Hydrogen-rich gas production from biomass air and oxygen/steam gasification in a downdraft gasifier

Lv, Pengmei; Yuan, Zhenhong; Ma, Longlong; Wu, Chao; Chen, Yong; Zhu, Jesse

doi:10.1016/j.renene.2006.11.010

Cited by 289 publications

(105 citation statements)

References 11 publications

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“…They are estimated based on literature data [23] obtained for wood. The composition of tar is determined by the tar cracking reaction, and the stoichiometric coefficient of tar, α 6 , is calculated by the elemental balance of the biomass pyrolysis reaction.…”

Section: Model Descriptionmentioning

confidence: 99%

Energy and Exergy Analysis of High Temperature Agent Gasification of Biomass

Yang

Blasiak

2014

Energies

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A chemical equilibrium model was developed to predict the product composition of a biomass gasification system using highly preheated air and steam. The advantages and limitations of this system were discussed from a thermodynamic viewpoint. The first and second law analyses have been conducted for various preheating temperatures and steam/biomass mass (S/B) ratios. The results demonstrated that the chemical energy output of the produced syngas is highest when the S/B ratio is 1.83 under the conditions used in this study. However, higher S/B ratios have a negative effect on the energy and exergy efficiencies. Higher preheating temperatures increase the chemical energy of the produced syngas and the two efficiencies. The peak values for the energy and exergy efficiencies are 81.5% and 76.2%, respectively. Based on the calculated limitation values, where the highest chemical energy (exergy) of the produced syngas and maximum achievable efficiencies are determined, a thermodynamically possible operating region is suggested.

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Section: Model Descriptionmentioning

confidence: 99%

Energy and Exergy Analysis of High Temperature Agent Gasification of Biomass

Yang

Blasiak

2014

Energies

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“…Contradictory reports are found in literature, while Lv et al [41] reported a positive effect on hydrogen content when biomass/steam ratio increases. Lv et al [33] reported a negative effect of biomass/steam ratio increase on syngas hydrogen content. This variation can be understood by considering that biomass/steam ratio effect is altered according to the entire system configuration.…”

Section: Effect Of Biomass/steam Ratiomentioning

confidence: 99%

“…Lv et al [33] developed a downdraft gasifier to produce hydrogen from biomass using air and oxygen/steam as gasifying agents. Total 5 kg of char were supported on the grate to reduce tar content and to act as a catalyst to upgrade syngas.…”

Section: Biomassmentioning

confidence: 99%

Review of Biomass Thermal Gasification

Hamad¹,

Radwan²,

AshrafAmin³

2017

Biomass Volume Estimation and Valorization for Energy

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Gasification of biomass is one of the most attractive methods for producing hydrogen rich gas. Syngas production from biomass is an attractive solution for energy crisis. The production of energy from biomass reduces the dependence of developing countries on fossil fuels, as ample biomass is available in the developing countries and is renewable. Downdraft gasifiers are fixed bed gasifiers where the gasifying agent and biomass are flowing downwards, developed for high-volatile fuels such as wood or biomass gasification. Cocurrent flow regime throughout the oxidation and reduction zones reduces the tars and particulates in syngas, which will reduce the necessity of complicated cleaning methods compared to updraft gasifiers especially if the gas is used as a burnable gas in a small community. It is important to ensure homogenous distribution of gasifying agent at the downdraft gasifier throat. This chapter presents latest trends in gasification of biomass using downdraft gasification.

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“…When distributing small-scale fuel cellｓ to an urban area, installation of steam reforming of natural gas is influential 1), 2) . Hydrogen production methods not dependent on fossil fuel have been investigated 3), 4) . We are investigating the technique of bioethanol steam reforming using daylight as a heat source 5) .…”

Section: Introductionmentioning

confidence: 99%

Operation Prediction of a Bioethanol Solar Reforming System Using a Neural Network

Obara

Tanno

2007

JTST

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The bioethanol reforming system (FBSR) using sunlight as a heat source is a fuel production system for fuel cells with little environmental impact. However, because solar radiation and outside air temperature are unstable, it is difficult to predict operation of the system with accuracy. Therefore, an operation prediction program of the FBSR using a layered neural network (NN) with the error-correction learning method has been developed. We developed a method of analyzing the operation of a natural energy system with sufficient accuracy. The weather pattern (the amount of global solar radiation and the outside air temperature) and energy-demand pattern for the past one year are inputted into the NN. Moreover, training signals are calculated by a genetic algorithm (GA). The training signals are given to the NN, and the operation pattern of the FBSR is made to learn. Operation of the FBSR on arbitrary days can be predicted by inputting the weather pattern and the energy-demand pattern into this learning NN. In this paper, the operation prediction program of the FBSR is developed, and details of the analytic accuracy are clarified. As a result of analyzing using the developed algorithm, when ±20% or less of power load fluctuation occurred, the operation plan was analyzable in 14% or less of error span. On the other hand, in operation prediction when ±50% or less of fluctuation is added to the outside temperature and global solar radiation, there was 16% or less analysis error.

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Hydrogen-rich gas production from biomass air and oxygen/steam gasification in a downdraft gasifier

Cited by 289 publications

References 11 publications

Energy and Exergy Analysis of High Temperature Agent Gasification of Biomass

Energy and Exergy Analysis of High Temperature Agent Gasification of Biomass

Review of Biomass Thermal Gasification

Operation Prediction of a Bioethanol Solar Reforming System Using a Neural Network

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