Study of kinetics of co-pyrolysis of coal and waste LDPE blends under argon atmosphere

Fuel 89 (2010) 3943–3951

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Study of kinetics of co-pyrolysis of coal and waste LDPE blends under argon atmosphere Sumedha Sharma, Aloke K. Ghoshal * Department of Chemical Engineering, Indian Institute of Technology Guwahati, Guwahati 781039, Assam, India

a r t i c l e

i n f o

Article history: Received 9 July 2008 Received in revised form 21 June 2010 Accepted 22 June 2010 Available online 4 July 2010 Keywords: Co-pyrolysis Energy from waste Synergistic effect

a b s t r a c t Co-pyrolysis of coal with waste plastic is increasingly being looked upon as a potential technique to counter the present day challenges in energy and waste management by harnessing the multiple benefits associated with the same. In the present work, the kinetics of co-pyrolysis of waste LDPE carried out with coal of Ledo origin from the coalfields of Assam (India) has been studied. A thermo-gravimetric (TG) study of the co-pyrolysis has been carried out taking waste LDPE-coal mixtures in ratios of 3:1, 1:1, and 1:3 by weight and at five varying heating rates from 5 to 25 K min1 with a 5 K increment. Experiments were also carried out for the individual components at all the five heating rates. This TG analysis data was used to evaluate the kinetics parameters such as the activation energy, pre-exponential factors, and the reaction orders, applying a model fitting approach. The results of the kinetics analysis indicate higher activation energy for the mixtures. With increase in the coal percentage, the reaction order is found to increase. Also the synergistic effect between the two sample species in the mixtures has been studied and found to indicate presence of interaction between coal and LDPE. The effect of sample composition on the products of co-pyrolysis has also been compared. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction Energy and waste management are two key issues of concern assuming increased importance with the escalating industrial and economic growth. This also encompasses the effective utilization of the fossil fuel reserves such as coal. Life cycle assessment (LCA) used to compare different alternative waste treatment strategies suggest the environmental preference of methods like recycling, monomer recovery and development of value added products from plastic, over incineration over land filling [1,2]. Cost of segregation, questionable quality of recycled product and unsuitability of some polymers like polyethylene (PE) and polypropylene (PP) for monomer recovery [3] have led to emphasis on alternate techniques such as feedstock recycling and incorporation of waste into existing industrial processes as raw materials or partial blends with other materials such as coal, as energy sources through recovery of high calorific value gases from pyrolysis or conversion to liquid fuel [4–6]. The objective of co-pyrolysis of coal and plastic is to potentially harness the multiple benefits of production of useful liquid product and valuable carbon material from coal and plastic, decreased en-

* Corresponding author. Tel.: +91 361 2582252 (O)/2584252 (R); fax: +91 361 2582291/2690762. E-mail addresses: [email protected] (S. Sharma), [email protected] (A.K. Ghoshal). 0016-2361/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.fuel.2010.06.033

ergy load on coal resulting in efficient ‘‘energy management”, utilization of waste plastic thus resulting in effective ‘‘waste management” and the application of waste plastic in various existing coal conversion processes. Plastics can be fired in blast furnaces as energy replacements for coke, coal, gas or oil with a recycling efficiency of nearly 76% [7]. Current research focuses on optimizing operating variables to enhance the usability of plastic waste therein [5]. Integrated steel plants are using plastic waste in carbonization processes and as blends with coal for production of metallurgical coke. Industrial utilization of plastic waste in coke ovens has been started at the Nagoya and Kimutsu coke ovens (capacity 80,000 t/y) by Nippon Steel Corporation (in 2000) and is functioning satisfactorily [8]. The results of using waste plastic with coal in different coke ovens indicate that approximately up to 1–3% PE waste can be blended with coal without deteriorating the quality of coke produced while the same may be enhanced by optimizing charging method and device etc. [8–10]. Several researchers have studied the interactions between coal and plastic waste during co-processing [11–14]. Dominguez et al. [12] have observed synergistic effects in inert atmosphere with effective H2 and radical transfer from polymers to coal. This confirms the H2 donating capacity of polymers. Thus the mutual benefit associated with the technique is that while coal promotes radical formation, the polymer plays the role of H2 donor and enhances coal conversion [13]. Díez et al. report that low density polyethylene (LDPE) has both H2 donating and accepting abilities

