isolated hemicellulose and cellulose, there is only one peak occurred in 550-610 K and 610-670 K. Similar to the previous research about biomass pyrolysis [33], the peak 9
Journal Pre-proof location moves to higher temperature while its value decreases as the higher heating rate. The average residue or produced char proportion for hemicellulose and cellulose is about 0.2 and 0.1, respectively. Namely, the char yield of hemicellulose is higher than that of cellulose, which is in accordance with the experimental results of Yang et al. [9] and Patwardhan et al. [34]. 1.0
28
(a) Hemicellulose
5 K/min 10 K/min 20 K/min 40 K/min 60 K/min
24
-1000×dm/m0/dT (K-1)
0.8
0.6
α
5 K/min 10 K/min 20 K/min 40 K/min 60 K/min
0.4
0.2
20 16 12 8 4
0.0
0 400
500
600
700
800
900
400
500
Temperature (K)
600
700
800
900
Temperature (K)
1.0
35
5 K/min 10 K/min 20 K/min 40 K/min 60 K/min
30
-1000×dm/m0/dT (K-1)
(b) Cellulose
0.8
0.6
α
5 K/min 10 K/min 20 K/min 40 K/min 60 K/min
0.4
0.2
25 20 15 10 5
0.0
0 400
500
600
700
800
900
400
500
Temperature (K)
600
700
800
900
Temperature (K)
1.0
18 16
(c) 50%Hemicellulose +50%Cellulose
5 K/min 10 K/min 20 K/min 40 K/min 60 K/min
14
-1000×dm/m0/dT (K-1)
0.8
α
0.6
5 K/min 10 K/min 20 K/min 40 K/min 60 K/min
0.4
0.2
12 10 8 6 4 2
0.0
0 400
500
600
700
800
900
Temperature (K)
400
500
600
700
800
Temperature (K)
Fig.1. Pyrolysis at various heating rates: Conversion rate (left) and mass loss rate 10
900
Journal Pre-proof (right) For the pyrolysis behaviors of the mixture, two stages and two obvious peaks occur. Then the peak locations are compared to the pyrolysis behaviors of isolated hemicellulose and cellulose at specific heating rate, as shown in Fig.2. It can be seen intuitively that the first and second peaks of mixture exactly correspond to the peaks of isolated hemicellulose and cellulose, respectively. Therefore, the consistence of peak locations can be regarded as a preliminary evidence of no thermal interaction of isolated hemicellulose and cellulose during biomass pyrolysis. 35
30
10 K/min 25
-1000×dm/m0/dT (K-1)
25
-1000×dm/m0/dT (K-1)
30
Hemicellulose Cellulose 50% Hemicellulose +50% Cellulose
5 K/min
20
15
10
15
10
5
5
0 450
20
500
550
600
650
700
0 450
750
500
Temperature (K)
550
600
650
700
750
Temperature (K)
30 25 25
20 K/min
40 K/min
20
-1000×dm/m0/dT (K-1)
-1000×dm/m0/dT (K-1)
20
15
10
10
5
5
0 500
15
550
600
650
700
750
Temperature (K)
0 500
550
600
650
700
750
Temperature (K)
Fig.2. Peak location comparison at different heating rates
3.2 Thermal interaction analysis of isolated hemicellulose and cellulose based on SCE Except the peak locations, the pyrolysis behaviors of mixture during the whole 11
Journal Pre-proof temperature range should be further validated to explore the thermal interactions of isolated hemicellulose and cellulose. Then SCE algorithm is applied here. At first, the reaction kinetic parameters of isolated hemicellulose and cellulose based on Eq. (4) are obtained by SCE optimization at various heating rates, and then the optimized parameters are used to predict the pyrolysis behaviors of mixture. If the good agreement results are achieved, then it can be assumed that the thermal interactions of isolated hemicellulose and cellulose can be ignored. The parameters search range of isolated hemicellulose and cellulose based on SCE algorithm is obtained from Ding et al. [22], including the activation energy, pre-exponential factor and reaction order, as listed in Table 2. Wherein, the char yields of isolated hemicellulose and cellulose are measured to 0.2 and 0.1 in our current experiment, respectively, so their corresponding search ranges are set to 0.1-0.3 and 0.05-0.15. Eventually, the optimized parameter values are obtained and the fitting performance is shown in Fig.3. It can be seen that some deviations of peak value exist while the R2 value can reach up to 0.94 and 0.96 for isolated hemicellulose and cellulose, respectively, indicating an appropriate fitting results. Moreover, these optimized kinetic parameters can also be applied to bench-scale pyrolysis and combustion simulation which have been successfully validated in our previous studies [4, 27, 35]. Table 2 Search range of kinetic parameters and their optimized values Parameters
Initial Values
Search Range
Optimized Values
Reference Values a
lnAh[ln (s-1)]
23.51
[7.63, 39.38]
28.10
22.94
Eh (kJ/mol)
135.25
[50.00, 220.50]
155.39
131.19
12
Journal Pre-proof nh
1.00
[0.00, 3.00]
0.62
2.17
h
0.20
[0.10, 0.30]
0.25
0.18
lnAc[ln (s-1)]
38.50
[16.28, 60.72]
23.46
23.98
Ec (kJ/mol)
225.25
[96.50, 354.00]
148.46
151.73
nc
1.00
[0.00, 3.00]
0.44
0.63
c
0.10
[0.05, 0.15]
0.09
0.08
a Optimized
values from Ding et al. [22].
