Kinetic Study of Carbon Dioxide Gasification of Rice Husk Fast

Apr 22, 2015 - The rice husk char gasification kinetics with carbon dioxide has been studied in a thermogravimetric analyzer (TGA) under isothermal an...
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Kinetic Study of Carbon Dioxide Gasification of Rice Husk Fast Pyrolysis Char Jon Alvarez, Gartzen Lopez, Maider Amutio, Javier Bilbao, and Martin Olazar* Department of Chemical Engineering, University of the Basque Country (UPV/EHU), Post Office Box 644, E48080 Bilbao, Spain ABSTRACT: The rice husk char gasification kinetics with carbon dioxide has been studied in a thermogravimetric analyzer (TGA) under isothermal and dynamic regimes. The effect of the carbon dioxide concentration (50, 75, and 100 vol %) and heating rate (5, 10, and 20 °C min−1) was determined in the dynamic runs, and the effect of the temperature (750, 800, and 850 °C) was determined in the isothermal runs. Rice husk char gasification has a complex kinetic behavior, with reactivity being strongly dependent upon the temperature and char conversion. The experimental results have been fitted to four different kinetic models, namely, homogeneous, nth order, random pore model and modified random pore model. The random pore model is the model that provides the best fit to the experimental evolution.

1. INTRODUCTION Rice husk is one of the most abundant agro-residues in developing countries and is commonly used as a low-value energy resource.1 Pyrolysis and gasification are considered the most promising thermochemical processes for the production of renewable energy and fuels from this waste.2 In a previous study, fast pyrolysis of rice husk was carried out in a conical spouted bed reactor, which performed well for the production of high bio-oil yields.3 Moreover, to enhance the overall economy of the process, the valorization of rice husk char was proposed according to two simultaneous routes: the extraction of amorphous silica using Na2CO3 and the production of activated carbon by physical activation of the char remaining after the extraction.4 The quality and porous structure of the activated carbons produced are a consequence of the dwell time, temperature of carbonization, and gasification conditions.5 Hence, the study of char gasification kinetics is of great relevance for the understanding of the activation process as well as the design of the activation reactor. Furthermore, char gasification is the rate-limiting step in the biomass gasification processes; i.e., it is much slower than the other reactions involved in biomass gasification, such as devolatilization and reforming reactions in the gas phase.6 Accordingly, char gasification plays a crucial role in the design of the gasification reactor, in which the kinetics of this step is required. Numerous studies have been conducted on the gasification of biomass chars, and different theoretical and semi-empirical models have been proposed to predict the gasification performance and phenomena inside the gasifiers.7−9 According to Zhang et al.,10 char reactivity and kinetic modeling studies are closely related to the improvement in the gasifier design and process efficiency. Studies have been carried out to ascertain the factors controlling the char gasification kinetics and char reactivity.9,11−14 The results reported in the literature for the kinetic parameters differ depending upon several factors, which are (i) original biomass composition, mainly the ash content of the samples15 [in this regard, Di Blasi8 analyzed the factors affecting the parameters of the global reaction and reported that inorganic matter content is one of the most influential factors; in addition, López-González et al.9 showed that the effect of ashes in the process was more noticeable at © XXXX American Chemical Society

high conversion values (0.7−0.8) because of the increase of catalytically active sites available], (ii) gasification conditions (particle diameter, sample mass, gas concentration, etc.) and the previous pyrolysis conditions (heating rate, final temperature, etc.)16,17 (thus, the presence of CO or H2 in the reactive gas produces an inhibiting effect on the gasification reaction, and therefore, these factors should be included in the model;7,18 however, when CO or H2 concentrations are very small, the inhibition effect is insignificant and may be ignored),8 and (iii) methodology followed for the analysis of the kinetic results (as a consequence of these differences in experimental conditions and data treatment procedure, significant differences are obtained in the activation energies).8 This paper aims to study the rice husk char gasification kinetics using carbon dioxide as a gasifying agent. The experimental runs were performed in a thermogravimetric analyzer (TGA), which allows for reliable kinetic results to be obtained under different conditions and temperature sequences. Four different models have been proposed and compared in this study, namely, homogeneous, nth order, random pore model, and modified random pore model, for the gasification kinetics of the char obtained by rice husk fast pyrolysis. Among these models, the random pore model has often shown satisfactory agreement between experimental and calculated results, given that it considers the effect of pore growth and coalescence throughout gasification. However, this model sometimes fails describing the different reactivity profiles of biomass chars, mainly when reactivity increases as conversion is increased. Accordingly, Zhang et al.10 proposed the modified random pore model, in which two new parameters were introduced in the original random pore model, to account for the evolution of char reactivity.

