The kinetic analysis of the pyrolysis of agricultural residue under non-isothermal conditions
Highlights
► Thermal decomposition characteristics of corn straw and rice husk. ► Characteristic parameters of TG–DTG curves of the samples are calculated. ► Variation of activation energy corresponding to different conversion fractions. ► Variation of reaction order corresponding to varied heating temperatures.
Introduction
A large number of agricultural wastes have not been properly utilized in China every year. Corn and rice are the most widely cultivated crop in China, especially in the eastern area of the country. Consequently, as the agricultural waste of the two crops, corn straw and rice husk are abundantly available every year. The National Bureau of Statistics of China (NBSC) has reported in China Statistical Yearbook that the predicted availabilities of the two agricultural wastes are estimated to be approximately 220 Mt/year (dry basis) and 100 Mt/year (dry basis) respectively (National Bureau of Statics of China, 2009). However, a majority of the two kinds of agricultural wastes is burned in the farmland every year, which seriously pollutes the atmospheric environment and wastes the various biomass resources.
In order to address this common problem brought by direct burning, and promote the use of clean and highly efficient energy derived from agricultural wastes in rural areas, researchers have continuously paid special attention to convert agricultural wastes into solid, liquid or gaseous fuels using thermochemical conversion (TCC) technologies, which are summarized in Table 1. As for the biomass gasification, it is an important technology for converting wastes into combustible gas and subsequent use in power generation (Karmakar and Datta, 2011).
As a separate technology and the preliminary stage of combustion and gasification, pyrolysis not only involves complicated chemical processes, but also includes complex physical processes such as heat transfer, mass transfer and their interactions. It has a significant effect on the gasification process. According to White et al. (2011), a thorough understanding of pyrolysis kinetics is vital to supply guidance on the feasibility, design, and scaling of industrial gasification reactors, as well as pave the way for optimizing the operating conditions. In addition, as already discussed in a previous paper (Gai and Dong, 2012), during the gasification of agricultural wastes, apart from the pollutants of particulate dust and condensable tars, the detrimental element of sulphur and chlorine in the feedstock are also released in several chemical forms in terms of gaseous compounds or bounded to the ash. In terms of the potential technologies of syngas cleaning, it can generally be divided into two categories including treatments during gasification and gas cleaning after gasification. Undoubtedly, gasifier design parameters and operating parameters have effect on the formation of those pollutants during gasification. Therefore, in order to minimize the formation of such pollutants during the gasification process, the knowledge of pyrolysis kinetics is also needed, which is usually investigated by the thermogravimetric analysis (TGA).
TGA can be carried out under isothermal or non-isothermal conditions. The main disadvantage of the traditional isothermal method is that there is a small mass-lose before reaching the desired temperature, causing a certain error when investigate the pyrolysis reaction mechanism of the solid state. Therefore, non-isothermal method has become a widespread analytical technique in recent decades due to the high sensitivity to experimental noise compared to isothermal methods. Characteristic parameters of the sample can be obtained from the TG and DTG curves during the TGA experiments; then in order to investigate the pyrolysis mechanism, the kinetic parameters of pyrolysis reactions can be calculated using the characteristic parameters, including apparent activation energy E, pre-exponential factor A, reaction order n and reaction model f(α). Assessing these kinetic parameters of pyrolysis of the sample rely on different mathematical approaches.
Many mathematical approaches have been developed to calculate the kinetic parameters, in terms of Coats–Redfern (Daood et al., 2010), Freeman and Carroll method (Font and Williams, 1995), et al. These methods are usually based on the preliminary assumption of a certain reaction order and reaction model, such as mampel, nucleation, autocatalytic, diffusional and contracting geometry, et al. Then the apparent activation energy and pre-exponential factor are calculated simultaneous, but not separately. Consequently, these approaches are viewed as the model-fitting method. This kind of method often employs the single and simple reaction model during the reaction process, which can result in a substantial divergence; this discrepancy results from the difference between the ideal reaction model and actual heterogeneous reaction process consisting of series of parallel and sequential reactions. Besides, the values of E and A will vary greatly on condition that different reaction model is preliminary assumed; but the two parameters can always get good linear regression between each other. This manifestation of ‘kinetic compensation effect’ could result in some difficult in determining the accurate reaction model, since more than one reaction model can be obtained using this method. Therefore, adopting the model-fitting method may bring about the highly uncertain values of the kinetic parameters.
