Article pubs.acs.org/EF
Quantitative Model of Interactions in the Thermal Decomposition of Key Refuse-Derived Fuel Components Qun X. Huang,* Ru P. Wang, Li J. Zhang, Yong Chi, and Jian H. Yan State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, People’s Republic of China ABSTRACT: Advanced thermal treatment of refuse-derived fuels (RDFs) necessitates accurate determination of the key component fractions and comprehensive understanding of the decomposition characteristics during thermal conversion. In this paper, the linear weighted sum method is employed to retrieve mass fractions of key components in different municipal solid waste (MSW)-derived fuel pellets through thermogravimetric (TG) analysis. A new Gaussian-fitting-based adjusting model is proposed to quantitatively assess the effect of the interaction on the decomposition of individual components based on differential thermogravimetric (DTG) analysis. Results show that the mass fractions of combustible key components can be determined from DTG curves and by applying the Gaussian-fitting-based adjusting model, the effects of the interaction on the decomposition of polyethylene (PE), polypropylene (PP), polystyrene (PS), polyvinyl chloride (PVC), and cellulose can be identified. It is found that, after mixing PE into RDFs, both the reaction time and activation energy of PE are decreased. The degradation of PVC starts at a higher temperature within the temperature range from 200 to 380 °C, and its reaction time is decreased by 50% within the temperature range from 380 to 500 °C. The activation energy of cellulose is slightly increased. The model proposed in this paper could be a promising method to evaluate the interaction between different key components in mixed samples for optimizing the parameters of the thermal conversion system.
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INTRODUCTION Waste treatment has become a big problem in many developing countries, especially those with huge populations and limited land resources, such as China. In 2012, the World Bank forecasted that, until 2025, more than 40% of the increase in global municipal solid waste (MSW) generation would belong to the countries located in east Asia and the pacific region.1 According to statistics, more than 160 million tons of MSW had been collected from urban cities in 2011 in China.2 Despite its rapid volume reduction capability and global advances in air pollution control, direct mass burning continues to have a poor public image because of the potential risk to the health of local residents that might result from the emission of pollutants, especially dioxins and toxic heavy metals. To alleviate such public concerns, many researchers have devoted themselves to converting waste to refuse-derived fuels (RDFs), which then will be used as feedstock to advanced thermal treatment, such as pyrolysis and gasification, which are considered to be more environmentally friendly.3,4 In comparison to original waste, RDF has a higher heating value and a more uniform size and density and is more convenient for transportation and bulk storage.5 However, even if their physical appearance is quite similar, the thermochemical characteristics of RDFs may vary strongly with the original waste feed and production processes.6 Although the ultimate and proximate analyses are commonly used, the key components of RDFs with identical properties should be understood in sufficient detail for optimizing the thermal conversion system. Moreover, previous studies7−9 have declared that small differences in component fractions may result in major variations in pyrolysis and gasification, including the initial reaction temperature, activation energy, and total reaction time. According to the technical specification EN 15440:2011,10 four methods can be employed to determine the biomass (mostly cellulose and hemicellulose) and fossil © 2013 American Chemical Society
[polyethylene (PE) and polyvinyl chloride (PVC) in particular] fractions in waste-recovered fuels, but none of these methods is well-suited for routine analysis in plants consuming large amounts of RDF. Fellner and co-workers11,12 proposed a balance method based on five mass balances and one energy balance to calculate the portion of biogenic and fossil organic material in the feed of a waste-to-energy plant. Cozzani et al.13 proposed a weighted sum method to calculate the fractions of key components through thermogravimetric (TG) curve fitting. This method assumed that the mass loss caused by thermal degradation of RDF could be considered as the linear weighed sum of the decomposition of individual components under the same conditions. Although the weighted sum method has been applied to retrieve the composition of RDF6,14 and biomass,15 the thermochemical interactions between these components should also be considered. Sørum et al. found that HCl, released from the dehydrochlorination of PVC in an inert atmosphere, could enhance the reactivity of cellulosic matter.16 Dong et al.17 and Kim et al.18 pointed out that the mixture of PE and wood could promote the production of light gas during pyrolysis. Caglar et al.19 also observed that the yield of gas, liquid, and solid products during pyrolysis would be affected significantly by the interaction between hazelnut shells and PE. Because of the lack of available mathematical models, no quantitative analysis has ever been reported to study the effect of the interactions on the thermal decomposition of individual components mixed in RDFs. In this paper, the linear weighted sum method is employed to derive the mass fractions of the key components in mixed waste. Meanwhile, a new GaussianReceived: February 8, 2013 Revised: December 16, 2013 Published: December 17, 2013 1213
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Figure 1. Pictures of the samples: (a) NY-RDF, (b) ZJ-RDF, and (c) SH-RDF.