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S. Sharma, A.K. Ghoshal / Fuel 89 (2010) 3943–3951

but is a stronger H2 acceptor than donor during co-pyrolysis [11]. Dominguez et al. [12] have reported that blending of PE waste to coal produces a significant reduction in Gieseler fluidity since the degradation products from PE act as H2 acceptors. Polyolefinic plastic wastes form reactive intermediates during pyrolysis which may interact with coal to produce significant effect on product quality [14]. Although coal degradation occurs over a wider range of temperature compared to polymer degradation, the closely overlapping ranges of temperatures are considered conducive for interaction between the products of thermal degradation of the two through radical transformation, H2 transfer and stabilization of reactive moieties or radicals produced by the degradation of either species [11,12,15]. Formation of such hydrocarbons and reactive radicals can affect the thermal behavior of coal, particularly in the plastic stage, and the consequent product formation [12]. Some studies indicate that yield of gas/tar from co-pyrolysis is higher than from pyrolysis of coal alone [16] and co-pyrolysis enhances conversion over that of individual components [17–19] while some others identify some products of co-pyrolysis which are not found during pyrolysis of either component thereby indicating strong interactions [13]. Moreover, the formation of higher alkanes is found to be augmented when coal is blended with plastics [12]. The blending of plastic waste or other materials with coal produces a decrease in coal fluidity due to physical and chemical interactions [11,20], which affects the processing and quality during manufacture of metallurgical coke. Polyolefins like LDPE produce less reduction in coal fluidity compared to other polymers [11] and therefore blending up to 5% of PE by mass effects the maximum fluidity insignificantly and even increases the strength of coke produced [21]. This makes the study of co-pyrolysis kinetics of coal and LDPE extremely useful. Several researchers have studied the products of pyrolysis in order to predict their influence on coal thermoplastic and coking properties [11–12,19–23]. However, few works report the kinetics of co-pyrolysis of coal with plastic [15,24–25]. The novelty of the present work is that it focuses on investigation of the kinetics of the co-pyrolysis reaction of coal and waste LDPE and study of the interaction between the two components by comparing the cumulative conversions and product distribution (using capillary gas chromatography technique) of the individual components and a 1:1 mixture of the two. Kinetic study of co-pyrolysis is useful in order to understand the degradation mechanism, to know the rate of reaction and reaction parameters. Kinetic modeling of co-pyrolysis of coal and plastic waste is extremely important for the selection, design and operation of the reactors for industrial application. Study of the synergistic effect between coal and polymer is important in order to predict the interaction between the two species during co-pyrolysis and the nature of products obtained. The coal used in this study is obtained from the Ledo colliery of Makum coalfields in Assam, India. Ledo coal has been classified as the sub-bituminous type high sulphur coal [26]. Due to the high sulphur content, it needs to be blended with other auxiliary fuels, such as natural gas or imported coals to satisfy the coal quality requirement for thermal power generation, particularly from the