In respect to these faults of the model-fitting method, another approach called model-free method has been more widespread employed to assessing the pyrolysis kinetics of the solid state in recent years, such as Friedman (Aboyade et al., 2011), Flynn–Wall–Ozawa (Ounas et al., 2011) and Kissinger–Akahira–Sunose (Damartzis et al., 2011) approach. In this method, the apparent activation energy can be calculated at the same fraction conversion of the sample in multiple TG curves, without assuming the reaction model. Hence this approach eliminates the phenomenon of ‘kinetic compensation effect’. And it is also called the iso-conversional approach, since the apparent activation energy obtained by this approach is a function of the extent of conversion. Multistep reaction mechanism of the pyrolysis kinetics can be obtained from the knowledge of the relationship between apparent activation energy and conversion degree. Besides, it is adequate to evaluate the reaction kinetics over a wider temperature region.
Therefore, it is more reliable for analyzing the thermal data using the multi-heating rate methods together with iso-conversional approaches in recent years. Iso-conversional approaches have been increasingly used to investigate the thermal decomposition kinetics of varied materials such as cattle manure (Otero et al., 2011), sensitive plants (Wongsiriamnuay and Tippayawong, 2010) or municipal solid waste (Liu et al., 2009).
In terms of the studies of pyrolysis kinetics of corn straw and rice husk, most investigations adopt the model-fitting method, and usually take the first-order reaction as the presumed reaction model. Some other researches generally assume a certain reaction order (e.g. normally range from zero-order to three-order) and a particular reaction model. However, just as the former discussion, the pyrolysis of the solid-state sample is a complicated physical and chemical process. Not only does the reaction order may vary greatly during the reaction process, but such significant difference may also exist in the reaction model preliminary selected. Therefore, the main objective of this study is to investigate the variation of pyrolysis kinetics of corn straw and rice husk at different conversion fractions and reaction temperature. It is hoped that the pyrolysis kinetics of agricultural wastes obtained in the current study can give a better and more comprehensive understanding of the pyrolysis process, as well as to help establish the kinetic model of biomass gasification for design and scaling of industrial gasification reactors.
Section snippets
Raw material properties
As typical kinds of agricultural residues, corn straw and rice husk were selected to carry out thermogravimetric experiments. They were both locally available materials. Before the pyroylsis tests, the samples were air-dried, crushed and then sieved. The fraction 20–40 mesh was used as power samples for the experimental runs. As for the properties of the applied feedstocks, the methods of proximate and ultimate analysis are described by Gai and Dong (2012). The proximate, ultimate and
Thermal decomposition characteristics of CS and RH
The TG (Thermogravimetric) and DTG (Differential Thermogravimetric) curves of CS and RH at different heating rates from 5 to 40 K/min are illustrated in Fig.1 and Fig.2. It can be observed that the two samples resembled each other in TG–DTG curves, which both presented two comparatively large losses of weight. As a whole, the entire thermal decomposition process of the corn straw sample is later than that of rice husk, whereas the mass-loss extent of the corn straw is greater than that of rice
Conclusions
TGA of CS and RH indicates that the two samples both showed two relatively large weight losses. The characteristic parameters of Tp, Ti, Tf, HTZ, −DTGmax and ML were found to have inconsistent variation trends. With conversion fraction increasing from 20% to 80 %, the apparent activation energy of CS initially increased from 98.715 to 148.062 kJ/mol and then decreased to 144.387 kJ/mol; as for the RH, it was continuously increased from 50.492 to 88.994 kJ/mol. For the range of temperatures
Acknowledgements
Financial support for this study that has been provided by Shandong Provincial Natural Science Foundation of China (2009ZRA01100 and ZR200EZ001) and National Science and technology support program (2010BAC66B02, 2011BAD15B05-05 and 2011YQ120039) is gratefully acknowledged.
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