fitting-based adjusting model is proposed to quantitatively study the effect of the interaction on the activation energy and reaction time of key components. The thermal degradation behavior of three different MSW-derived fuel pellets is studied in an inert environment, and the discussion presented here is expected to help to optimize the advance thermal waste treatment system.
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squared algorithm can be used to obtain the optimized mass fractions by minimizing the following equation: errlinear fit
MODELING
dα dα1 + ... + μn n = dt dt
n
∑ μi i=1
dαi dt
w − w∞ × 100% w0 − w∞
n
2
(5)
where yi is the mass loss rate of the ith component and Ai, σi, and Tc,i are the peak height, half-width, and peak temperature associated with the maximum mass losing rate, respectively. The value of Tc,i can be used to evaluate the activation energy of individual components during pyrolysis, i.e., the smaller the Tc,i is, the lower the activation energy is expected. Similarly, σi is corresponding to the total reaction time; i.e., the larger the σi value, the slower the reaction. T is the instantaneous reaction temperature. Exceptionally, the decomposition rate of lignin during the whole temperature range from 200 to 600 °C is very slow, and its DTG curve represents an irregular shape, which cannot be formulated by a Gaussian function. Therefore, its original DTG data are used in the following discussion. Then, the Gaussian function of each component is summarized to simulate the DTG curves of RDFs. The DTG peak temperature and half-width for each component are adjusted to obtain the optimal results as
(1)
(2)
and
⎛ E ⎞ dα n ⎟(1 − α) = − A exp⎜ − ⎝ RT ⎠ dt
dαi , j ⎤ ∑ μi ⎥ dt ⎥⎦ i=1 (4)
yi = Ai exp[−2.77(T − Tc, i)2 /σi 2]
where γ is the mass loss rate of RDF under a certain atmosphere and heating rate, μi is weight loss contribution coefficient of key component i (i = 1, 2, ..., n), and αi is the residual weight fraction during the thermochemical reaction, which is typically expressed as
α=
⎡ dαRDF, j − ∑⎢ ⎢ dt j=1 ⎣ m
where errlinear fit characterizes the discrepancy between the calculated and measured mass loss rate and μi that can minimize errlinear fit is the expected mass fraction of key component i. dαRDF,j/dt and γj mean the measured and calculated mass loss rates at the temperature of j, respectively. To quantitatively describe the effect of interactions on the decomposition of key components mixed in RDF, a mathematical model should be developed. By taking a deep look at the differential thermogravimetric (DTG) peaks of key components, we can find that the shapes are similar for most components and can be fitted with Gaussian functions. Therefore, in this paper, a mathematical model is developed on the basis of the manipulation of the DTG peak shape. First, the individual DTG curve of each component is formulated with the Gaussian function as
According to the linear weighted sum model proposed by Cozzani et al.,13,14 RDF can be treated as a mixture of biomass components and fossil carbon-originated components. The biomass components, such as paper and wood, can be represented by cellulose, hemicellulose, and lignin.20,21 The fossil carbon-originated components typically consist of polyamide, polycarbonate, PE, polypropylene (PP), polystyrene (PS), and PVC.6 In this study, cellulose, hemicellulose, and lignin are selected to represent the composition of biomass in RDF because the paper and wood are generally the dominant fractions. All fossil carbonoriginated components are examined for their relevance as representatives, but more reliable results are obtained when PE, PP, PS, and PVC are used. According to the linear weighted sum method, the thermal decomposition of each key component is assumed to be independent and the mass loss rate of RDF is considered as the weighted sum of the mass loss rate of its components as
γ = μ1
⎡ dαRDF, j ⎤2 = ∑⎢ − γj ⎥ = ⎣ dt ⎦ j=1 m
(3)
φ = μl
where w, w0, and w∞ are the instantaneous mass, primary mass, and final mass of the sample during decomposition, respectively, t and T are the time and thermodynamic temperature, respectively, A is the pre-exponential factor, and n, E, and R are the reaction order, activation energy, and ideal gas constant, respectively. The mass fraction of each component in RDF can be retrieved from its contribution to the total weight loss by fitting the measured TG curves with the sum of curves of key components. Here, the least
