Energy & Fuels 2007, 21, 3695–3697
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Research on Water Evaporation in the Process of Biomass Pyrolysis Junmeng Cai* and Ronghou Liu* Biomass Energy Engineering Research Center, School of Agriculture and Biology, Shanghai Jiao Tong UniVersity, 800 Dongchuan Road, Shanghai 200240, P. R. China ReceiVed July 25, 2007. ReVised Manuscript ReceiVed August 9, 2007
The first step of biomass pyrolysis corresponds to water evaporation. In the present short paper, the possibility of applying the Weibull model for description of water evaporation in the process of biomass pyrolysis has been investigated. The fitting of the mass loss data in the water evaporation step using the proposed model, which can overcome the noise problem involved in the data and make the data analysis easier, is perfect. Considering the dependence of the evaporation enthalpy of water on temperature, the heat flow from the water contributions has been calculated. The results have shown that the heat flow from heat capacity of not yet evaporated water is almost negligible compared to the heat flow from water evaporation.
1. Introduction In the process of biomass pyrolysis, there are several steps. The first step corresponds to water evaporation, and the others correspond to thermal degradation of the different components of biomass (cellulose, hemicellulose, and lignin).1–3 The number of publications where the kinetic and thermodynamic behaviors of thermal degradation of the biomass components have been studied is vast. However, a few literatures concern the water evaporation step. In the paper of He et al.,4 the energy required to raise the sample from the room temperature to the reaction temperature and covert the solid biomass into the reaction products has been obtained. In their calculations, the constant heat flow from the water contributions was assumed. In the paper of Artiaga et al.,5 a method subtracting the heat flow corresponding to the water contributions from the differential scanning calorimetry (DCS) baseline has been developed. In their calculations, the constant evaporation enthalpy of water was assumed. Thermogravimetric analysis (TGA) is the most common technique used for the kinetic analysis of biomass pyrolysis.6,7 The TGA data contain some amount of noise. Maybe a small amount of noise is unimportant, but when further calculations are needed, i.e., to obtain first- or higher-degree derivatives, the noise may become an important problem.8 Several smoothing methods were proposed to reduce the noise.8 The fitting of * Corresponding author. E-mail:
[email protected] (J.C.) or
[email protected] (R.L.). (1) Yang, H.; Yan, R.; Yan, H.; Chen, H.; Lee, D. H.; Zheng, C. Characteristics of hemicellulose, cellulose and lignin pyrolysis. Fuel 2007, 86, 1781–1788. (2) Luangkiattikhun, P.; Tangsathitkulchai, C.; Tangsathitkulchai, M. Bioresour. Technol. 2007, doi: 10.1016/j.biortech.2007.03.001. (3) Sonobe, T.; Worasuwannarak, N. Fuel 2007, doi: 10.16/j.fuel. 2007.05.004. (4) He, F.; Yi, W.; Bai, X. Investigation on caloric requirement of biomass pyrolysis using TG-DSC analyzer. Energy ConVers. Manage. 2006, 47, 2461–2469. (5) Artiaga, R.; Naya, S.; García, A.; Barbadilo, F.; García, L. Subtracting the water effect from DSC curves by using simultaneous TGA data. Thermochim. Acta 2005, 428, 137–139. (6) Cai, J.; Liu, R. Bioresour. Technol. 2007, doi: 10.1016/ j.biortech.2007.06.033. (7) Brown, M. E. Introduction to Thermal Analysis: Techniques and Applications; Kluwer Academic Publishers: Boston, 2001.