emission point of view [26]. High ash and sulphur content makes coal inapplicable for combustion, gasification, liquefaction, carbonisation and metallurgical purposes [27–28]. Ledo coal is reported to have a low softening temperature and a high swelling index, volatile matter and H2 content though having the highest tar yield amongst the Indian coals [29]. The present work concentrates on studying the co-pyrolysis behavior of various mixtures of Ledo coal from Assam and waste LDPE at different heating rates using dynamic Thermogravimetric Analysis (TGA) under inert atmosphere. The thermal events during pyrolysis are identified and the kinetic parameters are evaluated for the same, employing optimization tool of MATLAB and using multi-heating rate and multi-parameter optimization. A preliminary investigation of the products of pyrolysis has been carried out using capillary gas chromatography technique. The synergistic effect between coal and LDPE during pyrolysis has also been studied. 2. Experimental 2.1. Materials The physical and chemical characteristics of coal used in this study are listed under Table 1 [26,29]. The coal was ground manually in the laboratory. The particle size distribution was estimated using laser particle size analyzer (Make: Malvern, UK; Model: Mastersizer-2000) and the mean diameter was found to be 110 lm. The waste plastic used is LDPE, a common packaging material that constitutes a major fraction of the total household and commercial plastic waste. The waste LDPE (melting point 128 °C, heat of fusion 38.7 Jg1, crystallinity 23.95% [30]) was shredded manually to an average size of 2–3 mm. Five different sample mixtures prepared for this study were I: 100% LDPE, II: 1:3 ratio of coal-LDPE (by weight), III: 1:1 ratio of coal-LDPE (by weight), IV: 3:1 ratio of coal-LDPE (by weight) and V: 100% coal. The ground coal and waste LDPE mixtures were prepared by weighing appropriate ratios of coal and plastic in the TGA MT-5 inbuilt microbalance having sensitivity 1 lg. For weighing, the samples were taken in 900 lL alumina crucibles and mixed thoroughly using a forceps tip to obtain as homogenous mixture as possible. The same crucible was then used to perform the TG study to ensure no loss of material due to transfer. 2.2. Experimental equipment and procedure Experiments were carried out in a TGA instrument of Make: Mettler Toledo and Model No. TGA/SDTA 851e. All experiments were carried out under an argon atmosphere for a range of temperature 303–1173 K taking sample weights below 15 mg. The continuous flow of argon ensures an inert atmosphere on the sample and the slow heating rate ensures that heat transfer limitations can be ignored. Five sets of experiments at heating rates 5, 10 15, 20 and 25 Kmin1 were performed for each of the five samples. The experiments were repeated three times for one of the samples, namely LDPE, to confirm the repeatability of the experiments and establish the authenticity of data generated though this instrument. Table 2 shows the results of the TG experiments.

Table 1 Physico-chemical characteristics of Ledo coal [26,29]. Proximate analysis (wt.%) Moisture Ash Volatile matter Fixed carbon

Forms of sulphur (wt.%) 3.07–4.9 10.35–10.4 41.5–43.38 43.20

Total sulphur Pyritic sulphur Sulphate sulphur Organic sulphur

Ultimate analysis (wt.% dry basis) 3.57–4.52 0.58–0.16 0.43–0.33 2.56–4.03

C H N O

70–72.6 5.2–5.33 0.92–1.4 10.8–19.1

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S. Sharma, A.K. Ghoshal / Fuel 89 (2010) 3943–3951 Table 2 Characteristic temperatures and percentage weight loss for different blends of coal and LDPE determined by thermogravimetric analysis. Sample

b (Kmin1)

Tmax (K)

W0 (mg)

TW0 (K)

W1 (mg)

TW1 (K)

%Weight Loss

I II III IV V

5–25 5–25 5–25 5–25 5–25

732–767 735–767 737–768 731–763 716–742

7.777–11.189 12.121–14.322 10.94–14.336 7.05–8.159 2.8–3.174

534–537 516–522 517–523 508–522 516–523

0.256–0.111 2.404–2.926 5.511–4.212 3.842–4.342 2.263–2.5

802–804 802–805 800–806 802–806 803–806

97.6–98.7 79.61–80.95 62–65.85 45.1–47.06 19.18–21.1

Tmax = maximum degradation temperature. W0 = the initial weight of the sample. TW0 = temperature corresponding to W0. W1 = final weight of the sample. TW1 = temperature corresponding W1. %Weight Loss = cumulative weight loss corresponding to temperature range 500–810 K only.

2.3. Chromatographic analysis

The integral form of Eq. (1) written for non-isothermal condition and a constant heating rate ‘l’ is given as-

Chromatographic analysis was done to study the effect of sample composition on the evolved products of pyrolysis reaction. Samples I, III and V were used in this study. An average of the maximum degradation temperature (Tmax) for all five heating rates was estimated for each of the samples. The samples were subjected to an isothermal degradation at their respective averaged Tmax and the evolved gases were analyzed using a Varian 3800 gas chromatograph. The evolved gases from the TGA outlet were collected in a gas tight syringe and injected manually into a capillary Gas Chromatograph column. The column used for separation was VF200MS from Varian, detector used was FID and the carrier gas used was nitrogen. The column was programmed for dynamic heating to 280 °C at the rate of 6 Kmin1 followed by a hold time of 20 min.