2. MATERIALS AND METHODS 2.1. Rice Husk Char Production and Characterization. The rice husk char samples have been obtained by flash pyrolysis carried out in a conical spouted bed reactor at 500 °C. The char yield was Received: February 9, 2015 Revised: April 22, 2015

A

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Energy & Fuels 26 wt %, and those for the gas and bio-oil were 6 and 68 wt %, respectively.3 The conical spouted bed reactor is characterized by a high solid circulation rate that enhances heat transfer and, therefore, promotes high heating rates.19 Furthermore, the vigorous solid movement ensures bed isothermicity and a residence time in the reactor as short as hundreds of milliseconds, minimizing secondary reactions in the reactor.20 The excellent performance of the spouted bed reactor has been verified in the pyrolysis and gasification of different wastes, such as biomass21,22 plastics23,24 and tires.25 Furthermore, it has been successfully scaled up to a 25 kg/h unit for the pyrolysis of biomass.26 These pyrolysis conditions lead to low char yields and improve the quality of the char. In fact, high heating rates in the pyrolysis process promote the formation of a well-developed internal porous structure because of the coalescence of smaller pores by the overpressure generated by the sudden release of volatiles.27,28 These pores increase the reactivity of the char as a result of a higher surface area29 and/or a higher concentration of active sites for the gasification of the char particles.28 Furthermore, the conical spouted bed reactor allows for continuous removal of the char particles from the bed throughout the pyrolysis process, which avoids their accumulation as well as the deposition of volatiles that may lead to the blockage of the pores. Table 1 shows the features of the rice husk and rice husk char. The ultimate and proximate analyses of the chars have been carried

on the activation kinetics has been assessed, varying its value between 50 and 100 vol %. The dynamic experiment performed with a heating rate of 20 °C min−1 was repeated with a gas flow rate of 200 mL min−1, to obtain information about the external diffusion limitation. As expected, no detectable differences were observed between the results obtained with different gas flow rates. Therefore, the char gasification process with CO2 is considered to be under kinetic control.8 The mass of demineralized char (without silica) used in each experiment was around 3 mg, with a particle size below 200 μm, to enhance gasification under a kinetic regime.16 To avoid the entrance of oxygen traces that may disturb the results, a slight overpressure was generated inside the reaction chamber by means of a valve located at the outlet of the TGA. The heating sequence, under both an isothermal and a dynamic regime, is made up of two different steps, the carbonization of the sample and the carbon dioxide gasification. Thus, the sample is initially heated under inert conditions (nitrogen) at a heating rate of 20 °C min−1 from room temperature to a final temperature of 800 °C in dynamic experiments or to the gasification temperature in isothermal runs (750, 800, and 850 °C). To ensure full carbonization of the sample, this temperature was maintained for 60 min. This treatment avoids thermal degradation disturbances in the gasification step.11,31 In the dynamic experiments, the sample was first cooled to 600 °C and the reactive mixture (CO2 and N2) was then introduced in the thermobalance. After 5 min, nitrogen was completely purged and the activation step was started. Three different heating rates were used, 5, 10, and 20 °C min−1, to a final temperature of 900 °C. Figure 1 shows the temperature sequence and the thermogravimetry (TG) curve corresponding to a dynamic experiment (Figure 1a) and an isothermal experiment (Figure 1b). The end of the carbonization step and the beginning of the gasification step are indicated by a dash line in the figure. Some of the experiments were repeated 3 times to guarantee the reproducibility of the results. In fact, the deviations observed were below 2% in mass. 2.3. Kinetic Model Description. The carbon dioxide gasification is considered to be a simple heterogeneous reaction fully controlled by the chemical reaction step, and the rate can be described by a general kinetic expression for the quantification of conversion evolution with time as a function of the temperature and CO2 concentration.

