Article pubs.acs.org/IECR
Modeling Pyrolysis of Charring Materials: Determining Kinetic Properties and Heat of Pyrolysis of Medium Density Fiberboard K. Y. Li,† D. S. W. Pau,‡ Y. N. Hou,† and J. Ji*,† †
State Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230027, China Department of Civil and Natural Resources Engineering, University of Canterbury, Christchurch 8140, New Zealand
‡
ABSTRACT: For modeling the burning behavior of medium density fiberboard (MDF), pyrolysis kinetics was experimentally investigated through simultaneous differential scanning calorimetry and thermogravimetric analysis (SDT) experiments. Three decomposition models with up to four pyrolyzing components (phenol−formaldehyde (PF) resin, hemicellulose, cellulose, and lignin) were used to model the pyrolysis process. Genetic algorithm (GA) was applied to produce the kinetic properties based on the experimental thermogravimetric (TG) curves. On the other hand, the heat of pyrolysis was determined from the differential scanning calorimetry (DSC) measurements. The kinetic properties determined by the genetic algorithm are found to be consistent with those from other sources within the literature. Unlike natural biomass materials with only one endothermic peak, MDF presents two noticeable endothermic peaks from the DSC results. This shows two apparent endothermic regions during the pyrolysis process. A combined analysis of the DSC curves and the decomposition model demonstrates that the first endothermic region is mainly caused by the pyrolysis of PF resin which is also influenced by the exothermic reaction of hemicellulose pyrolysis. As a result, the first endothermic region has a higher heat of pyrolysis, 530 kJ/kg, compared to the second endothermic region, 150 kJ/kg. The second endothermic region is mainly caused by the cellulose pyrolysis.
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during flaming combustion, raw MDF exposed to heat will pyrolyze, giving off volatile gases and leaving behind a char residue. The literature contains much information on the kinetic properties and heat of pyrolysis of natural wood.5,6 However, there is insufficient information regarding engineered wood products, especially MDF. It should be noted that the different wood fibers used in MDF would lead to different decomposition behaviors. Therefore, a single set of kinetic properties is less likely to predict the pyrolysis behaviors for all types of MDF. In this research, the experimental technique known as simultaneous differential scanning calorimetry and thermogravimetric analysis (SDT) is used to investigate the MDF samples. The thermogravimetric (TG) and differential scanning calorimetric (DSC) curves obtained are used to determine the kinetic properties and heat of pyrolysis of MDF. According to the literature, the kinetic properties calculated for the decomposition of wood vary over a range. Hence, genetic algorithm7,8 is used to search for the kinetic properties that produce the best fit TG curves. The objective of this research is to investigate the mechanisms of MDF pyrolysis and obtain a set of kinetic properties and heat of pyrolysis that are applicable for pyrolysis modeling for the particular MDF used.
INTRODUCTION Medium density fiberboard (MDF) is a widely used material in buildings for the construction of cabinetry, etc. Features of MDF make it an excellent product for applications which require consistent performance, stability, and fine finish. The production of MDF has reached 15 million m3 in Europe since 2006,1 and in New Zealand, the total MDF production capacity is nearly 1 million m3/year.2,3 As the biggest commercial MDF supplier in the world, China has established a production of more than 38 million m3 since 2010.4 MDF can be a significant contributor of heat and toxic species during fires. To predict the generation of heat and toxic species during burning, the pyrolysis of MDF should be investigated. Two major mechanisms are involved in the pyrolysis process: the chemical reaction and the physical heat transfer. The heat transfer process determined by the material thermophysical properties have been reported previously.3 On the other hand, the chemical reaction is determined by the kinetic properties governing pyrolysis. Therefore, understanding the kinetic properties with regard to the pyrolyzing characteristics is relevant to the safety management in buildings and the current work is part of a wider scope of research on developing a comprehensive pyrolysis model for medium density fiberboard. The kinetic properties related to pyrolysis include the preexponential factor, activation energy, and kinetic model, which are usually presented by Arrhenius equations. These properties dictate the chemical process during pyrolysis. Besides kinetic properties, there is also the heat of pyrolysis which governs the amount of energy absorbed or released during the decomposition. As a typical wood product, MDF contains a majority of natural wood fibers as well as some additives. Hence, the pyrolysis process of MDF is similar to the natural wood (biomass materials). As a typical charring material, prior to and © 2013 American Chemical Society