The kinetics study involves measurement of the conversion as a function of temperature in dynamic analysis where the temperature is increased in a programmed fashion. The kinetic analysis attempts to relate the experimentally observed conversion v/s temperature data to that predicted using a model fitting approach. In this approach, the reaction model may take various forms based on nucleation and nucleus growth, phase boundary reaction, diffusion, and chemical reaction [31]. In the present investigation, different chemical reaction models (1st order, 2nd order and nth order) and nucleation and growth model were used to study the kinetics of co-pyrolysis. However, only the nth order chemical reaction model is found to fit the experimental data well and hence has been reported. The kinetic model equation combined with Arrhenius approach of temperature function of reaction rate constant for l-th heating rate is expressed as-

ð1Þ

where, ‘t’ is the time (min), ‘T’ the temperature (K), ‘a’ the conversion of the reaction,

a ¼ ðW 0  WÞ=ðW 0  W 1 Þ

f ðaÞ ¼ ð1  aÞ

al

dal ¼ ðk0 =bl Þ fl ðal Þ

0

Z

Tl

expðE=RT l ÞdT l

0

¼ ðk0 =bl ÞIðE; T l Þ

ð4Þ

where, bl ¼ dT is the heating rate (K min1) and dt

IðE; T l Þ ¼

Z

T i;l

expðE=RT l ÞdT l

ð5Þ

0

The nth order kinetic model equation can be solved by substituting k0 e 0 Þ, where K 0  lnðb Þ ¼ K e 0 and k0 ¼ expðK 0 Þand trans¼ expð K l bl forming Eq. (4) for nth order chemical reaction model as follows for calculation of a,

ð3Þ

ð6Þ ð7Þ

The temperature integral [Eq. (5)] can be evaluated by several popular approximations and direct numerical integration [30,32– 34]. In this study, the technique of direct numerical integration [30,34] has been used, where the temperature integral takes the following form,

  E expðul Þ expðE=RT l ÞdT l ¼  Eiðul Þ R ðul Þ 0 Z 1 E expðul Þ where;ul ¼ andEiðul Þ ¼ dul RT l ul ul

IðE; T l Þ ¼

Z

T i;l

ð8Þ ð9Þ

3.1. Optimization The optimization approach followed here is that of multiparameter optimization. The objective function most frequently used in case of multiple heating rates of TG curves to calculate optimum values of D (E, K0, n) for L heating rates and total J data points by minimization of square of deviation between experimental mass (Wexp(T)) and calculated mass (Wcal(T)) is given by Eq. (10),

ð2Þ

where, ‘W0’ is the initial weight of the sample, ‘W1’ the final weight of the sample and ‘W’ that at any temperature ‘T’, (all weights given in mg), ‘da/dt’ is the rate of the reaction (min1) and ‘f(a)’ the reaction model, ‘k0’ the pre-exponential factor (K1) and ‘E’ the activation energy (kJ/mol) are the Arhennius parameters, ‘R’ is the Universal Gas Constant (kJ/mol-K). For the nth order chemical reaction model, the following expression for reaction model holds, n

Z

1 e 0 ÞÞIðE; T l Þðn þ 1Þ þ 1ðn1Þ For n–1; al ¼ 1  ½ðexpð K e 0 ÞÞIðE; T l Þ For n ¼ 1; al ¼ 1  exp½ðexpð K

3. Kinetic models and methods

da1 ¼ k0 expðE=RT l Þf ða1 Þ dt

g l ðal Þ ¼

DðE; K 0 ; nÞ ¼

" J L X X  l¼1

W Exp;l;j  W Cal;l;j

2

# ð10Þ

j¼1

The values of Wcal (T) calculated for each single value of ‘al,j’, the conversion written for l-th heating rate and j-th data point, are as follows:

W Cal;l;j ¼ W Exp;l;0  al;j ðW Exp;l;0  W Exp;l;/ Þ

ð11Þ

Where W Exp;l;0 , is the initial point and W Exp;l;/ is the final point of degradation data recorded for the l-th heating rate. The optimization of the objective function is carried out by direct search

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methods in MATLAB. The local minimization function (LOA) used here is a multidimensional unconstrained nonlinear minimization, by Nelder-Mead direct search method [35]. In addition to that, the deviation between the calculated and experimental values of (dW/dT)cal and (dW/dT)exp are also minimized using Matlab. Thus the mathematical function for minimization assumes a form given by Eq. (12).