Table 1. Characterization of the Original Rice Husk, Pyrolysis Char, and Char Remaining after Silica Removal ultimate analysis (wt %) C H N O proximate analysis (wt %) volatile matter fixed carbon ash BET surface area (m2 g−1) pore volume (cm3 g−1) higher heating value (MJ kg−1)

rice husk

char

demineralized char

42.0 5.4 0.4 39.3

45.2 1.5 0.4 1.7

83.1 2.9 0.7 6.9

70.5 16.6 12.9

12.8 36 51.3 25.6 0.05 14.4

26.7 67 6.3 227 0.17 32.0

16.8

dX α = k(T )PCO f (X ) 2 dt

(1)

The term f(X) in eq 1 describes the structural and chemical changes throughout the gasification process. The conversion degree, X, has been defined as w −w X= 0 w0 − w∞ (2)

out in a LECO CHNS-932 elemental analyzer and in a Q500IR TGA, respectively. The high heating value has been measured in a Parr 1356 isoperibolic bomb calorimeter. Surface area and pore volume and distribution have been determined from nitrogen adsorption− desorption isotherms, in a Micromeritics ASAP 2000. Brunauer− Emmett−Teller (BET) and Barrett−Joyner−Halenda (BJH) methods were used, respectively. The most remarkable characteristic of the rice husk char is its high ash content (52.2 wt %), mainly made up of amorphous silica. This content hinders the application of this material as both fuel and active carbon source.30 To improve the properties of the rice husk char for use as an activated carbon promoter and the economy of the process, the amorphous silica was removed and recovered (with a yield of 88%) using a solution of sodium carbonate as the silica solvent.4 The characterization of the carbon material remaining after silica extraction is also shown in Table 1. The main effect of silica removal is the reduction of the ash content as well as a significant improvement in the surface area from only 15 to 227 m2 g−1. 2.2. Gasification Kinetics. The gasification kinetic study has been conducted in a TGA, TA Instruments SDT 2960, under isothermal and dynamic conditions. Dynamic conditions allow for kinetic results to be obtained in a wide temperature range with a single experiment, whereas isothermal experiments provide useful information concerning the evolution of char reactivity with conversion. The gas flow rate used in all of the experiments was 100 mL min−1, under both inert (nitrogen) or gasifying (carbon dioxide and nitrogen mixture) conditions. The effect of the carbon dioxide concentration

where w0 is the initial mass of the sample (after carbonization), w is the mass of the sample at time t, and w∞ is the final mass of the sample, corresponding to the ashes in the sample. To assess the evolution of the sample reactivity with conversion, four kinetic equations have been tested, corresponding to a homogeneous model (eq 3), a modification of the homogeneous model provided with an adjustable exponent n or nth order model (eq 4), the random pore model (eq 5), and a modified random pore model (eq 6) dX α (1 − X ) = kPCO 2 dt

(3)

dX α (1 − X )n = kPCO 2 dt

(4)

S0 dX α =k PCO (1 − X ) 1 − ψ ln(1 − X ) dt 1 − ε0 α = k′PCO (1 − X ) 1 − ψ ln(1 − X )

(5)

dX α = k′PCO (1 − X ) 1 − ψ ln(1 − X ) (1 + (cX ) p ) dt

(6)

where both c and p are dimensionless parameters. B

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assumes that the reaction surface area decreases linearly with char conversion.36 Furthermore, the random pore model is based on the assumption that new pores are created and, at the same time, overlapping of existing pores takes place throughout the gasification process. The modified random pore model was proposed by Zhang et al.10 to improve the description of biomass char gasification, in which reactivity usually increases until high conversion values. Given the physical meaning of the parameter ψ, it was fixed in the same value obtained in the fit of the random pore model. 2.4. Kinetic Model Fitting. The fitting of the proposed models has been carried out by means of a program written in MATLAB and using the subroutine fminsearch with the Levenberg−Marquardt algorithm to minimize the error objective function defined by eq 11. L

EOF =

∑ j = 1 [(DTG)calculated − (DTG)experimental ]2 L(DTG)2 experimental

(11)

where L is the number of available experimental data and DTGexperimental is the average value of the L experimental points considered for the fitting. Both the dynamic and isothermal experiments have been used for the fitting. In addition, the program includes a subroutine ode45 based on the Runge−Kutta−Fehlberg pair of orders 4 and 5 for solving the differential equations for each model (eqs 3−6).