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LITERATURE REVIEW Thermogravimetric analysis (TGA) is the most common approach used to investigate the pyrolysis of materials. From numerous studies related to wood and biomass, Di Blasi5 presents the state of the art with regard to the pyrolysis kinetics Received: Revised: Accepted: Published: 141
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studies focused solely on the pyrolysis of PF resin22−25 show that the thermal decomposition of PF resin starts at 160−180 °C and continues to over 1000 °C. The heat of pyrolysis is another important property for materials, and it is defined as the change in the amount of energy per unit mass of reactant converting into gaseous products.26 DSC measures the changes in heat flow, while TGA measures the changes in sample mass during a decomposition process. From the changes in heat flow and sample mass, the heat of pyrolysis is determined. The study of Rath et al.6 involved the decomposition of woods in nitrogen and two wood samples, spruce and beech, were investigated. For spruce, the values without a lid ranged from endothermic 300 to 472 kJ/kg while the values with a lid ranged from endothermic 56 to 209 kJ/kg. For beech, the values without a lid ranged from endothermic 177 to 180 kJ/kg while the values with a lid ranged from exothermic 111 to 207 kJ/kg. Matala et al. in refs 8 and 27 investigated the decomposition in nitrogen of birch, oak, pine, PVC, PMMA, graphite and various wood components such as cellulose, lignin, and xylan. The authors reported that endothermic heat flow was obtained during the decomposition and the heat of pyrolysis for cellulose and birch was consistent, 482 and 230 kJ/kg, respectively. Ladacki et al.28 investigated the decomposition of resin in a nitrogen environment. The heat flow was endothermic and the heat of pyrolysis derived was 1255 kJ/kg (300 kcal/kg). From these studies, it can be concluded that the decomposition of most materials in a nitrogen environment results in an endothermic heat of pyrolysis. Some of the samples were also tested by the respective authors in air whereby the heat flow was exothermic due to the presence of additional oxidative reactions. The heats of reaction in air from the literature were not reported because the focus of this paper is on the decomposition in a nitrogen environment only.
and the combustion modeling of biomass materials. According to ref 5, the decomposition models can be generalized into three categories, which are the one-component mechanism, the multicomponent devolatilization mechanism, and the multicomponent mechanism. Figure 1 presents the basic conversion processes of these mechanisms. As shown in Figure 1, in the one-component
Figure 1. Conversion processes of three wood pyrolysis mechanisms.
mechanism,9,10 pyrolysis is modeled as a conversion process of wood to char, tars, and volatiles. The global activation energies of different biomass materials are found between 56 and 174 kJ/mol. In the multicomponent devolatilization mechanism, the pyrolysis is modeled as a conversion process of wood components (hemicellulose, cellulose, and lignin) to volatiles based on the known mass loss.11,12 Grønli et al.12 used the multicomponent devolatilization mechanism to analyze the pyrolysis of four hardwoods and five softwoods. A first-order kinetic model of which the reaction order equals 1 was used, and a set of kinetic properties for different components were proposed by Grønli et al.12 The devolatilization process is usually the major concern in combustion modeling; therefore, most of the recent works in the literature focus on the modeling of devolatilization.13−15 The multicomponent mechanism8,16,17 can be regarded as a combination of the previous two mechanisms, which models the composite of wood and the conversion of wood to active intermediates and the successive solid and gaseous products. Also, many studies7,8,18 focused on developing decomposition models without including the details of the reaction’s chemistry. Becidan et al.18 studied the pyrolysis characteristics of pine and spruce based MDF and came up with three decomposition models which can reproduce the differential thermogravimetric (DTG) curves. One of the proposed models is based on the first-order kinetic model which consists of seven parallel reactions and each of them is the pyrolysis reaction of a single component. Preliminary calculation in this research has shown that the decomposition models proposed by Becidan et al. are not suitable for the current MDF samples. This is because the wood fibers of MDF in this research originate from radiata pine,3 which is different from the mixture of pine and spruce. Other than wood fibers, urea−formaldehyde (UF) and phenol−formaldehyde (PF) resins have also been added during the manufacturing of MDF to bind the fibers.19 In New Zealand, PF resin is commonly applied by the manufacturers.2,20 Li and Li21 investigated the effect of PF resin on the overall pyrolysis process of wood ceramics MDF using TGA, and it was found that the TG curve of MDF can be reproduced from the combined TG curves of wood fibers and resin. Several
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SIMULTANEOUS DSC−TGA EXPERIMENTS The current study investigated two MDF panels made by a local (New Zealand) manufacturer with thicknesses of 25 and 18 mm, as shown in Figure 2a. As specified by the
Figure 2. Materials and sample.