" # J L X X  2 W Exp;l;j  W Cal;l;j l¼1

j¼1

2 !2 3     J L X X dW dW 4 5  þ dT cal;l:j dT exp;l;j j¼1 l¼1

ð12Þ

In order to examine the closeness of fit of the experimental data to the model chosen, the resultant values of kinetic triplet (obtained using the minimization approach followed by the application of minimization technique outlined by Eq. (12)) were used to simulate the non- isothermal thermogravimetric (TG) and derivative thermogravimetric (DTG) data using Eq. (6) and Eq. (7) for the calculation of a. The Standard Deviation between the experimental and simulated result is then calculated using the formula given by Eq. (13) and (14) for the TG and DTG data respectively and is shown in Table 3.

vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi #ffi u " J L uP  2 P t W Exp;l;j  W Cal;l;j l¼1

SD1 ¼

j¼1

ð13Þ

ðJ t  1Þ vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi #ffi u " J  2 L uP dW

dW

P t  dT exp;l;j dT cal;l:j l¼1

a

100 90

j¼1

SD2 ¼

ð14Þ

ðJ t  1Þ

where, Jt is the total number of data points for all heating rates. Table 3 also reports the values of the kinetic triplet for each of the five sample mixtures. The challenge in multi-parameter optimization is the choice of initial guess of the parameters [34,36]. The parameters E, K0 and n are strongly correlated and the solution of this objective function to find the global minimum is very difficult. Therefore we follow a statistical approach of obtaining the local optima for a number of initial estimates and observe the trajectory of the same. The standard deviation for each case is then calculated and the optimum corresponding to the lowest standard deviation obtained is considered as the local optimum globalized over the entire range of values considered. 4. Results and discussion

80 70

Weight %

F min ¼

tion region, indicated by maximum weight loss, is within 500– 810 K. Further study has therefore been concentrated within this temperature range. Table 2 reports the experimental details such as the range of maximum degradation temperature Tmax, at which the maximum release of volatile matter takes place, as determined on the basis of the DTG curve, and the percentage weight loss for each sample for heating rates of 5–25 Kmin1. Table 2 also lists the range of TW0 and TW1, the temperatures at which the initial weight, W0 and final weight, W1 of the sample are considered. W0 is taken at TW0 in order to eliminate the loss accounting for moisture and free volatile content of the solids. The final weight is taken at temperature TW1 up to which a fast degradation region is observed. After this temperature, the sample continues to degrade slowly with a decreasing gradient. The region between these two temperatures is the region in which the maximum conversion takes place. For sample I i.e. pure LDPE, no overlapping is observed between the TG curves at the five heating rates. However, for other sample mixtures and for coal alone the TG curves for 10 and 15 Kmin1 and that at 20 and 25 Kmin1 are found to overlap. A sample plot for sample III at all five heating rates is reported in Fig. 1(a) to indicate the same. Similar overlapping behavior of TG curves for coal has been observed by other researchers [37]. This nature indicates that at low differences in heating rates, the thermal degradation profiles for samples containing coal and its blends with LDPE are close. This may be attributed to the heterogeneous nature of coal and is also suggestive of similar interactions between the

60 50 40

5 Kmin-1

10 Kmin-1

15 Kmin-1

30 20 Kmin-1

20 10 0 500

25 Kmin-1

550

600

650

700

750

800

Temperature (K)

b

4.1. Thermal degradation The dynamic thermogravimetric study for the three LDPE-Coal blends at heating rate 10 Kmin1 revealed that the main degradaTable 3 Values of kinetic parameters and the standard deviation between calculated and experimental data. Sample

E (kJmol1)

ln k0

n

SD1

SD2

I II III IV V

194.9 205.8 206.4 198.14 187.5

30.278 32.136 32.346 31.526 30.876

0.33334 0.59999 0.71429 1.1089 2.7699

0.011787 0.01145 0.013425 0.0080184 0.0030349

0.010574 0.010756 0.0077018 0.003058 0.00054707

SD1 = Standard deviation between experimental and calculated mass (W). SD2 = Standard deviation between experimental and calculated values of dW/dT, where W is the mass and T the temperature.