35 30
20 K/min
Experimental data Predicted results
Hemicellulose
φmlr =0.0345
-1000×dm/m0/dT (K-1)
25 20
R
15
2 mlr
25
-1000×dm/m0/dT (K-1)
30
=0.9655
10
Predicted total mass loss rate
5
Experimental data Predicted results
20 K/min
20 15
φmlr =0.0547 R2mlr=0.9453
Cellulose
10
Predicted total mass loss rate 5
0
500
700
40 K/min
-1000×dm/m0/dT (K-1)
500
600
800
900
Temperature (K)
30
25
700
Experimental data Predicted results
40 K/min
20
Hemicellulose
φmlr =0.0198 R2mlr=0.9802
10
Predicted total mass loss rate
5 0 -5 -10 400
-5 400
900
Experimental data Predicted results
20 15
Char 800
Temperature (K)
30 25
600
-1000×dm/m0/dT (K-1)
-10 400
0
Char
-5
600
700
R2mlr=0.9614
Cellulose
10
Predicted total mass loss rate
5
0
Char 500
φmlr =0.0386 15
Char 800
900
-5 400
500
Temperature (K)
600
700
800
900
Temperature (K)
Fig.3. Fitting performance based on optimized parameters of isolated hemicellulose (hemicellulose) and cellulose (right) Furthermore, the optimized parameters from isolated hemicellulose and cellulose are applied to predict the pyrolysis behaviors of their mixture, and the predicted pyrolysis results of mixture are compared with the experimental data, as shown in Fig.4. It is obvious that the peak locations can be exactly captured. Although there are some deviations in the peak value which could be attributed to the used optimized 13
Journal Pre-proof parameters, the overall trend of predicted pyrolysis behavior is satisfied compared with experimental data. The good agreement of predicted results and experimental data can be achieved with the R2 reaching up to 0.91. It means the parameters obtained from the isolated hemicellulose and cellulose can be applied to their mixture, indicating the thermal interactions of isolated hemicellulose and cellulose can be ignored during the whole pyrolysis process. 20
10
Experimental data Predicted results
10 K/min
Cellulose
Hemicellulose
15
φmlr =0.0560
-1000×dm/m0/dT (K-1)
-1000×dm/m0/dT (K-1)
15
20
R2mlr=0.9440
Predicted total mass loss rate
5
0
Experimental data Predicted results
20 K/min
φmlr =0.0503 10
Cellulose
Hemicellulose 5
Predicted total mass loss rate 0
Char
Char -5 400
500
700
800
900
Experimental data Predicted results
40 K/min
15
R2mlr=0.9587 Cellulose
Hemicellulose 5
600
Predicted total mass loss rate
0
800
900
Experimental data Predicted results
60 K/min
R2mlr=0.9105
10
Cellulose
Hemicellulose
Predicted total mass loss rate
5
0
Char
Char -5 400
700
Temperature (K)
φmlr =0.0895
φmlr =0.0413 10
500
20
-1000×dm/m0/dT (K-1)
-1000×dm/m0/dT (K-1)
600
-5 400
Temperature (K)
20
15
R2mlr=0.9497
500
600
700
800
900
Temperature (K)
-5 400
500
600
700
800
Temperature (K)
Fig.4. Predicted pyrolysis results of mixture based on optimized parameters (lines) compared with their experimental data (symbols) It should be noted that there are several non-negligible issues in the current paper. At first, xylan is just the important component of real hemicellulose rather than the unique one. Secondly, due to the difference of intrinsic structure between physical mixtures of hemicellulose−cellulose and real biomass, the possible chemical linkages 14
900
Journal Pre-proof within the real biomass structure may result in the pyrolysis behavior of hemicellulose and cellulose not being captured through the simple physical addition [14]. Thirdly, accurate removal of the individual component from real biomass without changing its structure is another problem [11], namely, it is hard to separate the hemicellulose and cellulose without altering their real structure in the current experiment conditions [36]. Nevertheless, the objective of this work is to explore the thermal interactions of isolated hemicellulose and cellulose during pyrolysis by the simple physical mixture.
3.3 Further application of isolated hemicellulose and cellulose on the real lignocellulosic biomass pyrolysis Actually, the further aim of our current study is the application of three isolated main components to predict the real lignocellulosic biomass pyrolysis by various proportions [13]. Although there are only two main components involved in our current study, their predicted thermogravimetric results are still applied to compare with that of real biomass. According to Collard and Blin’s study, angiosperm hemicelluloses contain mostly xylans, whereas gymnosperm hemicelluloses contain mostly glucomannans [7, 37]. Then the typical angiosperm beech wood (Fagus sylvatica) with 28.6% hemicellulose and 46.2% cellulose [38] is chosen in our current study. Correspondingly, the proportions of isolated hemicellulose and cellulose are set to the real value of beech wood to obtain the predicted thermogravimetric results based on the optimized kinetic parameters in Table 2, and then these predicted results are compared with our previous experimental data of beech wood [22, 39], as shown in Fig. 5. 15
Journal Pre-proof It can be seen that the total trend of predicted results is basically the same as that of experimental data. The whole pyrolysis behaviors can be divided into shoulder region, peak region and tail region. The predicted peak region agrees well with experimental data, which is mainly related with cellulose pyrolysis [22, 40]. In the tail region, the predicted results cannot be reproduced due to the lack of lignin in our current study. Last but not the least, the obvious deviation appears in the shoulder region, which can be attributed to three reasons. First, the proportion of hemicellulose used here is inaccurate. Although the main component in angiosperm hemicelluloses is xylan, there are still other components, such as glucomannans whose pyrolysis behaviors should be different of that of xylan. Second, the lignin is not considered here, but the interaction between hemicellulose and lignin might do exist. Third, the effects of alkalis [41, 42] and chemical or covalent linkages among different components [14] on the pyrolysis behaviors are not considered during the prediction process. Then in our next study, the thermogravimetric experiment of isolated lignin pyrolysis would be conducted and added to predict the real biomass pyrolysis behaviors along with the effects of alkalis and chemical linkages among different components. 12
12
8
6
Beech wood
4
20 K/min
10
-1000×d(m/m0)/dT (K-1)
-1000×d(m/m0)/dT (K-1)
Experimental data Predicted results
10 K/min
10
2
Experimental data Predicted results
8
6
Beech wood
4
2
0
0 400
500
600
700
800
900
Temperature (K)
400
500
600
700
800
Temperature (K)
Fig.5. Predicted pyrolysis results of mixture based on optimized parameters (lines) 16
900
Journal Pre-proof compared with experimental data of beech wood (symbols)
3.4 Global sensitivity analysis Eight kinetic parameters listed in Table 2 have an effect on the predicted thermogravimetric results of the mixture of hemicellulose and cellulose. To explore their influence degree, global sensitivity analysis is conducted based on LHS and rank transformation. 20000 sets of samples are selected from the parameter search range in Table 2. Then the first 20 sets of samples and their ranks are listed in Table 3. Furthermore, the Spearman rank correlation coefficients ρ between kinetic parameters and predicted results based on Eq. (13) are obtained and shown in Fig.6. 0.4 0.3
lnAh
lnAc
0.2 0.1
ρ
0.0 -0.1
nc
-0.2 -0.3 -0.4
nh
vh
vc
7
8
Ec Eh
-0.5 1
2
3
4
5
6
Rank of kinetic parameters
Fig.6. Sensitivity analysis The sensitivities of eight kinetic parameters are ranked in order from largest to smallest are: activation energy of hemicellulose, activation energy of cellulose, pre-exponential factor of hemicellulose, pre-exponential factor of cellulose, reaction order of cellulose, reaction order of hemicellulose, char yield of hemicellulose and 17
Journal Pre-proof char yield of cellulose. It can be seen that the kinetic triplet (activation energy, pre-exponent factor and reaction order) plays an important role on the pyrolysis process. Especially, the sensitivities of activation energy and pre-exponential factor are far higher than that of other kinetic parameters, and their influences should be paid more attention.