manufacturer, the MDF panels are made of radiata pine with PF resin and about 0.6% paraffin wax by mass added to improve binding. A simultaneous DSC−TGA Q Series instrument, SDT Q600 thermal analyzer, manufactured by TA Instruments,29 has been employed to carry out the simultaneous TGA and DSC experiments. In each experiment, two alumina (Al2O3) cups without lids were used. The volume capacity of the cups is 90 142
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μL and the diameter is about 5 mm. The sample mass is measured through the current signal required to correct a tautband meter movement caused by the changes in sample mass. The MDF samples were tested following the equipment calibrations.29 To ensure the accuracy of the temperature measurements, the sample measuring 10−15 mg was used in each experiment and the sample was cut to such size which covers the bottom of the cup as shown in the left-hand side of Figure 2b. The sample size used also half filled the alumina cup. The samples tested are obtained from the center of the MDF panel because the specimen is less exposed to the ambient air, humidity, and the effect of natural oxidization. The SDT experiments have been conducted in nitrogen at a heating rate of 5 °C/min. Nitrogen is used as it creates a nonreactive environment where the decomposition occurs under the sole influence of heat. The decomposition under nitrogen environment is theoretically similar to the decomposition of materials during flaming combustion where oxygen is consumed before reaching the pyrolysis front.15 In the experiments, the purge flow rate used is 100 mL/min. To account for the experimental uncertainties, the experiments at the same heating rate were repeated three times. Each MDF sample was subjected to two runs during the experiment. The first run dehydrated the sample where the furnace temperature was increased to 130 °C and then the furnace was left closed until the temperature dropped to ambient. The second run decomposed the sample over the temperature range from 20 to 800 °C. The experimental approach is adopted in this research because the focus is specifically on the decomposition of MDF. The moisture removed which otherwise would be released during MDF decomposition complicates the determination of the governing kinetic properties. SDT Q600 operates based on the heat flux concept29 where the heat flow, dq/dt, is determined from the thermal equivalent of Ohm’s law as seen in eq 1. dq ΔTsam − ref kf ku = dt Rt
done by Lautenberger and Fernandez-Pello,33 where previous works are shown to use similar theories and equations with GA. GA is a search tool based on the theory of evolution which is often coupled with a decomposition model. The decomposition model simulates the experiments, while GA searches for the best fit model inputs which provide the closest comparison between the model outputs and the experimental results. During the evolution process, GA will generate a set of individuals carrying several genes to form the generations. The genes are the trial values of the undetermined parameters, which would evolve along the searching process with the bad genes eliminated from the new generation. In the current study, the parameters to be determined by GA include the kinetic properties, mass fractions, and char yields. Two hundred individuals were used in each generation. The total generation number was preset as 5000; however, as the fitness would converge to steady state fairly quickly, the evolution process was run for only 1000 generations in each case. Detailed mechanisms regarding GA can be found in refs 7 and 34. It is worth noting that other than GA both PEAKFIT23,25 and a least-squares method12,18 have also been used to estimate the kinetic properties. Their methodologies are similar to GA while the kinetic properties are adjusted to produce TG curves to fit the experimental results. Decomposition Model and Parameter Searching Regions. Theoretically, the user can make the searching regions as {−∞, +∞} for the undetermined parameters to get the best fit out of GA automatically. However, this is not the case in practice owing to two reasons: it is too time-consuming and it might struggle to converge. Figure 3 presents a GA result
(1)
ΔTsam−ref is the difference between the sample and reference temperatures measured by the individual thermocouple located underneath the platinum lined platform. Rt, kf, and ku are respectively the thermal resistance, the factory set calibration value, and the user set calibration value which are obtained through a series of calibrations.