Fig. 1. Profiles for sample III at all five heating rates indicating overlap at close heating rates, (a) thermograms, (b) variation of conversion with temperature.

S. Sharma, A.K. Ghoshal / Fuel 89 (2010) 3943–3951

sample components at close heating rates. However, for the purpose of this study, close heating rates were chosen as the heat and mass transfer limitations are more pronounced between widely differing heating rates. 4.1.1. Effect of heating rate With increasing heating rate, Tmax, DTG peak and conversion profiles shift to a higher temperature (Fig 1(b)) due to heat transfer and medium diffusion effects but the percentage weight loss varies little and hence cannot be correlated conclusively to the changing heating rate. 4.1.2. Effect of sample composition The thermo-gravimetric (TG) curves for LDPE, coal and their blends at 10 Kmin1 each (Fig. 2(a)) indicate that the cumulative degradation within the studied temperature region (500–810 K) decreases successively as the percentage of coal in the sample increases. The decrease in weight loss may be attributed to re-solidification stage of coal which occurs at the temperature around 773 K [11] resulting in semi-coke formation. A definite trend is observed in the variation of Tmax with changing sample composition. Tmax is found to increase for mixtures of coal and LDPE while it is lower for the individual components (Table 2). It is seen that the maximum degradation temperature overlaps with the temperature range 723–773 K. This temperature range [11] is suggested

3947

to be conducive for hydrogen transfer and overlapping degradation regions are found conducive for interaction between the degradation products of coal and polymer [25]. This may suggest the presence of strong interaction parameters between coal and LDPE and a possible influence on the reaction pattern. Variation of rate of reaction (da/dT) with temperature for all samples at the heating rate 10 Kmin1 is reported through Fig. 2(b). The maximum reaction rate is the highest for 100% LDPE and is found to decrease as the percentage of coal in the sample increases. The slower conversion rate for coal may be a consequence of regressive condensation of coal pyrolysis products which is associated with the formation of semi-cokes and cokes [38]. The temperature range of de-volatilization of organic matter is broader by nearly 44% for pure coal than for LDPE alone (Fig 2(b)). However, for coal-plastic blends, this region is found to be more closely overlapping with plastic than with coal. The chromatograms obtained for each of the three samples are shown in Fig. 3(a–c). A comparison of the same clearly indicates that the products evolved during co-pyrolysis differ significantly from those obtained during degradation of the individual components. This further evidences the presence of interaction between the two sample components. 4.1.3. Study of synergistic effect In order to investigate the synergistic effect between the coal and polyethylene samples, the conversion for the mixture Cmix was calculated assuming the additivity law. Thus Cmix gives the predicted value of conversion for a blend of coal and LDPE at a particular temperature. Fig. 4 gives a plot of experimentally obtained conversions against temperature for samples II, III and IV as compared to those predicted using the additivity law. It can be seen that there is a deviation in the experimental data as compared to the predicted. The actual conversion is lower than predicted at lower temperature while it exceeds the predicted at higher temperatures. This difference can be explained on the basis of the distinctly different degradation behavior of the individual components. The peak melting temperature of LDPE, (651–682 K [39]) is followed by the plastic stage which inhibits the evolution of volatile matter. With further increase of temperature, the polymer degrades rapidly up to about 800 K [25]. On the other hand, the de-volatilization of the studied coal begins earlier than LDPE (Fig. 2(b)) and covers a wider range of temperature. The overlapping degradation temperature region indicated in Fig. 2(b) may be suitable for transfer of free radicals from coal pyrolysis to participate in the LDPE decomposition reactions [25]. Thus the synergistic effect observed here further supports the earlier results suggesting interaction between the sample components in the mixture and hence also constitutes a scope for further investigation. 4.2. Kinetics analysis

Fig. 2. Profiles for LDPE, coal and their blends at 10 Kmin1 (a) thermograms, (b) variation of reaction rate with average temperature.