dα l + dt
n
∑ μi Ai exp{−2.77[(T − Tc′, i)/σi′]2 } i=1
(6)
where φ is the calculated mass loss rate of RDF and T′c,i and σ′i are the adjusted DTG peak temperature and half-width of the ith component as T′c,i = Tc,i + ΔT and σ′i = σi + Δσ, respectively. ΔT and Δσ are the adjustments of the peak temperature and half-width. The optimum ΔT and Δσ are obtained through solving a nonlinear function by minimizing the difference between the calculated and measurement 1214
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results. μl and dαl/dt are the mass fraction and mass loss rate of lignin, respectively m
errGaussian fit =
∑
daRDF, j
m
=
∑ j=1
daRDF, j dt
− φj
dt
j=1
− μl
energy and mass balance, they are insufficient to determine the ratios of the key components contained in RDFs for characterizing the thermochemical behavior of the RDF during pyrolysis or gasification. TG Analysis. TG analysis was carried out using a simultaneous thermal analyzer NETSCH STA409 equipped with a vertical sample carrier and a reference crucible. Samples were crushed and sieved into particles of size less than 0.25 mm to achieve uniform temperature distribution during analysis. For each test, 5 mg of sample was heated at a heating rate of 10 °C/min from 25 to 800 °C. The inert atmosphere was maintained by purified argon gas with a flow rate of 50 mL/ min. Before analysis, a calibration test with empty crucibles was conducted to obtain the background signal, which was then subtracted from sample measurement data. For each sample, three tests were carried out and the obtained DTG curves were averaged. Direct Linear Weighted Summarized Results. According to the above analysis, eight species (two kinds of cellulose, hemicellulose, lignin, PE, PP, PS, and PVC) with known thermochemical properties are selected as key components to represent the combustibles contained in RDFs. Thermochemical properties of these species under different environments and heating rates have been studied intensively, and previous reported data are used in this paper. The data of PP, PS, PE, and PVC are cited from Jinno et al.;22 the data of lignin, second cellulose, and hemicellulose are cited from Yang et al.;23 and the data of first cellulose are cited from Stenseng et al.24 The DTG curves for these key components are plotted in Figure 2.
dαl, j dt
n
∑ μi Ai
−
i=1
exp{ −2.77[(T − Tc,′ i)/σi′]2 } (7) where φj means the simulated mass loss rate by the Gaussian-fittingbased adjusting model at the temperature of j. The optimal ΔT and Δσ can be obtained by minimizing errGassian fit through the iterative nonlinear optimizing method.
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RESULTS AND DISCUSSION Samples. To evaluate the proposed method, the pyrolysis behavior of three different RDF samples provided by waste utilization companies in New York City, U.S.A. (NY-RDF), and Zhejiang province (ZJ-RDF) and Shanghai (SH-RDF), China, were studied. The production processes of these three RDFs are similar. First, pretreated MSW was screened to remove noncombustible materials, such as metal and glass, and then it was dried, shredded, and compressed to small pellets at 100 °C and 25 MPa. The appearance of three RDFs is shown in Figure 1, and the major components of these three RDFs provided by manufacturers are given in Table 1. RDF from New York Table 1. Composition of Three RDFs samples
paper (wt %)
plastic (wt %)
wood (wt %)
textile (wt %)
others (wt %)
NY-RDF ZJ-RDF SH-RDF
40.4 29.0 22.3
40.5 37.9 19.4
13.0 28.0 50.6
4.6 3.0 5.2
1.5 2.1 2.5
contains the highest contents of paper (40.4%) and plastic (40.5%) compared to the other samples. It is a typical sample from sorted MSW in industrialized countries. The plastic content of ZJ-RDF is much higher than the paper content, and it can be considered as the selected valuable components in unsorted MSW in medium-developed cities, where plastic bags are widely used. The extremely high wood content in SH-RDF indicates that wood wastes are deliberately added to improve its heating value. All RDFs contain a small portion of noncombustible materials, which are mainly small bricks, glass, and metal. Ultimate and proximate analyses, measured with 5ECHN2000 and 5E-MAG6700 analyzers, respectively, are listed in Table 2. As expected, the volatile matter and fixed carbon of NY-RDF are much higher than those of the other two samples. Although proximate and ultimate analyses can be used to build
Figure 2. DTG profiles for the various key components.