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PYROLSIS KINETICS MODELING Genetic Algorithm. The complexity of materials pyrolysis complicates the determination of kinetic properties. Often, a single first-order Arrhenius equation cannot reasonably reproduce a material’s TG curves. Decomposition models with different mechanisms have been proposed to address the essences of TG curves. However, when multiple reactions are used as the representation, the large number of kinetic properties that the user needs to adjust makes the modeling process impractical. Determination of kinetic properties by graphical techniques can be applied, but these approaches are only suitable for reactions that are well separated. Since the late 1990s, genetic algorithms have been used for estimations of kinetic properties7,8,30−32 as decomposition often exhibits nonlinearity with high dimensionality as the process is governed by several kinetic properties. A detailed review regarding the application of GA in pyrolysis modeling has been
Figure 3. GA results using broad searching regions.
with relatively broad searching regions for undetermined parameters. Five thousand generations have been used in such a case; however, it can be seen that the predicted DTG curve has not quite matched the experimental result. In fact, to achieve reasonable results, the user has to direct GA to the expected outcomes by setting reasonable decomposition models and searching regions. The search process can be refined appropriately if the user has prior knowledge of the potential region rather than adopting an inclusive region. In the current study, three different decomposition models are used which result in different kinetic properties and also different search regions of GA. The models are based on the multicomponent devolatilization mechanism.12,25 The volatile 143
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Table 1. Searching Regions and Final Searching Results of Undetermined Parameters in GA model I component
param
hemicellulose
cellulose
lignin
PF resin
−1
log Ah (s ) Eh (kJ/mol) f h (%) mh(char)/mh(0) (%) log Ac (s−1) Ec (kJ/mol) fc (%) mc(char)/mc(0) (%) log Al (s−1) El (kJ/mol) f l (%) ml(char)/ml(0) (%) log Ar (s−1) Er (kJ/mol) f r (%) mr(char)/mr(0) (%)
model II GA results
lit. values for comparison
searching regions
GA results
searching regions
GA results
5−8 80−110 22−33 10−30
5.51 87.5 26 13.6
6.51 [12] 100 [12] 31 [36] 20 [42]
5−8 80−110 25−35 10−30
6.33 95.9 30 17.5
5−8 80−110 22−33 10−30
6.03 93.2 28 12.5
12, 12, 35, 41,
9.5−18 150−250 31−43 3−10
12.3 173.5 39.5 5.8
13.23 [18] 180 [18] 40 [36] 6.5 [42]
9.5−18 150−250 35−45 3−10
14.1 195.0 38.5 8.8
9.5−18 150−250 31−43 3−10
15.2 207.9 35.5 4.6
12, 18, 37−39 12, 18, 37−39 35, 36 37, 39, 42
0.05−5 20−60 22−30 25−50 0.7−1.2 30−60 5−10 55−70
0.061 39.0 25 28.6 0.9 35.4 9.5 63.4
0.59 [12] 46 [12] 27 [36] 33 [40, 43] N/A N/A 10 [2] 68 [24]
0.05−5 20−60 25−35 25−50 N/A N/A N/A N/A
0.19 41.7 31.5 44.7 N/A N/A N/A N/A
N/A N/A N/A N/A 0.01−5 20−60 27−40 25−70
N/A N/A N/A N/A 0.01 38.1 36.5 49
12, 40 12, 40 35, 36 40, 42, 43 23−25 23−25 2 21, 24, 25
conversion rate of each component is expressed by the firstorder Arrhenius equation: ⎛ Ecp ⎞ = Acp exp⎜ − ⎟(1 − αcp) dt ⎝ RT ⎠
where αcp is the volatile conversion expressed as mcp(0) − mcp(t ) αcp = mcp(0) − mcp(char)
(2)
(3)
In eq 2 Acp is the pre-exponential factor and Ecp is the activation energy. The subscript “cp” denotes the component. The total conversion rate at any instant t can be determined by lumping the conversion rates of all the components. Providing there are Z components, the total conversion rate is determined by Z
∑ fcp cp = 1
dαcp dt
refs 18 18 36 42