18
Table 3 The first 20 sets of samples of kinetic parameters and predicted results No.
Rank transformation
LHS samples lnAh
Eh
nh
vh
lnAc
Ec
nc
vc
φ
lnAh
Eh
nh
vh
lnAc
Ec
nc
vc
φ
1
33.52
124.84
0.65
0.23
56.81
167.62
0.26
0.05
2.65×109
16311
8780
4362
12747
18240
5525
1705
422
16402
2
36.44
50.27
2.93
0.14
19.37
255.93
1.46
0.14
1.93×1022
18146
32
19558
4416
1392
12384
9702
18524
19623
3
11.68
87.47
2.21
0.13
37.18
212.96
2.7
0.07
2.56
2551
4396
14704
3363
9404
9046
18028
4394
88
4
36.69
116.73
1.43
0.18
32.39
164.7
1.74
0.12
1.31×105
18307
7828
9529
7734
7251
5298
11591
13967
14637
5
27.7
173.77
1.98
0.19
29.82
191.25
2.91
0.13
6.74
12645
14519
13169
9143
6093
7360
19394
16714
1290
6
10.79
180.81
0.68
0.27
23.88
122.47
1.38
0.09
11.3
1989
15345
4562
16889
3421
2018
9218
8112
4650
7
16.47
91.18
2.87
0.21
31.9
242.1
2.41
0.1
8.99
5569
4831
19156
10619
7032
11309
16055
9099
3098
8
24.91
55.81
0.79
0.11
58.13
263.21
1.01
0.08
7.22×1010
10886
682
5268
1450
18835
12949
6716
5364
16922
9
38.08
212.62
2.98
0.17
17.02
314
0.37
0.09
6.97
19183
19077
19875
7003
333
16894
2485
8809
1463
10
38.33
140.45
2.53
0.29
31.84
159.22
1.67
0.12
16.08
19337
10611
16863
18578
7001
4872
11116
14044
8448
11
12.68
77.6
1.77
0.18
26.58
103.87
1.98
0.12
14.22
3183
3237
11830
8219
4635
573
13202
14831
7057
12
38.47
112.02
2.34
0.17
41.31
261.82
0.72
0.1
7.90×107
19428
7276
15589
7301
11267
12841
4821
9383
15812
13
38.07
97.33
0.86
0.27
43.6
314.46
0.05
0.11
1.89×1011
19173
5553
5723
17005
12296
16930
352
12619
17071
14
22.95
148.98
0.76
0.26
21.02
198.38
0.56
0.13
8.80
9650
11611
5087
15947
2132
7914
3730
16321
2947
15
33.1
159.07
0.23
0.17
41.3
336.09
0.19
0.1
18.93
16044
12795
1521
6932
11262
18609
1291
10934
10129
16
12.18
70.36
1.91
0.26
49.79
163.95
1.84
0.09
1.80×104
2867
2388
12739
15522
15084
5239
12285
8281
14251
8410
603
13224
973
6647
5608
10265
16215
15710
17
20.98
55.13
1.98
0.11
31.05
168.7
1.54
0.13
4.21×107
18
36.77
132.28
0.87
0.22
50.81
270.22
1.47
0.15
32.43
18357
9652
5774
11808
15541
13493
9792
19803
12553
19
32.85
76.44
1.21
0.13
54.96
219.95
0.94
0.12
2.22×1012
15885
3102
8035
3135
17410
9589
6278
14426
17451
20
38.16
208.16
1.84
0.29
20.72
304.69
1.46
0.07
8.31
19235
18553
12266
18772
1998
16170
9730
3479
2572
19
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4. Conclusions The thermal interaction of isolated hemicellulose and cellulose during biomass pyrolysis was studied by thermogravimetric experiment. The kinetic parameters of isolated hemicellulose and cellulose were obtained by Shuffled Complex Evolution optimization method, and then these parameters were applied to predict the pyrolysis behaviors of mixture of hemicellulose and cellulose. The peak locations of mixture pyrolysis exactly corresponded to that of isolated hemicellulose and cellulose. Furthermore, the good agreement of predicted mixture pyrolysis results and experimental data showed the thermal interactions of isolated hemicellulose and cellulose could be ignored by the physical mixture. Furthermore, the global sensitivity analysis of these kinetic parameters was conducted to explore their influence on the thermogravimetric results, and found that activation energy and pre-exponential factor played the most important role on the mixture pyrolysis process.
Acknowledges The authors would like to acknowledge financial support sponsored by National Natural Science Foundation of China (Nos. 51806202 and 51806106), Natural Science Foundation of Hubei Province of China (No. 2018CFB352), Research Fund of Key Laboratory of Mining Disaster Prevention and Control (No. MDPC201921) and Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No. CUG170672).
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Figure and table captions
Fig.1. Pyrolysis at various heating rates: Conversion rate (left) and mass loss rate (right) Fig.2. Peak location comparison at different heating rates Fig.3. Fitting performance based on optimized parameters of isolated hemicellulose (hemicellulose) and cellulose (right) Fig.4. Predicted pyrolysis results of mixture based on optimized parameters (lines) compared with their experimental data (symbols) Fig.5. Predicted pyrolysis results of mixture based on optimized parameters (lines) compared with experimental data of beech wood (symbols) Fig.6. Sensitivity analysis
Table 1 Elemental analysis of hemicellulose and cellulose (% mass, dry basis) Table 2 Search range of kinetic parameters and their optimized values Table 3 The first 20 sets of samples of kinetic parameters and predicted results
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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
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Highlights
Thermal interactions of isolated hemicellulose and cellulose are studied. Kinetic parameters are coupled with Shuffled Complex Evolution. Kinetic parameters obtained from isolated component are used to predict mixture. The predicted results are compared with real lignocellulosic biomass. Interactions of isolated hemicellulose and cellulose can be ignored.