Under the inert condition used in this paper, the decomposition of all eight key components begins at around 200 °C and ends before 600 °C. In Figure 2, it can be observed that the biomass species or lignocellulosic materials decompose at a temperature between 200 and 400 °C, while the fossil carbon-
Table 2. Proximate and Ultimate Analyses proximate analysis (as received; wt %)
a
a
samples
moisture
ash
VM
NY-RDF ZJ-RDF SH-RDF
5.0 5.2 6.1
10.7 28.5 35.6
71.6 56.6 49.0
ultimate analysis (as received; wt %)
b
LHV
12.7 9.7 9.3
20.2 15.8 10.8
FC
d
C
H
O
N
S
Cl
47.7 37.7 28.5
6.1 4.5 3.6
28.2 23.1 25.3
1.3 0.5 0.5
0.3 0.1 0.1
0.7 0.4 0.3
VM = volatile matter. bFC = fixed carbon. dLHV = lower heating value (MJ/kg). 1215
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originated species decompose at a higher temperature range from 350 to 550 °C. The single-peaked DTG profile for these key components looks very similar, except for PVC and lignin. PVC has two distinct weight loss peaks at 295 and 460 °C, respectively. The degradation of lignin spans the whole temperature range from 200 to 600 °C because of its relatively big molecular size and refractory structure. The fitted DTG curves simulated by the linear sum method are plotted in Figures 4−6 along with measured curves for the three RDFs. Similar to previous research by Cozzani et al.,13 the fitted curves are in good agreement with the measured curves. As expected, the mass loss rate curves for three RDFs exhibit two distinctive peaks: the first peak near 350 °C is contributed by the decomposition of cellulose and hemicellulose and the dehydrochlorination of PVC (first step in PVC decomposition), and the second weight loss peak is associated with the decomposition of PE, PS, PP, and PVC (second step in PVC decomposition). The retrieved mass fractions of these key components for each RDF have been illustrated in Figure 3. Each RDF sample
Figure 4. DTG curve of NY-RDF simulated by direct linear fitting.
Figure 5. DTG curve of ZJ-RDF simulated by direct linear fitting. Figure 3. Component fractions of the three RDFs.
peak within 400−520 °C is much wider than the measured peak, as shown in Figure 6. It implies that the reaction time of the key components has been reduced when mixed in RDF. The maximum differences in the mass loss rate between measured and fitted values for NY-RDF, ZJ-RDF, and SH-RDF are −0.62%/min at 350 °C, 0.66%/min at 495 °C, and −0.57%/min at 355 °C, respectively. The differences reflect the
contains only several key components, and during fitting, the key component with a mass ratio less than 4% is discarded as a minor component. All three RDFs contain PVC and lignin components. There is no PP in NY-RDF and SH-RDF, no PS and hemicellulose in ZJ-RDF and SH-RDF, and no PE in ZJRDF. The first type of cellulose appears in NY-RDF and SHRDF, while the second type of cellulose appears only in ZJRDF. The total ratio of fossil carbon-generated species (PE, PP, PS, and PVC) is 42.82, 39.26, and 18.37% for NY-RDF, ZJRDF, and SH-RDF, respectively, and they are proportional to the corresponding plastic content in Table 1. SH-RDF contains the highest ratio of lignin because of its high content of wood. Although the key component ratios can be determined successfully by the direct linear fitting method, the deviation of the peak temperature between measured and fitted curves can still be noticed. For example, in comparison to the measured DTG curve, the fitted DTG curve for NY-RDF, shown in Figure 4, within the temperature range from 380 to 520 °C, shifts approximately 10 °C to higher temperatures. The same phenomenon happens to ZJ-RDF, whose fitted DTG curve shown in Figure 5 shifts approximately 15 °C also to higher temperatures. This phenomenon indicates that the decomposition of individual plastic key components has been enhanced when mixed in RDF. For SH-RDF, the fitted DTG
Figure 6. DTG curve of SH-RDF simulated by direct linear fitting. 1216
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interaction between these key components during thermochemical reaction. Gaussian-Function-Based Fitting Results. To evaluate the effect of the interaction on the degradation of individual key components quantitatively, the DTG curves for each key component are described with Gaussian functions, as shown in Figure 2. The results are listed in Table 3. Here, the two-stage Table 3. Parameters of Gaussian Functions for Key Components components
TC (°C)
σ (°C)
A (wt %/min)
R2
PE PS PP PVC (200−380 °C) PVC (380−600 °C) first cellulose second cellulose hemicellulose
459.7 417.2 446.3 299.0 464.5 335.4 353.0 265.0
26.81 25.95 29.91 26.62 29.42 18.84 20.44 20.68
−0.1397 −0.1531 −0.1276 −0.0985 −0.0367 −0.2066 −0.1880 −0.1156
0.9884 0.9942 0.9883 0.9937 1.0000 0.9990 0.9973 0.9276
Figure 8. DTG curve of ZJ-RDF simulated by the Gaussian-fittingbased adjusting model.