region. Bradbury et al.37 proposed that the activation energy and pre-exponential factor of cellulose are 198 kJ/mol and 16.28 (log A) s−1, while Antal et al.38 reported 153 kJ/mol and 9.78 (log A) s−1, respectively. Lin et al.39 conducted TG experiments and came up with an activation energy of 198−199 kJ/mol and a pre-exponential factor of 14.74−14.81 (log A) s−1, while Grønli et al.12 proposed higher values of 236 kJ/mol and 17.36−17.97 (log A) s−1 for the two properties. Moreover, although Becidan’s work18 has not specifically pinpointed cellulose, it can be seen that the reactant with the highest mass fraction has an activation energy of 183 kJ/mol and a preexponential factor of 13.23 (log A) s−1. Consequently, a search region for cellulose can be derived from these literature data, as shown in Table 1. Similarly to cellulose, the search regions for hemicellulose and lignin are also generalized from the literature and shown in Table 1. Parameter Searching Regions of PF Resin. The mass fraction of PF resin added to the wood fibers is around 8−10% as specified by the manufacturer. In order to reduce the formaldehyde emission, it is common to use less than 10% of resin during the manufacturing process.2,20 Resin curing during the hot-pressing process further reduces the resin content. Hence, the mass fraction of the eventual resin can be less than 10%. Studies on PF resin have revealed that the pyrolysis of PF resin consists of either three24,25 or four22,23 parallel reactions. The three parallel reactions theory has been widely accepted, and these are the formation of additional cross-links, the breaking of cross-links, and the stripping of aromatic rings. According to the literature, the pyrolysis of PF resin shows the following characteristics which include low initial temperature, slow decomposition rate, and high char yield. A few decomposition models within the literature have simulated the pyrolysis of PF resin. Ma et al.24 developed an N-order model whose expression is similar to the first-order model as
dαcp
dα = dt
model III
searching regions
(4)
In eq 4, fcp denotes the mass fraction of the component. In terms of eqs 2−4, for each component, Acp, Ecp, fcp, and mcp(char) need to be determined. As the current MDF mainly consists of radiata pine and PF resin, these two materials are considered as primary reactants during pyrolysis. Parameter Searching Regions of Wood Fibers. Hemicellulose, cellulose, and lignin are the three main components of wood while radiata pine is considered to be a type of softwood. McKendry35 reported that the mass fractions of hemicellulose, cellulose, and lignin are 25−30, 35−40, and 27− 30%. Dubey36 concluded that the mass fractions of hemicellulose, cellulose, and lignin are 31, 40, and 27% for radiata pine while these are 25−29, 40−44, and 25−31% respectively for typical softwoods. Apart from the three main components, there are also extractives. Grønli et al.9 mentioned the extractive in pine is 5−7%; however, Dubey36 reported there is only 2% of extractive in radiata pine. Due to the low quantity, these extractives are ignored in modeling. Cellulose pyrolysis is the primary concern as it has the highest mass fraction in biomass materials. Thus it is used as an example to demonstrate the determination of the searching
⎛ Ecp ⎞ N = Acp exp⎜ − ⎟(1 − αcp) dt ⎝ RT ⎠
dαcp
(5)
Three reactions were simulated, and their kinetic properties were calculated as 64.66, 148.24, and 139.15 kJ/mol for E, 2.11, 144
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7.02, and 5.27 s−1 for log A, and 1.37, 1.35, and 1.37 for N. The total conversion rate is represented by eq 6: dα dα dα dα = 0.46 1 + 0.33 2 + 0.21 3 dt dt dt dt
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RESULTS AND DISCUSSION Experimental DTG and DSC Curves. In the experiments, three sample masses were used, which are 10, 13, and 15 mg. The samples were tested at the heating rate of 5 °C/min from 20 to 800 °C. At the end of the experiments, around 20% of initial mass was left in the form of char residue as shown in Figure 2b. Figure 5 presents the experimental DTG and DSC
(6)
Since the mass fraction of resin in MDF is often less than 10%, this study has tried to simplify the overall decomposition model by representing the resin pyrolysis as a first-order Arrhenius equation depicted in eq 2. As shown in Figure 4, by
Figure 4. Modeling PF resin pyrolysis using a first-order Arrhenius equation.
