Elsevier

Energy Conversion and Management

Volume 135, 1 March 2017, Pages 453-462
Energy Conversion and Management

Kinetic study and syngas production from pyrolysis of forestry waste

https://doi.org/10.1016/j.enconman.2016.12.086Get rights and content

Highlights

  • Pyrolysis process can be divided into three stages using differential DTG method.

  • A modified discrete DAEM model fitted experimental data well.

  • Fe/biochar catalyst showed a good performance on catalytic reforming process.

Abstract

Kinetic study and syngas production from pyrolysis of forestry waste (pine sawdust (PS)) were investigated using a thermogravimetric analyzer (TGA) and a fixed-bed reactor, respectively. In TGA, it was found that the pyrolysis of PS could be divided into three stages and stage II was the major mass reduction stage with mass loss of 73–74%. The discrete distributed activation energy model (DAEM) with discrete 200 first-order reactions was introduced to study the pyrolysis kinetic. The results indicated that the DAEM with 200 first-order reactions could approximate the pyrolysis process with an excellent fit between experimental and calculated data. The apparent activation energies of PS ranged from 147.86 kJ·mol−1 to 395.76 kJ·mol−1, with corresponding pre-exponential factors of 8.30 × 1013 s−1 to 3.11 × 1025 s−1. In the fixed-bed reactor, char supported iron catalyst was prepared for tar cracking. Compared with no catalyst which the gas yield and tar yield were 0.58 N m3/kg biomass and 201.23 g/kg biomass, the gas yield was markedly increased to 1.02 N m3/kg biomass and the tar yield was decreased to only 26.37 g/kg biomass in the presence of char supported iron catalyst. These results indicated that char supported iron catalyst could potentially be used to catalytically decompose tar molecules in syngas generated via biomass pyrolysis.

Introduction

Forestry wastes, in general, and all biomass residues, can be used as raw materials for the generation of liquid biofuels, syngas, chemicals, or charcoal via pyrolysis and liquefaction processes [1], [2], [3]. Thermochemical conversion methods, i.e. pyrolysis, gasification and combustion, are the most commonly employed and the most appropriate for these purposes. Most of the biomass have a heterogeneous property attributes to the fact that the biomass itself composes of numerous components, such as hemicellulose, cellulose, lignin, and minor amounts of extractives. Such of these components, in fact, contribute to the actual reaction mechanism of biomass pyrolysis possibly is extremely complex [4]. The proportion and composition of pyrolysis products are crosswise affected by many factors such as biomass type, feedstock pretreatment, and pyrolysis conditions. During the biomass pyrolysis, a large number of reactions take place in parallel and series, including dehydration, depolymerisation, isomerization, aromatisation, decarboxylation, and charring [4], [5]. Kinetic modeling of pyrolysis can help to describe practical conversion processes and optimize the design of efficient reactors [6].

Thermogravimetric analysis (TGA) is a useful technique for studying the decomposition reactions of a solid and it has been widely used to study the apparent kinetics of biomass pyrolysis [7]. By using the TGA data, the kinetic parameters as well as pyrolysis mechanism can be determined according to different mathematical approaches. The single-step global model couples with different iso-conversional method, is the most used kinetic approach. However, as mentioned above, the biomass pyrolysis process is extremely complex due to the difference in decomposition of the biomass components. The iso-conversional method is considered to be conflicting rather than complementary when treating the kinetics of complex reaction system. According to the International Confederation for Thermal Analysis and Calorimetry (ICTAC) Kinetics Committee recommendations, multi-step reaction model is more suitable to simulate solid fuels (such as coal and biomass) pyrolysis kinetic [8]. Among many multi-step reaction models, distributed activation energy model (DAEM) is one of the commonly used in biomass pyrolysis kinetics studies [3], [9]. Many methods such as model-fitting method, iso-conversional method and discretization method can be used to treat the DAEM and between them the discrete DAEM is even better for DAEM calculation.

During biomass pyrolysis, there are many obstacles need to be resolved before it further become a viable commercial renewable energy. The generation of tar in product gas is one of the major issues, which is known as energy waste, block pipeline, and even threat to human health. Methods of physical treatment, thermal cracking, plasma-assisted cracking, and catalytic reforming, etc. are considerable to eliminate the tar. Among these methods, catalytic reforming is considered the most promising approach for the tar removes as well as convert to combustible gas [10]. Various types of catalysts such as minerals (iron ores, clay minerals, olivine, calcined rocks) and synthetic catalysts (transition metals-based, activated alumina, alkali metal carbonates, FCC catalysts, and char/char-supported) have been studied on tar removal in biomass pyrolysis/gasification [11], [12], [13], [14], [15], [16]. Among these catalysts, the char/char-supported catalysts have shown low costs and adequate catalytic activities for tar reforming during the pyrolysis/gasification of biomass.

In this study, behaviors and kinetics of a representative forestry waste (pine sawdust) pyrolysis were investigated using a discretion DAEM method via thermogravimetric analysis. Meanwhile, char supported iron catalyst used for syngas production was also investigated.

Section snippets

Materials

Forestry waste (Pine Sawdust (PS), belong the species of macrophanerophytes) was obtained from a furniture factory Wuhan City, Hubei Province, China. The sawdust was naturally dried for a period of 7 days and then grinded and screened into a size of <0.107 mm (pass a Tyler standard screen scale of 100 mesh). Table 1 gives the results of proximate and ultimate analysis of pine sawdust feedstock. Ultimate analysis of the PS samples was measured by a CHNS/O analyzer (Vario Micro cube, Elementar).

Characterization of pine sawdust

The proximate and ultimate analysis results, as well as the low calorific value (LHV) of pine sawdust (PS) are listed in Table 1. As seen in Table 1, proximate analysis results for PS have a high amount of volatile matter content (82.18%) which could be considered suitable for combustion, pyrolysis or gasification process. Another important feature for PS is that the ash content (1.69%) is very low than previous studied biomass in literatures, such as hazelnut husk (5.27%) [6], rice husk

Conclusions

Pyrolysis behaviors and kinetics of forestry waste (pine sawdust) were studied. The decomposition process of pine sawdust could be divided into three stages. The kinetics of the main stage II was modeled in modified discretize distributed activation energy model (DAEM). The DAEM with 200 first-order reactions showed an excellent fit between experimental results and simulated data. With the Fe/biochar catalyst, the tar yield obviously decreased, and the gas yield significantly increased. Under

Acknowledgment

This research was supported by the China Postdoctoral Science Foundation – China (2015M580644 and 2016M592339), Wuhan International Science and Technology Cooperation Project (2016030409020221) and Ministry of Housing and Urban-Rural Development – China (No. 2016K4032). Authors would also like to thank the Analytical and Test Center of Huazhong University of Science and Technology for carrying out the analysis of the characterization of the samples.

References (28)

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