3. RESULTS AND DISCUSSION 3.1. Effect of Gasification Conditions. The rice husk char samples were subjected to a two-step treatment consisting of a carbonization and a gasification step. During the carbonization step, a variable mass loss was observed depending upon the final carbonization temperature, from 23 wt % at 750 °C to 28 wt % at 900 °C. Figure 2 compares the weight loss rate observed operating at different heating rates. As observed in Figure 2a, sharper and narrower peaks are observed at high heating rates. However, there is hardly any displacement of the curves to higher temperatures when the heating rate is increased (Figure 2b), which allows for the conclusion that heat- and mass-transfer limitations are negligible. Moreover, Figure 2b clearly shows that there is no considerable char gasification at temperatures below 750 °C. Figure 3 shows the effect of the CO2 concentration on the char gasification kinetics. As observed, the increase in the CO2 partial pressure enhances char gasification. Thus, the gasification of the sample using 100% CO2 was completed in 37 min, whereas using 50% CO2 required 44 min (see Figure 3a). In addition, gasification takes place at lower temperatures when the CO2 partial pressure is higher (Figure 3b). Apart from the dynamic experiments, isothermal experiments have been carried out at 750, 800, and 850 °C. Although isothermal kinetic results at 900 °C are interesting for comparison to dynamic runs, in which the maximum weight loss occurs around this temperature (see Figures 2b and 3b), the experimental results obtained were inconsistent because of low reproducibility. It should be noted than an inherent limitation of isothermal runs is the initial transient period.11 The gasifying agent is introduced at t = 0, but the inert gas must be purged from the reaction environment to attain the desired concentration. At low temperatures, the transient period is negligible, but when the gasification kinetics is faster, the uncertainty of the results is greater. Figure 4 shows the evolution of the weight loss rate with time (Figure 4a) and char conversion (Figure 4b) for the different temperatures studied in the isothermal runs. The first conclusion drawn by analyzing Figure 4a is the great effect of the temperature on CO2 gasification kinetics. Thus, an increase

Figure 1. (a) Temperature sequence and TG curve corresponding to a dynamic experiment carried out at 5 °C min−1 and 75 vol % CO2. (b) Temperature sequence and TG curve of an isothermal experiment carried out at 800 °C and 75 vol % CO2. The symbol ψ in eqs 5 and 6 is a structural parameter defined as32 ψ=

4πL0(1 − ε0) S0 2

(7)

where S0, ε0, and L0 correspond to the surface area, total volume, and length of the porous system made up of random overlapping pores, with V0(r) being the initial pore size distribution.

S0 = 2 ε0 = 2 L0 =

∫0 ∫0

1 π

∞

V0(r ) dr r

(8)

V0(r ) dr

(9)

∞

∫0

∞

V0(r ) dr r2

(10)

The random pore model has been applied by determining the parameters k′ and ψ of best fitting from experimental results.9,12,33−35 The homogeneous model suggests that the gasifying agent reacts with the char at all of the active sites, which are uniformly distributed on both the outside and inside char particle. In addition, this model C

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Figure 2. Evolution of the weight loss rate with (a) time and (b) temperature for the different heating rates studied, 5, 10, and 20 °C min−1.

Figure 3. Effect of the CO2 concentration on the evolution of the weight loss rate with (a) time and (b) temperature.

gasification rate. To explain these results, the dependency of char reactivity upon the chemical structure, porosity, and inorganic constituents must be considered.8 The first two characteristics, i.e., chemical structure and porosity, define the initial reactivity of the sample; likewise, the catalytic activity of the ashes has a prevailing role on the specific reaction rate at high conversion values. During the initial period of char conversion, the reaction rate increases as a result of the porous structure development (creation of new pores and growth of existing pores), which gives way to an increase in the surface area and active sites available for gasification.8,14,17,37 In fact, the surface area increased with burnoff to a value of around 70 wt %, attaining a BET surface area higher than 1500 m2 g−1. For higher conversion levels, a progressive decrease in the surface area was obtained.4 Accordingly, the sharp increase in char reactivity observed at high conversion levels is not related to the porous development but to the role played by the ashes. It is commonly accepted that an increase in the ash concentration with char conversion enhances the catalytic activity toward the gasification reaction.8 Recently, Lahijani et al.34 studied the catalytic activity of different alkali metals in the carbon dioxide gasification of biomass chars, and the activity order was determined as follows: Na > Ca > Fe > K > Mg.