Yanming Ding, Biqing Huang, Kaiyuan Li, Wenzhou Du, Kaihua Lu, Yansong Zhang PII:
S0360-5442(20)30117-1
DOI:
https://doi.org/10.1016/j.energy.2020.117010
Reference:
EGY 117010
To appear in:
Energy
Received Date:
16 November 2019
Accepted Date:
20 January 2020
Please cite this article as: Yanming Ding, Biqing Huang, Kaiyuan Li, Wenzhou Du, Kaihua Lu, Yansong Zhang, Thermal interaction analysis of isolated hemicellulose and cellulose by kinetic parameters during biomass pyrolysis, Energy (2020), https://doi.org/10.1016/j.energy.2020.117010
This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.
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Thermal interaction analysis of isolated hemicellulose and cellulose by kinetic parameters during biomass pyrolysis
Yanming Ding1, Biqing Huang1, Kaiyuan Li2, Wenzhou Du3, *, Kaihua Lu1, Yansong Zhang3, *
1.
2.
3.
Faculty of Engineering, China University of Geosciences, Wuhan 430074, China
School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, China
Key Laboratory of Mining Disaster Prevention and Control, Shandong University of Science and Technology,
Qingdao 266590,China
* Corresponding author. Tel: +86-15527115189.
E-mail address: [email protected] (Wenzhou Du); [email protected] (Yansong Zhang)
1
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Abstract Thermal interactions of isolated hemicellulose and cellulose were analyzed during biomass pyrolysis. Different from the previous researches focus on pyrolysis products, the current study paid attention to the kinetic parameters coupled with artificial intelligence optimization algorithm to explore whether the interactions of hemicellulose and cellulose could be ignored or not. A series of thermogravimetric experiments were conducted based on isolated hemicellulose, cellulose and their mixture at various heating rates. The experimental results showed that the peak locations of mixture pyrolysis exactly corresponded to the peak locations of isolated hemicellulose and cellulose. Furthermore, the kinetic parameters obtained from isolated hemicellulose and cellulose were applied to predict the pyrolysis behaviors of mixture, and the predicted results agreed well with experimental data. Eventually, it was speculated that the thermal interactions of isolated hemicellulose and cellulose could be ignored during the whole pyrolysis process from the point of kinetic parameters. Meanwhile, the pyrolysis behaviors of real biomass were compared with that of isolated hemicellulose and cellulose. Moreover, the effects of kinetic parameters on the thermogravimetric results were studied based on the global sensitivity analysis, indicating the activation energy and pre-exponential factor playing the most important role on the pyrolysis process of mixture.
Keywords Pyrolysis; Kinetics; Biomass; Hemicellulose; Cellulose 2
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1. Introduction It has received great attention to the thermochemical conversion of biomass over the past decades, especially for pyrolysis which plays a vital role to convert biomass into high value fuels [1-4]. Considering biomass is composed of three main components (hemicellulose, cellulose and lignin) whose contents vary with the biomass type, then it can be speculated that the total pyrolysis behavior of any biomass can be described by the each independent component pyrolysis [5-8]. In other words, since biomass pyrolysis includes complex reaction mechanisms, it can be simplified into the pyrolysis of three main components individually [6, 9, 10]. However, a critical question arises: whether the biomass pyrolysis can be represented by the simple physical addition of three main components or if the chemical or physical interactions among these components can cause the biomass pyrolysis to behave in a unique way [11]. In our current study, we will focus on two main components: hemicellulose and cellulose as well as their mixture to explore the thermal interactions during the pyrolysis process. Cellulose is highly crystalline while hemicellulose is amorphous [12]. In the previous studies, on one hand, Raveendran et al. [5] studied pyrolysis characteristics of main components with no detectable interactions among them. Yang et al. [13] found the negligible interactions for synthesized samples composed of two or three main components. Zhang et al. [14] also found that no interactions occurred between hemicellulose and cellulose by comparing the pyrolysis products under fast pyrolysis conditions. On the other hand, Hosoya et al. [15] reported the comparatively 3
Journal Pre-proof weak interactions in hemicellulose and cellulose fast pyrolysis at 800oC gasification temperature. Couhert et al. [16] observed that the interactions happened among the three main components, and the pyrolysis process was influenced by mineral matter. Qu et al. [17] studied the fast pyrolysis of xylan, cellulose and lignin and proposed an additivity law to predict pyrolysis products based on the content of main components. Wang et al. [18] stressed that the interaction of hemicellulose and cellulose not only promoted the formation of 2, 5-diethoxytetrahydrofuran but also inhibited the formation of altrose and levoglucosan. It can be seen that although the interaction of hemicellulose and cellulose has been investigated by many researchers, the conclusions are still contradicted, wherein most of the previous researches are focused on the interaction from the point of pyrolysis products. Different from this standpoint, a systematic study is conducted from the point of kinetic parameters coupled with optimization algorithm in our current research. Namely, the experimental data of the mixture pyrolysis are compared to their predicted results by using the kinetic parameters obtained from isolated hemicellulose and cellulose to explore the existence of interaction during the whole pyrolysis process. Furthermore, the global sensitivity analysis is conducted to study the effect of obtained kinetic parameters on the final predicted results of mixture pyrolysis. Actually, the further aim of our current study is the application of three isolated main components on the prediction of pyrolysis behaviors of real lignocellulosic biomass. Although there are only two components involved here, their total pyrolysis 4
Journal Pre-proof behaviors are still compared with that of real biomass to undertake an initial attempt.