The experimental data obtained from TG curves were utilized for calculation of the kinetic parameters concentrating on the main degradation region within 500–810 K. Since TG or DTG curves are not sufficient to predict the number of steps in a reaction, for the range of temperature from 500–810 K, considered for kinetic study, we assume a single step reaction for all samples and then proceed to validate the assumption. The nth order chemical reaction model used for kinetic analysis in the present investigation is found to predict the degradation behavior of LDPE, coal and their blends very well. This is evident from the standard deviation data for the experimental and calculated results (Table 3). The value of the kinetic triplet for different samples is also reported under Table 3. An increase in activation energy is observed for the blends as compared to a lower value

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S. Sharma, A.K. Ghoshal / Fuel 89 (2010) 3943–3951

a X: 39.5450 Minutes Y: 0.00463

mVolts

15

10

5

0 -2 10

20

30

40

50

40

50

40

50

Minutes

b

X: 0.0000 Minutes Y: -0.0540 mVolts

mVolts

75

50

25

0 -7 10

20

30

Minutes

c

X: 17.0953 Minutes Y: 0.0000 Minutes

mVolts

7.5

5.0

2.5

0.0 -1.0 10

20

30

Minutes Fig. 3. Comparison of chromatograms for evolved gases from pyrolysis of different samples, (a) 100% coal , (b) 100% LDPE ,(c) 50% mixture of coal and LDPE.

obtained for the individual components. A similar trend is observed for the values of ln k0. The increase in activation energy for the blends may be attributed to the fact that at higher temperature regions, the conversion for the blends is higher compared to that predicted using the additivity law (Fig. 4). The order of reaction is, on the other hand, found to increase with the coal content in sample mixture. This may suggest that some reactions occur

between the degradation products of LDPE and coal as the complex coking reactions occur. A significantly higher reaction order is found for pure coal. This can be attributed to the complex heterogeneous nature of coal and the multiple reactions associated with coal degradation. The variation in the values of the kinetic parameters clearly indicates a variation in the pyrolysis reactivity [25] and may suggest variation in the reaction mechanism involved.

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S. Sharma, A.K. Ghoshal / Fuel 89 (2010) 3943–3951

different models and methods used. The differences in the kinetic data for coal can be attributed to the fact that the degradation characteristics of coal depend strongly on the composition of the coal which in turn differs based on nature and origin of the coal [26]. The values of kinetic parameters found were utilized to predict the TG curves for the five samples at all five heating rates under investigation. Fig. 5(a) gives a sample plot of simulated and experimental TG curves for all samples at 10 Kmin1. A good agreement is found between the experimental and simulated values. Simulated DTG plots were also found to fit the experimental data closely (Fig. 5(b)). This implies that the co-pyrolysis reaction investigated is well predicted by the nth order model used for the kinetic analysis. The agreement between the calculated and experimental data, over the considered range of temperature leads to the inference that the assumption of single step reaction is not an oversimplification.

1 0.9

Conversion (α)

0.8 0.7

II-experimental

0.6

II-Predicted

0.5

III-Experimental III-Predicted

0.4

IV-Experimental

0.3 IV-Predicted

0.2 0.1 0

530

580

630

680

730

780

Temperature (K)

5. Conclusion Fig. 4. Experimental and predicted conversions for samples II, III and IV.

A significant volume of research has been done to study the kinetics of degradation of LDPE. Table 4 shows the kinetic data for LDPE and coal as obtained by other researchers [18,25,37,40– 48] in comparison with that obtained in the present work. The kinetic data obtained for LDPE using the nth order model are found to agree closely with some of the literature data. However, the differences observed in the tabulated data can be attributed to the