of 100 °C causes a reduction of the time required to attain full conversion to a 9th. Moreover, a detailed analysis of the evolution of the reaction rate with conversion shows a different behavior at 750 °C compared to that observed at higher temperatures (Figure 4b). Therefore, the weight loss rate at 750 °C is almost constant for a wide range of char conversion levels. However, at 800 and 850 °C, the weight loss rate increases with char conversion to maxima located at conversion levels of 0.9 and 0.6, respectively. To provide a more appropriate analysis of the effect of char conversion on gasification kinetics, the reactivity of the char has been calculated as dX /dt (12) 1−X The evolution of rice husk char reactivity versus char conversion is shown in Figure 5. Two different regions can be clearly distinguished in the reactivity curves: (i) the initial period up to a conversion value of around 0.8, in which the specific gasification rate increases linearly with char conversion, and (ii) the region between a conversion level of 0.8 and full conversion, characterized by a exponential increase of the reactivity =

D

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Figure 6. Comparison of weight loss rate with (a) time and (b) temperature in the gasification of demineralized char and original rice husk char.

Figure 4. Evolution of the weight loss rate with (a) time and (b) conversion at the different temperatures studied, 750, 800, and 850 °C.

mentioned alkaline metals was determined.3 Moreover, the rice husk char samples used in the present study were previously subjected to a desilication process.4 Accordingly, the relative concentration and catalytic activity of the alkaline species increase, thus causing an increase in the reactivity at high conversion values. To evaluate the applicability of this kinetic study to the original rice husk char (prior to demineralization), a gasification run has been performed using this material. The same procedure described in section 2.2 for the dynamic runs has been applied (carbonization and gasification steps), with the heating rate and CO2 concentration being 10 °C min−1 and 75%, respectively. Figure 6 compares the differential thermogravimetry (DTG) curves obtained in the gasification of rice husk char and the demineralized char. As observed, the reaction rate is significantly lower for the original rice husk char. This fact is related to the higher surface available for the heterogeneous gasification reaction in the deminealized char than in the original rice husk char; i.e., the BET surface areas are 227 and 25.6 m2 g−1, respectively. Furthermore, SiO2 removal increases the concentration and accessibility of active metallic species, such as alkaline metals, thereby promoting the reactivity of this material.

Figure 5. Reactivity profile with char conversion obtained at different temperatures.

In a previous study, rice husk ash was characterized by X-ray fluorescence and a significant content of the previously E

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Figure 7. Comparison of the experimental and calculated DTG results using the homogeneous model with α = 1. Dynamic results with different (a) heating rates and (b) CO2 concentrations and (c) isothermal runs.

Figure 8. Comparison of the experimental and calculated DTG results using the homogeneous model with α = 0.88 for carbon dioxide pressure. Dynamic results with different (a) heating rates and (b) CO2 concentrations and (c) isothermal runs.

3.2. Kinetic Analysis. Four different kinetic models, corresponding to eqs 3−6, have been used for the fitting of the results of isothermal and dynamic experiments.

3.2.1. Homogeneous Model. The fitting of this model to the experimental results has been carried out using two different alternatives, which are as follows: initially, the exponent α of the F

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Table 2. Values of Frequency Factor, Activation Energy, Fitting Error, and Adjusted Parameters for the Four Models Tested model

log k0 (s−1)

E (kJ mol−1)

error

n

homogeneous (α = 1) homogeneous (α ≠ 1) nth order (α ≠ 1) random pore (α ≠ 1) modified random pore (α ≠ 1)