2. Materials and Methods 2.1 Kinetics Analysis Thermogravimetic experiment is widely used to explore solid pyrolysis and the pyrolysis decomposition rate can be expressed as:
d E A exp f dt RT
(1)
where α is the conversion rate, f(α) is the reaction mechanism function, A is the pre-exponential factor, E is the activation energy and R represents the universal gas constant. For biomass pyrolysis, the above equation based on the reaction-order model (n-th order) can be further written as:
d n E A 1 exp dt RT
(2)
Usually, A, E and n are called as the kinetic triplet. From a mathematical viewpoint, A and E determine the location and magnitude of the reaction, while n should be responsible for the shape and symmetry of the reaction [19]. Hemicellulose and cellulose, as the main components of biomass, their reaction mechanism can be represented by:
Hemicellulose 1char 1 1 volatiles Cellulose 2 char 1 2 volatiles where v is the yield of produced char. 5
(3)
Journal Pre-proof Furthermore, the reaction rates of isolated hemicellulose and cellulose can be expressed as: n
Y dY E Y0 A exp dt RT Y0
(4)
The reaction rate of produced char is referred as: dYchar dY v dt dt
(5)
Then based on the experimental heating rate β, the total mass loss rate (MLR) of pyrolysis can be written as:
MLR
d m / m0 1 dY dYchar dT dt dt
(6)
If the interaction of hemicellulose and cellulose can be ignored ,then the reaction mechanism of their mixture can be established: 1 Hemicellulose+ 2Cellulose 1 1 + 2 2 char 1 1 1 + 2 1 2 volatiles (7)
where χ is the mass fraction of hemicellulose or cellulose in their mixture. Then the reaction rates of their mixture can be represented as: ni
2 Y dYmixture E iYi ,0 i Ai exp i dt RT i 1 Yi ,0
(8)
2. 2 Shuffled Complex Evolution global optimum algorithm Recently, many global optimum algorithms are applied to biomass pyrolysis, such as Genetic Algorithm [19, 20], Particle Swarm Optimization [21], Shuffled Complex Evolution [22, 23] and so on. The more detailed comparisons about these optimum algorithms can be found in Ref. [24-26]. In our current study, Shuffled Complex Evolution (SCE) method is applied here. The advantages of SCE is based on the 6
Journal Pre-proof synthesis of four concepts that have proved successful for global optimization: a combination of probabilistic and deterministic approaches, a clustering algorithm, systematic evolution of a complex of points spanning the space in the direction of global improvement, and a competitive evolution algorithm [27]. This method is proposed by Duan et al. [28, 29] and successfully applied to optimize thermogravimetric data by Ding et al. [22]. In the optimization process, objective function value is a vital criterion to select the fittest individual. For our current study, the objective function Φ is defined by the differences of predicted values compared to experimental data (including cumulative mass loss (CML) and mass loss rate (MLR)):
cml mlr
(9)
N cml = wCML , j j 1
CMLpred,k CMLexp,k k 1 2 1 CMLexp,k CMLexp,p p 1 k 1
(10)
N mlr = wMLR , j j 1
k 1 2 1 MLRexp,k MLRexp,p p 1 k 1
(11)
2
MLRpred,k MLRexp,k
2
where the weighted value w is set to 1, N and λ represent the number of experiments and experimental data points, respectively. Subscript exp and pred are the experimental data and predicted results, respectively.
2.3 Global sensitivity Analysis There are four parameters involved in the reaction of isolated component, including activation energy E, pre-exponential factor A, reaction order n and char yield v. To 7
Journal Pre-proof compare their effects on the final thermogravimetric results, global sensitivity analysis is conducted based on Latin Hypercube Sampling (LHS) and their rank transformation. Compared with the common local sensitivity analysis, global sensitivity analysis can examine the global response of a model over the variation of all input parameters, rather than the local response by varying one input parameter at a time with holding other parameters [30]. LHS is a typical stratified sampling approach by extracting a large amount of uncertainty and sensitivity information from a relatively small sample size, which is originally developed by McKay et al. [30-32]. Moreover, rank transformation, which can be used to linearize the underlying relationships between input parameters and output results, is coupled with the above samples. Furthermore, the quantitative relation between input parameters and output results is expressed by Spearman rank correlation coefficient as follows:
1 s 1 s R R Q i s i i s Qi i 1 i 1 i 1 s
1 s R i s Ri i 1 i 1 s
2
1 s Q i s Qi i 1 i 1 s
2
(12)
where s is the sample number. R and Q is the rank of input parameters (kinetic parameters) and output results (objective function Φ), respectively. This equation can be simplified as:
1
s
6
s s 2 1
R Q i 1
i
i
2
(13)
2.4 Thermogravimetric Experiment The used materials in the current study were xylan-based hemicellulose (C5H10O5, 8
Journal Pre-proof CAS-No.: 9014-63-5), cellulose microcrystalline (C6H10O5, CAS-No.: 9004-34-6) and their mixture with mass fraction of 1:1. All the materials were dried for 24 hours at 80°C before the experiment. Elemental analysis was conducted based on Vario EL cube by Germany Elementar and the results were listed in Table 1. Table 1 Elemental analysis of hemicellulose and cellulose (% mass, dry basis)
a Result
Elemental analysis
Hemicellulose
Cellulose
C
42.29
42.89
H
6.54
6.53
N
0.08
0.1
Oa
51.09
50.48
of O element is obtained by difference.
Thermal analyzer SDT Q600 was employed in the thermogravimetric experiment from 300 K to 1000 K at five heating rates (5, 10, 20, 40 and 60 K/min). Alumina cup without a lid was used to put the powdery sample (size less than 0.1 mm) with a pure nitrogen flow (100 mL/min) in all the experimental runs.
3. Results and Discussion 3.1 Experimental analysis The effects of heating rate on conversion rate and mass loss rate of hemicellulose, cellulose and their mixture in the thermogravimetric experiment are shown in Figure 1.