Figure 5. Experimental DTG and DSC curves.
using GA the first-order Arrhenius equation with an activation energy of 55 kJ/mol and a pre-exponential factor of 0.95 (log A) s−1 can reproduce Ma’s model well with a correlation coefficient of 0.988. Similarly, the proposed models in refs 23 and 25 are also modeled by first-order Arrhenius equations and the kinetic properties are estimated by GA. Eventually, the search region is set respectively as 30−60 kJ/mol and 0.7−1.2 (log A) s−1 for E and A to include the range for PF resins reported in the literature. Searching Regions of Char Yield. Previous studies have shown that the components in MDF have led to different char yields during pyrolysis.21 The presence of char has an impact on the pyrolysis kinetics; however, it was not sufficiently considered in previous work.5 The char yield of cellulose is as low as around 5% whereas the PF resin generates more than 60% char residue during pyrolysis, while the overall char yield for typical wood products including MDF is around 20%. The search regions regarding char yields are listed in Table 1 with all the relevant references presented. The three decomposition models investigated are denoted as models I, II, and III as shown in Table 1. In model I, all four components of MDFcellulose, hemicellulose, lignin, and PF resinare modeled, while model II uses the conventional wood model with three components which include cellulose, hemicellulose, and lignin. It is worth noting that the searching regions in model II are directly from refs 35 and 36. As can be seen in Table 1, lignin and PF resin have similar search regions of kinetic properties as they both have relatively low activation energies, low mass fractions, and high char yields. In model III, lignin and PF resin are lumped as a single component denoted as “PF resin” and modeled with the same kinetic properties. Subsequently, the search regions of model III are set by merging the characteristics of the lignin and PF resin.
curves. In Figure 5, the experimental DSC curves presented were corrected by baseline subtraction. In the experiments, the baseline offset is caused by the heat capacity of the sample while the curvature is caused by the changes in sample emissivity.6 Both the DTG and DSC results show the SDT experiments have good repeatability. From the DTG curves, the sample starts decomposing at 145 °C (418 K) with a noticeable increase in mass loss rate. Unlike materials with obvious DTG peaks and shoulders such as foam15 and hardwoods,12 the inflection points in the DTG curves of MDF are less identifiable, similar to the typical softwoods.12 In this study several concealed inflection points denoted as “shoulders” are identified by comparing the DTG and DSC curves. As noted in Figure 5, the first shoulder corresponds approximately to the first peak of the DSC curves at 217 °C (490 K) while the second shoulder is identified at 287 °C (560 K), which coincides with a minimum of the DSC curve. Lastly, the peak of DTG curves and the second peak of DSC curves are noted to coincide at a similar temperature of 347 °C (620 K). Parameter Determination Using GA. The DTG curves of the three experiments were individually used in GA. The best fit solution listed in Table 1 is the average best fit solution for all GA runs. Due to the high repeatability of the DTG curves as shown in Figure 5, the individual best fit solutions are similar with differences less than 2%. As the decomposition model determines the reaction mechanism of pyrolysis, the best fit solutions obtained are different for models I, II, and III, as shown in Table 1. To validate the decomposition models, the DTG curves are reproduced using spreadsheets with the respective best fit solutions in Table 1 as inputs and compared against the experimental data. For demonstration, experiment 3 with a sample mass of 15 mg was used in the current paper. 145
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Figure 6 compares the experimental DTG curve with the predicted DTG curves using different decomposition models. Figure 7 shows the fitness of the best fit solution throughout the generations over the GA simulation.