13.59 13.61 11.84 9.36 9.20

354.1 354.8 317.1 266.8 263.1

0.297 0.294 0.227 0.136 0.120

0.8

α

ψ

c

p

0.88 0.81 0.69 0.79

3.54 3.54

0.94

6.36

The use of the nth order model (Figure 9) clearly improves the fitting obtained by the homogeneous model; i.e., a reduction higher than 20% in the error is obtained (Table 2). 3.2.3. Random Pore Model. The random pore model considers modifications in the porous structure of the char throughout the gasification process. Figure 10 shows the fitting of the experimental values corresponding to the DTG curves with those calculated with the random pore model (eq 5), with α = 0.70 and ψ = 3.54. As observed, a significant improvement has been obtained in the prediction of isothermal runs. The deviation between calculated and experimental results has been reduced to less than a half of that corresponding to the homogeneous model and by 40% of that corresponding to the nth order model. Other authors have also observed a better description of char gasification kinetics with the random pore model than using homogeneous and shrinking core models.9,17,34,40−42 The activation energy obtained with the random pore model (267 kJ mol−1) is lower than those obtained with the homogeneous model (354 kJ mol−1) and nth order model (317 kJ mol−1). Bhat et al.1 studied the rice husk char gasification with carbon dioxide in a TGA using a homogeneous model, and the activation energy reported was of around 200 kJ mol−1. In the literature, a wide range of activation energies is reported for the carbon dioxide gasification of biomass chars. Di Blassi8 carried out an extensive review and concluded that a large fraction of the values varies from 200 to 250 kJ mol−1. The low value of the parameter ψ (3.54) is consistent with the properties of demineralized rice husk char, given that this material is a very porous char that undergoes a limited pore development in the subsequent gasification process. However, ψ takes high values for materials with low initial porosity, in which the pore surface and reactivity increase throughout the gasification process.32 The values reported for this parameter, obtained by either fitting or determined directly from the initial char properties, vary in a wide range, which is due to its strong dependency upon the original biomass characteristics and the pyrolysis conditions.31 3.2.4. Modified Random Pore Model. The random pore model is unable to predict reactivity profiles for conversion values greater than 0.393, and the modified random pore model has been developed to overcome this limitation.10 Figure 5 shows that the demineralized rice husk char exhibits the maximum reactivity at high values of conversion, and therefore, the carbon dioxide gasification results have been fitted to the modified random pore model. As observed in Figure 11, the fitting of the experimental data to the modified random pore model is significantly better than that to the random pore model; thus, i.e., the error is lowered from 0.136 to 0.120. Similar improvements have also been reported by other authors who explained that the experimental data for biomass chars were better described with the modified model over the entire conversion range.7,43,44 Therefore, this model satisfactorily predicts the effect of the heating rate and

carbon dioxide partial pressure in eq 3 was considered to be 1, whereas in the second alternative, α was taken as an adjustable parameter of the model. Figure 7 shows the results obtained by fitting the homogeneous model using the kinetic parameters shown in Table 2, corresponding to the best fitting. The fitting of the experimental and calculated results obtained in the dynamic runs (panels a and b of Figure 7) is acceptable but with certain deviation, especially at high conversion values. However, the fitting of the isothermal runs (Figure 7c) is very poor, with deviations being highly significant. The overall fitting of all of the experimental data is a challenging task, on the one hand, because the temperature range studied is wide, from 750 to 850 °C in the isothermal experiments and from 800 to 900 °C approximately in the dynamic experiments (see Figures 2b and 3b). Furthermore, the temperature seems to have a significant effect on char reactivity in the mentioned temperature ranges (Figure 5), which also hinders attaining a suitable fitting. On the other hand, the above-mentioned transient period at 800 and 850 °C poses an additional constrain for obtaining good agreement between experimental and calculated isothermal results. To improve the quality of the fitting, an order α ≠ 1 for the carbon dioxide partial pressure has been included as an adjustable parameter in the reaction rate expression (eq 3). Figure 8 shows the results for the best fitting corresponding to α = 0.88. As observed, the improvement obtained is rather limited. In fact, the error value defined by eq 11 and displayed in Table 2 is only slightly reduced. 3.2.2. nth Order Model. To simulate the variation of char reactivity with conversion, an order n can be included in the term (1 − X) considered in the reaction rate equation (eq 4). Thus, this nth order should take values below 1 to predict an increase in char reactivity with char conversion. The value of nth order obtained by fitting the modified homogeneous model is 0.80, with that of α being 0.81 (see Table 2). Different values are reported in the literature for this reaction order. Thus, Mani et al.13 obtained a higher reaction order (0.9) in the gasification of wheat straw char, but Wang et al. obtained values of 0.44 and 0.58 for wood char and forest residue char, respectively,11 and between 0.36 and 0.42 for torrefied biomass.38 Vamvuka et al.39 studied the gasification of different biomass chars, such as municipal solid waste (MSW), paper sludge, and sewage sludge, with the reaction order ranging from 0.4 to 0.6. The value obtained in this work is far from 0.66, corresponding to a shrinking core model,9,12,17,34,40 which assumes that the reaction takes place on the external surface of a non-porous particle with a progressive reduction in the particle size with conversion. Accordingly, the surface area available per char mass unit increases. Because the value of the order n of the best fit obtained in this study is higher than that corresponding to the shrinking core model, it may be concluded that the increase in char reactivity is less pronounced than that predicted by the mentioned model. G