sections of TGA curves, not including the water evaporation step, is usually performed using different models.9 Recently, a logistic approach was proposed for fitting an entire TGA curve of polymer degradation.8–11 The range of this study is limited to water evaporation in the process of biomass pyrolysis. The aim of the short present paper is to show a method to fit the mass loss process of water evaporation and to calculate the heat flow from the water contributions considering the dependence of the water evaporation enthalpy on temperature. 2. Experimental Section The biomass species used in this study are corn stalk and wheat straw, which are typical agricultural residues. The biomass samples are ground to a size range less than 0.15 mm. Thermogravimetric analysis experiments are carried out with a thermogravimetric analyzer. A brief summary of the experimental setup is described as follows: a small sample of starting biomass material (about 5 mg) is weighed and spread evenly in a sample crucible, and a pinhole was punched on the top; the crucible is then placed on the balance sample holder; the startup protocol is initiated; and finally, the sample is heated from room temperature to 773 K at a constant heating rate 30 K min-1 to a desired temperature using nitrogen as the carrier gas at a constant flow rate of 25 mL min-1. As shown in ref 11, these samples mass should be small enough to prevent heat transfer effects in nonisothermal thermogravimetric analysis experiments. Figure 1 shows the TGA and DTG curves obtained from a wheat straw sample. In the process of biomass pyrolysis, the (8) Cao, R.; Naya, S.; Artiaga, R.; García, A.; Varela, A. Logistic approach to polymer degradation in dynamic TGA. Polym. Degrad. Stab. 2004, 85, 667–674. (9) Capart, R.; Khezami, L.; Burnham, A. K. Assessment of various kinetic models for the pyrolysis of a microgranular cellulose. Thermochim. Acta 2004, 417, 79–89. (10) Barbadillo, F.; Fuentes, A.; Naya, S.; Cao, R.; Mier, J. L.; Artiaga, R. Evaluating the logistic mixture model on real and simulated TG curves. J. Therm. Anal. Calorim. 2007, 87, 223–227. (11) Naya, S.; Cao, R.; Ullibarri, I. L.; Artiaga, R.; Barbadillo, F.; García, A. Logistic mixture model versus Arrehenius for kinetic study of material degradation by dynamic thermogravimetric analysis. J. Chemom. 2006, 20, 158–163.
10.1021/ef700442n CCC: $37.00 2007 American Chemical Society Published on Web 09/25/2007
3696 Energy & Fuels, Vol. 21, No. 6, 2007
Cai and Liu
Figure 1. Wheat straw TGA plot decomposed into two processes. The range of study was limited to the area on the left of the vertical line.
Figure 2. Overlay of experimental data and Weibull model prediction in the case of corn stalk.
first mass loss at around 373 K is attributed to water evaporation.8,12,13 The water content is directly obtained and plotted in Figure 1, using the DTG peak as the end point of water evaporation, as shown in the paper of Artiaga et al.5 Thus, the original sample can be divided into two types of components: one corresponding to water and the other to the sample with water content subtracted. 3. Weibull Model for Description of the Water Evaporation Process Assuming that the reaction temperatures of water evaporation in the process of biomass pyrolysis are randomly distributed by the laws of the Weibull distribution function, and assuming that the value of the Weibull distribution function of reaction temperatures is proportional to the water content (c), it can be written
[(
)]
T - T0 (1) η where T is the temperature, T0 is the starting temperature of water evaporation, w is the total water content, F is the survival function of the Weibull distribution, and β and η are the shape and scale parameters of the Weibull distribution, respectively; η is expressed in K, and β is dimensionless. For fitting the data to the above-mentioned Weibull model, some estimation of the parameters in eq 1 is needed. The parameters of the model have been estimated by the nonlinear least-squares method. Thus, the residual sum of squares of the real values of water content and the values of water content calculated from eq 1 is used. It is defined for eq 2 by the following relation: c(T) ) wF ) w exp -
β
nd
RSS )
∑ (c
2 r,i - cc,i)
(2)
i)1
Figure 3. Overlay of experimental data and Weibull model prediction in the case of wheat straw. Table 1. Values of the Parameters of the Weibull Model for Water Evaporation of Two Biomass Samples with Corresponding Values of R2 corn stalk wheat straw
η/K
β
w
R2
49.370 58.135
2.070 2.153
0.02114 0.04015
0.999894 0.999709
method is a nonlinear regression method. Many methods use the gradient of the objective function, while the Levenberg–Marquardt method uses a Jacobian instead of gradient. More detailed information about the implementation of the Levenberg–Marquardt method can be found in the literature.16 The model was successfully applied to the water evaporation in the process of biomass pyrolysis for two samples analyzed in this study. The overlays of experimental data and the Weibull model prediction for corn stalk and wheat straw are shown in Figures 2 and 3, respectively. Values of the parameters in eq 1 and the correlation coefficients (R2) between experimental data and data calculated from eq 1 are included in Table 1. It is clear from Figures 2 and 3 and Table 1 that the fittings are very good (for the two cases, values of R2 are higher 0.9997). 4. Heat Flow from Water Contributions
where cr,i and cc,i are the water content in individual points of the real curve and the values calculated from eq 1 in the corresponding points, respectively; nd is the number of data points. The least RSS values are obtained for the best parameter estimates. There are a number of algorithms for minimizing eq 2, enabling us to find the best values of the parameters. In this paper, the Levenberg–Marquardt method14,15 was used for the determination of the parameters that minimize eq 2. The Levenberg–Marquardt
The aim of this section is to determine the heat flow from the water contributions in the process of biomass pyrolysis, which can be used to subtract the water effect from the heat flow data obtained by DSC experiments and to determine the energy requirement in the first step of biomass pyrolysis. The heat flow from the water contributions can be decomposed into two parts: one corresponding to water evaporation and the other to heat capacity of not yet evaporated water:
(12) Völker, S.; Rieckmann, Th. Thermokinetic investigation of cellulose pyrolysis - impact of initial and final mass on kinetic results. J. Anal. Appl. Pyrolysis 2002, 62, 165–177. (13) Liu, N. A.; Fan, W.; Dobashi, R.; Huang, L. Kinetic modeling of thermal decomposition of natural cellulosic materials in air atmosphere. J. Anal. Appl. Pyrolysis 2002, 63, 303–325.