degradation of PVC is described by two Gaussian functions between 200 and 380 °C and between 380 and 600 °C, respectively. The correlation coefficients R2 in Table 3 indicate that the thermal degradation of the key components for the condition discussed in this paper can be formulated by the Gaussian function very precisely, except for the lignin, the decomposition of which under inert conditions is very slow during the whole temperature range from 200 to 600 °C. Then, the Gaussian functions for all key components are summarized with the weights obtained by the above direct linear fitting. During summation, Tc and σ of the Gaussian function are adjusted to obtain the optimized results according to eqs 6 and 7. The recalculated DTG curves from Gaussian functions for three RDFs are plotted in Figures 7−9 along with measured
Figure 9. DTG curve of SH-RDF simulated by the Gaussian-fittingbased adjusting model.
κGaussian fit =
1 m
m
∑ j=1
dαRDF, j dt
− φj (9)
Table 4. Fitting Errors for Linear and Gaussian Methods
curves. The absolute errors calculated from the following equations for linear and Gaussian methods are given in Table 4: 1 m
m
∑ j=1
dαRDF, j dt
NY-RDF
ZJ-RDF
SH-RDF
0.272 0.075
0.253 0.081
0.190 0.067
where κlinear fit and κGassian fit are the average values of absolute deviations between experimental and calculated results for the direct linear fitting method and Gaussian-fitting-based adjusting model, respectively. In comparison to direct linear fitting (Figures 4−6), the accuracy has been improved significantly, especially within the temperature range from 380 to 500 °C. The deviation mainly appears after 550 °C when the decomposition of key components in RDF has almost been completed. Because the organic compounds have almost decomposed completely before 550 °C, the reason leading to this deviation may be that the inorganic matter in RDF also starts to decompose at this temperature. According to the principle of TG analysis, the shift of the DTG peak temperature ΔT is proportional to the change of the activation energy during decomposition, and also, the change of
Figure 7. DTG curve of NY-RDF simulated by the Gaussian-fittingbased adjusting model.
κlinear fit =
absolute errors κlinear fit (wt %/min) κGaussain fit (wt %/min)
− γj (8) 1217
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Table 5. Impact of the Interaction on Pyrolysis of Individual Key Components samples
components
ΔT (°C)
Δσ (°C)
E (kJ/mol)
E′ (kJ/mol)
ΔE (kJ/mol)
τ (%)
NY-RDF
PE PS PVC (200−380 °C) PVC (380−600 °C) first cellulose hemicellulose PP PVC (200−380 °C) PVC (380−600 °C) second cellulose PE PVC (200−380 °C) PVC (380−600 °C) first cellulose
−5.0 −7.0 4.0 0.0 5.0 0.0 −6.0 11.0 0.0 4.0 −6.5 8.0 0.0 3.5
−7 0 −3 −15 −2 15 3 4 −15 0 −6 0 −16 0
327.5 271.4 152.1 50.7 193.0 78.1 127.2 152.1 50.7 238.4 327.5 152.1 50.7 193.0
322.9 265.8 155.0 50.7 196.4 78.1 125.3 158.4 50.7 241.5 321.5 156.7 50.7 195.4
−4.6 −5.6 2.9 0.0 3.4 0.0 −1.9 6.3 0.0 3.1 −6.0 4.6 0.0 2.4
−26 0 −11 −51 −10 72 10 15 −51 0 −22 0 −54 0
ZJ-RDF
SH-RDF
the half-width of the DTG peak Δσ means that the decomposition time has increased or decreased. From the Gaussian fitting results, we can deduce the changes of the activation energy and decomposition time for each key component to evaluate the effect of the interaction between different components when mixed in RDF. Here, two parameters (ΔE and τ) are used to quantitatively characterize these two changes
ΔE = E′ − E τ=
Δσ × 100% σ
energy to decompose but the change of their reaction time is relatively small, unlike hemicellulose, whose reaction time has increased 72%, indicating that the reaction speed has decreased strongly.