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Figure 9. Comparison of the experimental and calculated DTG results using the nth order model. Dynamic results with different (a) heating rates and (b) CO2 concentrations and (c) isothermal runs.

Figure 10. Comparison of the experimental and calculated DTG results using the random pore model. Dynamic results with different (a) heating rates and (b) CO2 concentrations and (c) isothermal runs.

carbon dioxide concentration in the dynamic runs. However, the fit of the isothermal run data is not so satisfactory, which is probably due to the initial transient period occurring when the gasification agent was introduced into the thermobalance.11

The activation energy, frequency factor, and reaction order calculated on the basis of carbon dioxide pressure values are similar in both the modified and original random pore models. Concerning the values for the parameters c and p, H

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4. CONCLUSION The temperature has a great influence on carbon dioxide gasification kinetics of rice husk fast pyrolysis char. Below 750 °C, the reaction rate is almost negligible and strongly increases above 800 °C. Thus, the time required for full gasification of the sample was reduced to a 9th when the temperature was increased from 750 to 850 °C. However, the heating rate has no influence on gasification kinetics. Char reactivity increased monotonously to conversion values of 0.8. This increase is related to the pore development occurring throughout this stage. Furthermore, for higher conversion values, there is a sharp increase in the specific reaction rate because of the enhancement of the catalytic effect of the ashes, whose concentration in the char rises as conversion is increased. The random pore model and especially the modified random pore model have proven to be a better alternative than homogeneous and nth order models for the description of the experimental results. Thus, the deviation between the experimental results and those calculated using the modified random pore model is significantly lower than half of the value for the homogeneous model and approximately 50% of that corresponding to the nth order model. The kinetic parameters obtained are as follows: activation energy E = 263 kJ mol−1, reaction order α = 0.79, structural parameter ψ = 3.54, and dimensionless parameters c = 0.94 and p = 6.36.

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AUTHOR INFORMATION

Corresponding Author

*Telephone: +34946012527. Fax: +34-946-013-500. E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was carried out with the financial support from the Ministry of Science and Education of the Spanish Government (CTQ2013-45105-R), the FEDER funds, the Basque Government (Project GIC07/24-IT-748-13), and the University of the Basque Country (UFI 11/39).

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Figure 11. Comparison of the experimental DTG results and those calculated using the modified random pore model. Dynamic results with different (a) heating rates and (b) CO2 concentrations and (c) isothermal runs.

they are in the same range as those obtained by other authors in the gasification of biomass and biomass−tire mixture chars.10,45,46 I

NOMENCLATURE c = parameter of the modified random pore model (dimensionless) E = activation energy (kJ mol−1) k = kinetic constant (s−1 Pa−α) k0 = frequency factor (s−1 Pa−α) L = number of available experimental data L0 = initial length of the pores (m) n = reaction rate order referred to char conversion (dimensionless) p = parameter of the modified random pore model (dimensionless) PCO2 = carbon dioxide partial pressure (Pa) R = universal gas constant (8.314 × 10−3 kJ mol−1K−1) S0 = initial surface area (m2 g−1) t = time (min) T = temperature (K) w, w0, and w∞ = mass of the sample at time t, initial, and end of the run, respectively (mg) X = conversion (dimensionless) DOI: 10.1021/acs.energyfuels.5b00318 Energy Fuels XXXX, XXX, XXX−XXX

Article

Energy & Fuels Greek Letters

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α = reaction order referred to carbon dioxide pressure (dimensionless) ε0 = initial total volume (m3) Ψ = structural parameter of the random pore volume model (dimensionless)

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DOI: 10.1021/acs.energyfuels.5b00318 Energy Fuels XXXX, XXX, XXX−XXX