(14) Levenberg, K. A method for the solution of certain problems in least squares. Q. Appl. Math. 1994, 2, 164168.. (15) Marquardt, D. An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 1963, 11, 431–441. (16) Bates, D. M.; Watts, D. G. Nonlinear Regression and Its Applications; Wiley: New York, 1988.
Water EVaporation in Biomass Pyrolysis
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heat flow from the water contributions ) heat flow from water evaporation + heat flow from heat capacity of not yet evaporated water (3) From eq 3, it follows dT dT dc dc Qw ) Qwe + Qwc ) r - ccp ) - ccp ) r dt dt dt dT dc - ccp (4) br dT where Qw is the heat flow from the water contributions per unit sample mass, W kg-1, Qwe is the heat flow from water evaporation per unit sample mass, W kg-1, Qwc is the heat flow from heat capacity of not yet evaporated water per unit sample mass, W kg-1, b ) dT/dt is the heating rate, K s-1, cp is the specific heat capacity of water, J kg-1 K-1, and r is the evaporation enthalpy of water, J kg-1. Since the Weibull model predicts the experimental data of water evaporation very well, we have replaced the water content in eq 4 with the Weibull model: T - T0 β wβ T - T0 β-1 exp Qw ) -br η η η T - T0 β (5) bwcp exp η In the paper of Artiaga et al.,5 a constant evaporation enthalpy of water was assumed. However, it is dependent on temperature. In this study, we considered the dependence of the evaporation enthalpy of water on temperature:17
(
)
(
(
)
[(
)]
[(
)
Figure 4. Calculated water evaporation, heat capacity of not yet evaporated water, and total contributions in the case of corn stalk.
)]
r(T) ) 103(2818.37 - 0.319294T - 3.18088 × 10-3T2), 289 e T e 443 (6) In the above equation, r is expressed in J kg-1 and T is expressed in K. The variations of the specific heat capacity of water with temperature are very small. Thus, in this study, cp of 4186.8 J kg-1 K-1 is assumed.18 The calculated heat flow from water evaporation, heat capacity of not yet evaporated water, and total water contributions for two samples are shown in Figures 4 and 5. It is clear that the heat flow from heat capacity of not yet evaporated water is almost negligible compared to the heat flow from water evaporation. 5. Conclusions The biomass pyrolysis process contains several steps, the first step corresponding to water evaporation and the others (17) Yan, J. L.; Yu, X. F. Thermodynamic Property Tables and Diagram for Water and Steam. (in Chinese); Higher Education Press: Beijing, 1995. (18) O’Connell, J. P.; Haile, J. M. Thermodynamics: Fundamentals for Applications; Cambridge University Press: New York , 2005.
Figure 5. Calculated water evaporation, heat capacity of not yet evaporated water, and total contributions in the case of wheat straw.
to thermal degradation of the different components of biomass. The range of this study is limited to the water evaporation step. To overcome the noise problem, the Weibull model has been proposed for fitting the mass loss data in the water evaporation step. The method has been successfully applied to two biomass samples. Considering the dependence of the evaporation enthalpy of water on temperature, the heat flow from the water contributions is calculated. The results have shown that the heat flow from heat capacity of not yet evaporated water is much less than the heat flow from water evaporation. Acknowledgment. Financial support was obtained from National Natural Science Foundation of China, Project No. 50276039. The authors would like to thank Ping Zhang, a Ph.D. student at Shanghai Jiao Tong University in China, for his constructive suggestions to improve this paper. EF700442N