The main pyrolysis regions of hemicellulose and cellulose are in the temperature
range of 480-640 K and 520-700 K, respectively. For the pyrolysis of isolated hemicellulose and cellulose, there is only one peak occurred in 550-610 K and 610-670 K. Similar to the previous research about biomass pyrolysis [33], the peak 9
Journal Pre-proof location moves to higher temperature while its value decreases as the higher heating rate. The average residue or produced char proportion for hemicellulose and cellulose is about 0.2 and 0.1, respectively. Namely, the char yield of hemicellulose is higher than that of cellulose, which is in accordance with the experimental results of Yang et al. [9] and Patwardhan et al. [34]. 1.0
28
(a) Hemicellulose
5 K/min 10 K/min 20 K/min 40 K/min 60 K/min
24
-1000×dm/m0/dT (K-1)
0.8
0.6
α
5 K/min 10 K/min 20 K/min 40 K/min 60 K/min
0.4
0.2
20 16 12 8 4
0.0
0 400
500
600
700
800
900
400
500
Temperature (K)
600
700
800
900
Temperature (K)
1.0
35
5 K/min 10 K/min 20 K/min 40 K/min 60 K/min
30
-1000×dm/m0/dT (K-1)
(b) Cellulose
0.8
0.6
α
5 K/min 10 K/min 20 K/min 40 K/min 60 K/min
0.4
0.2
25 20 15 10 5
0.0
0 400
500
600
700
800
900
400
500
Temperature (K)
600
700
800
900
Temperature (K)
1.0
18 16
(c) 50%Hemicellulose +50%Cellulose
5 K/min 10 K/min 20 K/min 40 K/min 60 K/min
14
-1000×dm/m0/dT (K-1)
0.8
α
0.6
5 K/min 10 K/min 20 K/min 40 K/min 60 K/min
0.4
0.2
12 10 8 6 4 2
0.0
0 400
500
600
700
800
900
Temperature (K)
400
500
600
700
800
Temperature (K)
Fig.1. Pyrolysis at various heating rates: Conversion rate (left) and mass loss rate 10
900
Journal Pre-proof (right) For the pyrolysis behaviors of the mixture, two stages and two obvious peaks occur. Then the peak locations are compared to the pyrolysis behaviors of isolated hemicellulose and cellulose at specific heating rate, as shown in Fig.2. It can be seen intuitively that the first and second peaks of mixture exactly correspond to the peaks of isolated hemicellulose and cellulose, respectively. Therefore, the consistence of peak locations can be regarded as a preliminary evidence of no thermal interaction of isolated hemicellulose and cellulose during biomass pyrolysis. 35
30
10 K/min 25
-1000×dm/m0/dT (K-1)
25
-1000×dm/m0/dT (K-1)
30
Hemicellulose Cellulose 50% Hemicellulose +50% Cellulose
5 K/min
20
15
10
15
10
5
5
0 450
20
500
550
600
650
700
0 450
750
500
Temperature (K)
550
600
650
700
750
Temperature (K)
30 25 25
20 K/min
40 K/min
20
-1000×dm/m0/dT (K-1)
-1000×dm/m0/dT (K-1)
20
15
10
10
5
5
0 500
15
550
600
650
700
750
Temperature (K)
0 500
550
600
650
700
750
Temperature (K)
Fig.2. Peak location comparison at different heating rates
3.2 Thermal interaction analysis of isolated hemicellulose and cellulose based on SCE Except the peak locations, the pyrolysis behaviors of mixture during the whole 11
Journal Pre-proof temperature range should be further validated to explore the thermal interactions of isolated hemicellulose and cellulose. Then SCE algorithm is applied here. At first, the reaction kinetic parameters of isolated hemicellulose and cellulose based on Eq. (4) are obtained by SCE optimization at various heating rates, and then the optimized parameters are used to predict the pyrolysis behaviors of mixture. If the good agreement results are achieved, then it can be assumed that the thermal interactions of isolated hemicellulose and cellulose can be ignored. The parameters search range of isolated hemicellulose and cellulose based on SCE algorithm is obtained from Ding et al. [22], including the activation energy, pre-exponential factor and reaction order, as listed in Table 2. Wherein, the char yields of isolated hemicellulose and cellulose are measured to 0.2 and 0.1 in our current experiment, respectively, so their corresponding search ranges are set to 0.1-0.3 and 0.05-0.15. Eventually, the optimized parameter values are obtained and the fitting performance is shown in Fig.3. It can be seen that some deviations of peak value exist while the R2 value can reach up to 0.94 and 0.96 for isolated hemicellulose and cellulose, respectively, indicating an appropriate fitting results. Moreover, these optimized kinetic parameters can also be applied to bench-scale pyrolysis and combustion simulation which have been successfully validated in our previous studies [4, 27, 35]. Table 2 Search range of kinetic parameters and their optimized values Parameters
Initial Values
Search Range
Optimized Values
Reference Values a
lnAh[ln (s-1)]
23.51
[7.63, 39.38]
28.10
22.94
Eh (kJ/mol)
135.25
[50.00, 220.50]
155.39
131.19
12
Journal Pre-proof nh
1.00
[0.00, 3.00]
0.62
2.17
h
0.20
[0.10, 0.30]
0.25
0.18
lnAc[ln (s-1)]
38.50
[16.28, 60.72]
23.46
23.98
Ec (kJ/mol)
225.25
[96.50, 354.00]
148.46
151.73
nc
1.00
[0.00, 3.00]
0.44
0.63
c
0.10
[0.05, 0.15]
0.09
0.08
a Optimized
values from Ding et al. [22].