Figure 7. Fitness of different models (experiment 3).
From Figures 6 and 7, model I is much better at predicting the pyrolysis of MDF than models II and III. From the DTG curves, model I produces closer comparison with the experiment at the start of pyrolysis. From the fitness of the best fit solution in Figure 7, the fitness reaches steady state (convergence) rapidly, after approximately 200 generations. Model I has the highest fitness of approximately 800, while models II and III are lower at approximately 650. Table 1 presents a comparison between the searching results of model I and the literature values. As shown in Table 1, the kinetic properties of hemicellulose and lignin determined by GA for model I are similar to those proposed by Grønli et al.12 For cellulose, the kinetic properties of model I are consistent with values proposed by Becidan et al.18 but lower than those proposed by Grønli et al.; this is partly due to the fact that Grønli et al.’s model has a higher mass fraction of hemicellulose than cellulose. The kinetic properties determination for PF resin is based on the first-order kinetic model; hence there are no data in the literature for comparison. From this study, the involvement of PF resin in the decomposition modeling has significantly improved the model prediction. To investigate the reaction mechanism in-depth, the sub-DTG curves of all components are plotted in Figure 8a using model I with the overall DTG and the DSC curves. Figure 8a shows the subDTG curves of all components of model I with the overall DTG and the DSC curves. The kinetic properties of PF resin initiate the reaction at lower temperature, hence resulting in a better prediction for model I. The mass fractions of hemicellulose, cellulose, and lignin in radiata pine (90.5% of the total mass of MDF) can be recalculated as 28.7, 43.6, and 27.6% using the GA determined data. These are compatible with the values proposed by Dubey et al.36 From Table 1, the mass fraction of PF resin determined by GA, 9.5%, is close to the upper limit of the search region and it is comparable to the range proposed by the manufacturer. The char yields in Table 1 are similar to those in the literature, except for hemicellulose, which is found to be lower than the literature value.42 Heat of Pyrolysis. A number of studies have used DSC to determine the heat of pyrolysis of materials. Some of the heats of pyrolysis reported in the literature have been calculated based on the original sample mass rather than the amount of reactant converted into gaseous products, while similar terminologies such as heat of vaporization and heat of reaction
Figure 6. Comparison of measured and predicted DTG curves using different kinetic models (experiment 3). 146
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curve. However, it was shown by Yang et al.42 that the heat of pyrolysis of hemicellulose is relatively low, therefore it is believed that the pyrolysis of PF resin predominates in the first endothermic region. A calculation using the DTG data primarily before hemicellulose pyrolysis at temperatures lower than 180 °C gives a value of 1108 kJ/kg for PF resin pyrolysis which is consistent with the literature value of 1255 kJ/kg.28 From Figure 8a, the second region overlaps the pyrolysis region of cellulose in which about half of hemicellulose and lignin decompose simultaneously. As the endothermicity of cellulose has been shown to be approximately 5 times the exothermicity of hemicellulose and lignin in magnitude,42 the endothermic region is believed to be predominantly a result of the cellulose pyrolysis. As discussed above, the first endothermic region is mainly due to pyrolysis of PF resin while the second region is believed to be caused by cellulose pyrolysis. The heat of pyrolysis for these two endothermic regions is calculated by integrating the heat flow along each endothermic region and dividing it by the volatile mass. From Figure 8b, using experiment 3 as an example, the volatile mass fractions at the end of the two regions are found to be 20 and 52%, respectively, which produce the heats of pyrolysis of 527 and 143 kJ/kg. The average values of the three experiments are calculated as 530 and 150 kJ/kg, and eventually the weight averaging heat of pyrolysis is determined as 256 kJ/kg for MDF. In fact, it is found that the lignin pyrolysis would lead to an exothermic process with relatively low heat of pyrolysis at the end of pyrolysis (above 650 K), which is consistent with Rath’s6 and Yang’s42 works; however, it has been ignored in the current study for simplification.