The kinetic parameters obtained with a single step reaction assumption within the studied temperature range and using the nth order reaction model vary with changing sample composition in terms of coal percentage. The values of E and ln k0 are higher for mixtures than for individual components. However, quantitatively, they do not vary significantly. The order of the reaction is found to increase with increasing percentage of coal in the sample. Thus, the blending of waste LDPE with coal shifts the reaction to a lower order with a higher degree of conversion as well. The

Table 4 Comparison between kinetic parameters for LDPE-Coal in literature and present work. Sample

Method

E (kJmol1)

ln k0

n

Reference

LDPE

First-order model Random-chain dissociation model First-order model Random-chain dissociation model Three-reaction model (Flexible Simplex Optimization Method)

221 234.5 241 ± 10 244 ± 12 (214.2238.9, 200)a

31.094 30.518 34.6 ± 1.7 32.2 ± 2.1 (24.58, 32.71, 25.476)b

1 1 1 1 –

[40] [40] [41] [41] [42]

Freeman and Carroll’s Method nth-order model Isoconversion method Integral method

272 49.3 150–240(200)c 192–263

– 27.81,334 (26.039)c –

0.14 0.63 – –

[43] [44] [45] [46]

Isoconversional method by Flynn

201.5

28.1257 ±0.0185

0.55

[47]

Nucleation and growth Nth order model

189.058 194.9

29.69583 30.278

2/3 1/3

[48] Present work

Sample

Method

E (kJmol1)

Pre-Exponential factor A

n

Reference

Samla coal

Coats and Redfern 400–500 °C <400 °C >500 °C Three parameter Kinetic model Low temperature stage High temperature stage

21.345 12 24.69

1 2.8–2.9 0.4

169.9 245.1

0.6889 0.6889 0.6889 A(s1) 3.834  106 3.79  107

Integral method 358–491 °C 491–667 °C

128.9 115.4

Nth order model

187.5

Yuanbaoshan coal

Low volatile chinese coal

Ledo Coal(AssamIndia) a b c

[18]

At 10% weight-loss and values of E increase with the extent of degradation. Three step mechanism is assumed. Almost constant for alpha > 0.2(130–200).

– –

[37]

A(min-1) 5.5  108 1.8  106

– –

[25]

ln k0 30.876

2.7699

Present work

3950

a

S. Sharma, A.K. Ghoshal / Fuel 89 (2010) 3943–3951

gaseous products of pyrolysis; recovery of valuable chemicals from the tar obtained during co-pyrolysis; and safe and efficient utilization of this technique considering the emission and pollution concerns arising from its use.

120

100

Weight %

80

References V: Experimental V: Calculated

60

IV: Experimental IV: Calculated III: Experimental

40

III: Calculated II: Experimental II: Calculated

20

I: Experimental I: Calculated

0 500

550

600

650

700

750

800

Temperature (K)

b

Fig. 5. Comparison of experimental and simulated data depicting applicability of the nth order reaction model. (a) Thermograms for all five samples at heating rate of 10 Kmin1. (b) DTG curves for sample II at five heating rates.

predicted data are found to fit the experimental data close enough to conclude applicability of the assumed reaction model and single step reaction regime within the temperature range 500–810 K. Since a variation in the kinetic parameters is indicative of a variation in the mechanism that controls the co-pyrolysis [25], which is further evidenced from the comparative gas chromatographic analysis, this creates scope for further study of the reaction mechanism and the possible configurations of the products of co-pyrolysis. The study of synergistic effect between coal and LDPE in the sample mixtures also indicates the existence of some interaction between the sample components. As a future work, detailed study of product distribution through chromatography and/or other techniques can be done to further validate the synergistic effect and ascertain the effect of co-pyrolysis on the product distribution. Also the study can be extended to investigate copyrolysis kinetics of coal with mixed plastic streams. Co-pyrolysis reaction produces three types of products gaseous, tar or oil and solid carbonaceous residue. The challenges in utilization of co-pyrolysis of coal and plastic waste at the industrial scale are segregation of plastic waste so as to make use of those polymers which give high calorific value gaseous product, high yield of valuable liquid product and satisfactory residual properties and elimination of hazardous plastic waste like PVC which have high organic chlorine content; tapping of the energy from the

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