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CONCLUSION To determine the mass fractions of the key components contained in RDFs by taking their interaction into consideration, a new Gaussian-fitting-based adjusting model has been proposed on the basis of the linear weighted sum method. The DTG curve for each key component has been described by a Gaussian function, and then, fitted Gaussian functions have been summarized to simulate the DTG curves for RDFs with the weights obtained by a direct linear fitting method. During summation, the peak temperature and half-width have been adjusted to obtain optimal results, and the adjustments have been used to quantitatively describe the effect of the interaction on the decomposition of key components. Three different RDFs have been studied to evaluate the proposed model. Results show that direct linear fitting without consideration for the impact of the interaction between the pyrolysis of key components in RDF is able to predict the degradation of three RDFs as well as their key component fractions. However, the DTG peak temperature shifts of key components can be observed. Accurate degradation behaviors of three RDFs are predicted using the Gaussian-fitting-based adjusting model. After mixing in RDFs, both the decomposition time and activation energy are decreased for PE. PVC degrades at higher temperatures within the temperature range of 200− 380 °C, implying a corresponding increase of its activation energy. In comparison to other key components, the reaction rates of PVC between 380 and 600 °C are enhanced and the reaction times are decreased by 51, 51, and 54% for NY-RDF, ZJ-RDF, and SH-RDF, respectively. The activation energy of cellulose pyrolysis is slightly increased, but the impacts on reaction time need further study. The model proposed in this paper can be used as a promising method to predict the optimal reaction temperature and residual time for the design of the advanced thermal waste treatment system.
(10) (11)
where E and E′ are the activation energy for key components before and after mixing in RDFs and, in this paper, are deduced from the Gaussian functions before and after the adjustment of the peak temperature through the first-order kinetic model.21,25 The changes in the DTG peak temperature and half-width as well as the corresponding variation of the activation energy and reaction time are listed in Table 5. After mixing the key components into RDFs, the activation energy required for chemical decomposition has decreased by 4.6 and 6.0 kJ/mol for PE in NY-RDF and SH-RDF, 5.6 kJ/mol for PS in NY-RDF, and 1.9 kJ/mol for PP in ZJ-RDF. The decrease of the activation energy for polymers may be caused by the existence of PVC in RDF. Miranda et al. have once declared that chlorine species released from PVC dehydrochlorination could promote low-temperature degradation of polyolefins.8 The reaction time for PE decomposition has decreased by 26 and 22% after mixing into NY-RDF and SHRDF. However, for PP, the time required has increased by 10% instead. The mechanism of this phenomenon requires further study and confirmation. For PVC in the first decomposition stage (200−380 °C), the activation energy has increased by 2.9, 6.3, and 4.6 kJ/mol when mixed into NY-RDF, ZJ-RDF, and SH-RDF, respectively. This phenomenon agrees well with the results reported by Mcghee et al.,26 who found that, when mixed with wood pine, PVC would degrade at a higher temperature. A similar phenomenon was also observed by Saeed et al.,27 who studied the decomposition of the PVC−wood mixture in a fluidizedbed reactor. In the second decomposition stage of PVC, the activation energy is not affected but the reaction time has increased by 51, 51, and 54% when mixed into NY-RDF, ZJRDF, and SH-RDF, respectively. Similar to PVC, after mixing in RDFs, the first and second types of cellulose need more
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AUTHOR INFORMATION
Corresponding Author
*Telephone: +86-571-87952834. Fax: +86-571-87952438. Email:
[email protected]. 1218
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Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS Acknowledgment is gratefully extended to the National Basic Research Program of China 973 Program (Grant 2011CB201500), the National Science and Technology Pillar Program (2012BAB09B03), and the Creative Team Project of Solid Waste Treatment of Zhejiang Province (A2009R50049) for their financial support.
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