Figure 8. Effects of detailed pyrolysis kinetics on heat flow (experiment 3).
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CONCLUSIONS In the current study, a set of simultaneous DSC−TGA experiments were conducted to investigate the pyrolysis kinetics of MDF. On the basis of the decomposition of MDF, the related kinetic properties and the heat of pyrolysis are determined. These are useful inputs for modeling the burning behavior of MDF. The four components of MDF hemicellulose, cellulose, lignin, and resinwere considered during the modeling of MDF decomposition. Three decomposition models were investigated, and genetic algorithm was used to optimize the kinetic properties. A comparison of the different decomposition models shows that model I where all four components are modeled produces better predictions than the other two models. The kinetic properties determined by model I are also comparable with the literature values. The mass fractions and char yields of different components determined by GA are also consistent with the literature, except for the char yield of hemicellulose which is slightly lower than the literature value. Unlike nature biomass materials which have only one endothermic peak, the DSC result of MDF has two noticeable endothermic peaks. The analysis of the DSC and the simulated DTG curves indicates that the first endothermic region is believed to be caused by the resin pyrolysis. As the heat of pyrolysis of resin is much higher than the one of natural wood fibers, the effect of exothermic hemicellulose is insignificant for the first endothermic region. The second endothermic region is due to the pyrolysis of cellulose, and it has a heat of pyrolysis relatively lower than the first region. The heat of pyrolysis is calculated as 530 and 150 kJ/kg, respectively, for the first and
have also been used in the literature. Therefore, in accordance with the definition adopted in this paper, which is the energy change per unit mass of gaseous products generated, the heat of pyrolysis is expressed as hcp =
dq dq = Z dm d(∑cp = 1 fcp mcp)
(7)
where the energy change, dq, is determined by integrating the curvatures at the heat flow curves with temperatures as shown in Figure 5 while the mass losses (or masses of gaseous products) are determined from the TG curves. From Figure 5, unlike the nature biomass materials which have one endothermic peak,6 the experimental DSC curves of MDF have two major endothermic peaks. This indicates that there are two apparent endothermic regions during pyrolysis. The first endothermic peak occurs from 145 to 287 °C, while the second endothermic region takes place between 287 and 375 °C. From Figure 8a, the onset of MDF pyrolysis is caused by the decomposition of PF resin, which has the lowest decomposition temperature when compared with the other components modeled. The pyrolysis of PF resin overlaps the first endothermic region of the DSC curve where the peak of the PF resin DTG curve coincides with the first endothermic peak. From Figure 8a, the endothermic region also includes part of the hemicellulose pyrolysis. According to Yang et al.,42 the pyrolysis of hemicellulose is exothermic, thus the energy generated from hemicellulose pyrolysis acts to reduce the endothermicity of PF resin pyrolysis. This produces the minimum between the two endothermic peaks on the DSC 147
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second endothermic regions and the weight averaging heat of pyrolysis is 256 kJ/kg.
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AUTHOR INFORMATION
Corresponding Author
*E-mail:
[email protected]. Tel.: (86) 551-63600572. Fax (86) 551-63600327. Notes
The authors declare no competing financial interest.
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ACKNOWLEDGMENTS This work was supported by National Natural Science Foundation of China (NSFC) under Grant 51376173 and the Fundamental Research Funds for the Central Universities No. WK2320000017.
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NOMENCLATURE A = pre-exponential factor (s−1) cp = component E = activation energy (kJ/mol) f = mass fraction of component kf = factory set calibration value ku = user set calibration value m = mass of sample or char residue (mg) N = reaction order q = heat generated or consumed during pyrolysis (J) R = ideal gas constant (J/mol/K) Rt = thermal resistance relevant to thermal analyzer calibration ΔTsam−ref = difference between sample and reference temperatures (K) T = sample temperature (K) t = time (s) Z = number of components
Greek Symbol
α = volatile conversion
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