35 30
20 K/min
Experimental data Predicted results
Hemicellulose
φmlr =0.0345
-1000×dm/m0/dT (K-1)
25 20
R
15
2 mlr
25
-1000×dm/m0/dT (K-1)
30
=0.9655
10
Predicted total mass loss rate
5
Experimental data Predicted results
20 K/min
20 15
φmlr =0.0547 R2mlr=0.9453
Cellulose
10
Predicted total mass loss rate 5
0
500
700
40 K/min
-1000×dm/m0/dT (K-1)
500
600
800
900
Temperature (K)
30
25
700
Experimental data Predicted results
40 K/min
20
Hemicellulose
φmlr =0.0198 R2mlr=0.9802
10
Predicted total mass loss rate
5 0 -5 -10 400
-5 400
900
Experimental data Predicted results
20 15
Char 800
Temperature (K)
30 25
600
-1000×dm/m0/dT (K-1)
-10 400
0
Char
-5
600
700
R2mlr=0.9614
Cellulose
10
Predicted total mass loss rate
5
0
Char 500
φmlr =0.0386 15
Char 800
900
-5 400
500
Temperature (K)
600
700
800
900
Temperature (K)
Fig.3. Fitting performance based on optimized parameters of isolated hemicellulose (hemicellulose) and cellulose (right) Furthermore, the optimized parameters from isolated hemicellulose and cellulose are applied to predict the pyrolysis behaviors of their mixture, and the predicted pyrolysis results of mixture are compared with the experimental data, as shown in Fig.4. It is obvious that the peak locations can be exactly captured. Although there are some deviations in the peak value which could be attributed to the used optimized 13
Journal Pre-proof parameters, the overall trend of predicted pyrolysis behavior is satisfied compared with experimental data. The good agreement of predicted results and experimental data can be achieved with the R2 reaching up to 0.91. It means the parameters obtained from the isolated hemicellulose and cellulose can be applied to their mixture, indicating the thermal interactions of isolated hemicellulose and cellulose can be ignored during the whole pyrolysis process. 20
10
Experimental data Predicted results
10 K/min
Cellulose
Hemicellulose
15
φmlr =0.0560
-1000×dm/m0/dT (K-1)
-1000×dm/m0/dT (K-1)
15
20
R2mlr=0.9440
Predicted total mass loss rate
5
0
Experimental data Predicted results
20 K/min
φmlr =0.0503 10
Cellulose
Hemicellulose 5
Predicted total mass loss rate 0
Char
Char -5 400
500
700
800
900
Experimental data Predicted results
40 K/min
15
R2mlr=0.9587 Cellulose
Hemicellulose 5
600
Predicted total mass loss rate
0
800
900
Experimental data Predicted results
60 K/min
R2mlr=0.9105
10
Cellulose
Hemicellulose
Predicted total mass loss rate
5
0
Char
Char -5 400
700
Temperature (K)
φmlr =0.0895
φmlr =0.0413 10
500
20
-1000×dm/m0/dT (K-1)
-1000×dm/m0/dT (K-1)
600
-5 400
Temperature (K)
20
15
R2mlr=0.9497
500
600
700
800
900
Temperature (K)
-5 400
500
600
700
800
Temperature (K)
Fig.4. Predicted pyrolysis results of mixture based on optimized parameters (lines) compared with their experimental data (symbols) It should be noted that there are several non-negligible issues in the current paper. At first, xylan is just the important component of real hemicellulose rather than the unique one. Secondly, due to the difference of intrinsic structure between physical mixtures of hemicellulose−cellulose and real biomass, the possible chemical linkages 14
900
Journal Pre-proof within the real biomass structure may result in the pyrolysis behavior of hemicellulose and cellulose not being captured through the simple physical addition [14]. Thirdly, accurate removal of the individual component from real biomass without changing its structure is another problem [11], namely, it is hard to separate the hemicellulose and cellulose without altering their real structure in the current experiment conditions [36]. Nevertheless, the objective of this work is to explore the thermal interactions of isolated hemicellulose and cellulose during pyrolysis by the simple physical mixture.
3.3 Further application of isolated hemicellulose and cellulose on the real lignocellulosic biomass pyrolysis Actually, the further aim of our current study is the application of three isolated main components to predict the real lignocellulosic biomass pyrolysis by various proportions [13]. Although there are only two main components involved in our current study, their predicted thermogravimetric results are still applied to compare with that of real biomass. According to Collard and Blin’s study, angiosperm hemicelluloses contain mostly xylans, whereas gymnosperm hemicelluloses contain mostly glucomannans [7, 37]. Then the typical angiosperm beech wood (Fagus sylvatica) with 28.6% hemicellulose and 46.2% cellulose [38] is chosen in our current study. Correspondingly, the proportions of isolated hemicellulose and cellulose are set to the real value of beech wood to obtain the predicted thermogravimetric results based on the optimized kinetic parameters in Table 2, and then these predicted results are compared with our previous experimental data of beech wood [22, 39], as shown in Fig. 5. 15
Journal Pre-proof It can be seen that the total trend of predicted results is basically the same as that of experimental data. The whole pyrolysis behaviors can be divided into shoulder region, peak region and tail region. The predicted peak region agrees well with experimental data, which is mainly related with cellulose pyrolysis [22, 40]. In the tail region, the predicted results cannot be reproduced due to the lack of lignin in our current study. Last but not the least, the obvious deviation appears in the shoulder region, which can be attributed to three reasons. First, the proportion of hemicellulose used here is inaccurate. Although the main component in angiosperm hemicelluloses is xylan, there are still other components, such as glucomannans whose pyrolysis behaviors should be different of that of xylan. Second, the lignin is not considered here, but the interaction between hemicellulose and lignin might do exist. Third, the effects of alkalis [41, 42] and chemical or covalent linkages among different components [14] on the pyrolysis behaviors are not considered during the prediction process. Then in our next study, the thermogravimetric experiment of isolated lignin pyrolysis would be conducted and added to predict the real biomass pyrolysis behaviors along with the effects of alkalis and chemical linkages among different components. 12
12
8
6
Beech wood
4
20 K/min
10
-1000×d(m/m0)/dT (K-1)
-1000×d(m/m0)/dT (K-1)
Experimental data Predicted results
10 K/min
10
2
Experimental data Predicted results
8
6
Beech wood
4
2
0
0 400
500
600
700
800
900
Temperature (K)
400
500
600
700
800
Temperature (K)
Fig.5. Predicted pyrolysis results of mixture based on optimized parameters (lines) 16
900
Journal Pre-proof compared with experimental data of beech wood (symbols)
3.4 Global sensitivity analysis Eight kinetic parameters listed in Table 2 have an effect on the predicted thermogravimetric results of the mixture of hemicellulose and cellulose. To explore their influence degree, global sensitivity analysis is conducted based on LHS and rank transformation. 20000 sets of samples are selected from the parameter search range in Table 2. Then the first 20 sets of samples and their ranks are listed in Table 3. Furthermore, the Spearman rank correlation coefficients ρ between kinetic parameters and predicted results based on Eq. (13) are obtained and shown in Fig.6. 0.4 0.3
lnAh
lnAc
0.2 0.1
ρ
0.0 -0.1
nc
-0.2 -0.3 -0.4
nh
vh
vc
7
8
Ec Eh
-0.5 1
2
3
4
5
6
Rank of kinetic parameters
Fig.6. Sensitivity analysis The sensitivities of eight kinetic parameters are ranked in order from largest to smallest are: activation energy of hemicellulose, activation energy of cellulose, pre-exponential factor of hemicellulose, pre-exponential factor of cellulose, reaction order of cellulose, reaction order of hemicellulose, char yield of hemicellulose and 17
Journal Pre-proof char yield of cellulose. It can be seen that the kinetic triplet (activation energy, pre-exponent factor and reaction order) plays an important role on the pyrolysis process. Especially, the sensitivities of activation energy and pre-exponential factor are far higher than that of other kinetic parameters, and their influences should be paid more attention.
18
Table 3 The first 20 sets of samples of kinetic parameters and predicted results No.
Rank transformation
LHS samples lnAh
Eh
nh
vh
lnAc
Ec
nc
vc
φ
lnAh
Eh
nh
vh
lnAc
Ec
nc
vc
φ
1
33.52
124.84
0.65
0.23
56.81
167.62
0.26
0.05
2.65×109
16311
8780
4362
12747
18240
5525
1705
422
16402
2
36.44
50.27
2.93
0.14
19.37
255.93
1.46
0.14
1.93×1022
18146
32
19558
4416
1392
12384
9702
18524
19623
3
11.68
87.47
2.21
0.13
37.18
212.96
2.7
0.07
2.56
2551
4396
14704
3363
9404
9046
18028
4394
88
4
36.69
116.73
1.43
0.18
32.39
164.7
1.74
0.12
1.31×105
18307
7828
9529
7734
7251
5298
11591
13967
14637
5
27.7
173.77
1.98
0.19
29.82
191.25
2.91
0.13
6.74
12645
14519
13169
9143
6093
7360
19394
16714
1290
6
10.79
180.81
0.68
0.27
23.88
122.47
1.38
0.09
11.3
1989
15345
4562
16889
3421
2018
9218
8112
4650
7
16.47
91.18
2.87
0.21
31.9
242.1
2.41
0.1
8.99
5569
4831
19156
10619
7032
11309
16055
9099
3098
8
24.91
55.81
0.79
0.11
58.13
263.21
1.01
0.08
7.22×1010
10886
682
5268
1450
18835
12949
6716
5364
16922
9
38.08
212.62
2.98
0.17
17.02
314
0.37
0.09
6.97
19183
19077
19875
7003
333
16894
2485
8809
1463
10
38.33
140.45
2.53
0.29
31.84
159.22
1.67
0.12
16.08
19337
10611
16863
18578
7001
4872
11116
14044
8448
11
12.68
77.6
1.77
0.18
26.58
103.87
1.98
0.12
14.22
3183
3237
11830
8219
4635
573
13202
14831
7057
12
38.47
112.02
2.34
0.17
41.31
261.82
0.72
0.1
7.90×107
19428
7276
15589
7301
11267
12841
4821
9383
15812
13
38.07
97.33
0.86
0.27
43.6
314.46
0.05
0.11
1.89×1011
19173
5553
5723
17005
12296
16930
352
12619
17071
14
22.95
148.98
0.76
0.26
21.02
198.38
0.56
0.13
8.80
9650
11611
5087
15947
2132
7914
3730
16321
2947
15
33.1
159.07
0.23
0.17
41.3
336.09
0.19
0.1
18.93
16044
12795
1521
6932
11262
18609
1291
10934
10129
16
12.18
70.36
1.91
0.26
49.79
163.95
1.84
0.09
1.80×104
2867
2388
12739
15522
15084
5239
12285
8281
14251
8410
603
13224
973
6647
5608
10265
16215
15710
17
20.98
55.13
1.98
0.11
31.05
168.7
1.54
0.13
4.21×107
18
36.77
132.28
0.87
0.22
50.81
270.22
1.47
0.15
32.43
18357
9652
5774
11808
15541
13493
9792
19803
12553
19
32.85
76.44
1.21
0.13
54.96
219.95
0.94
0.12
2.22×1012
15885
3102
8035
3135
17410
9589
6278
14426
17451
20
38.16
208.16
1.84
0.29
20.72
304.69
1.46
0.07
8.31
19235
18553
12266
18772
1998
16170
9730
3479
2572
19
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4. Conclusions The thermal interaction of isolated hemicellulose and cellulose during biomass pyrolysis was studied by thermogravimetric experiment. The kinetic parameters of isolated hemicellulose and cellulose were obtained by Shuffled Complex Evolution optimization method, and then these parameters were applied to predict the pyrolysis behaviors of mixture of hemicellulose and cellulose. The peak locations of mixture pyrolysis exactly corresponded to that of isolated hemicellulose and cellulose. Furthermore, the good agreement of predicted mixture pyrolysis results and experimental data showed the thermal interactions of isolated hemicellulose and cellulose could be ignored by the physical mixture. Furthermore, the global sensitivity analysis of these kinetic parameters was conducted to explore their influence on the thermogravimetric results, and found that activation energy and pre-exponential factor played the most important role on the mixture pyrolysis process.
Acknowledges The authors would like to acknowledge financial support sponsored by National Natural Science Foundation of China (Nos. 51806202 and 51806106), Natural Science Foundation of Hubei Province of China (No. 2018CFB352), Research Fund of Key Laboratory of Mining Disaster Prevention and Control (No. MDPC201921) and Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No. CUG170672).
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Figure and table captions
Fig.1. Pyrolysis at various heating rates: Conversion rate (left) and mass loss rate (right) Fig.2. Peak location comparison at different heating rates Fig.3. Fitting performance based on optimized parameters of isolated hemicellulose (hemicellulose) and cellulose (right) Fig.4. Predicted pyrolysis results of mixture based on optimized parameters (lines) compared with their experimental data (symbols) Fig.5. Predicted pyrolysis results of mixture based on optimized parameters (lines) compared with experimental data of beech wood (symbols) Fig.6. Sensitivity analysis
Table 1 Elemental analysis of hemicellulose and cellulose (% mass, dry basis) Table 2 Search range of kinetic parameters and their optimized values Table 3 The first 20 sets of samples of kinetic parameters and predicted results
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Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:
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Highlights
Thermal interactions of isolated hemicellulose and cellulose are studied. Kinetic parameters are coupled with Shuffled Complex Evolution. Kinetic parameters obtained from isolated component are used to predict mixture. The predicted results are compared with real lignocellulosic biomass. Interactions of isolated hemicellulose and cellulose can be ignored.