The Composition of Syngas and Biochar Produced by Gasifier from Viet Nam Rice Husk

Nguyen, Huy Bich; Lanh, Nguyen Van; Hung, Bui Ngoc

doi:10.18517/ijaseit.7.6.2623

Cited by 4 publications

(4 citation statements)

References 15 publications

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“…In particular, the presence of some solid output can be seen at the end of the process, which is known as biochar. The biochar is mainly made up of carbon and is formed by the biomass that has not reached the reaction zone [10]. To consider that not all the carbon participates in the equilibrium reactions, the n1 factor has been introduced.…”

Section: B Model Developmentmentioning

confidence: 99%

See 1 more Smart Citation

Performance of Household Rice Husk Downdraft Gasifier in Vietnam: Modeling and experiment

Lanh¹,

Nguyen²,

Hung³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

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Section: B Model Developmentmentioning

confidence: 99%

“…A general summary of the various numerical approaches developed up to the present is first presented to focus on thermochemical equilibrium models [15], [16]. More detail about the composition of syngas and biochar produced by gasifier from Viet Nam rice husk [10].…”

Section: Introductionmentioning

confidence: 99%

Performance of Household Rice Husk Downdraft Gasifier in Vietnam: Modeling and experiment

Lanh¹,

Nguyen²,

Hung³

et al. 2021

International Journal on Advanced Science, Engineering and Information Technology

Self Cite

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“…Coke is generally denser and relatively strong, produced from bituminous coal (Sun et al, 2015; P. Wang et al, 2020). The development of upgrading low-rank coal is an effort to increase the calories of low-rank coal (Bich et al, 2017;Deska et al, 2018;Zhuo et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Characteristics of Char from Low-Rank Coal Gasification

Asof¹,

Arita²,

Andalia³

2023

J. Ecol. Eng.

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The coal gasification process in this study aimedto increase the quality of low-rank coal (LRC) from lignite type to coal with semi-anthracite and anthracite grades with higher fixed carbon values. The gasification process wascarried out at temperatures between 700-900 °C. In the initial stages of the study, the relationship between the increase in gasification temperature with the mass of coal and the gasification process time was investigated. The proximate and ultimate analysis wascarried out to determine the characteristics of the raw materials used and the coal char produced from combustion. The analysis results are used to calculate the fuel ratio, where the fuel ratio is a parameter to determine the grade of coal. In addition, an SEM-spot EDS analysis was carried out on the gasified coal solids to ensure the fixed carbon content produced from the above process. The results showed that the gasification process could raise the grade of LRC into char products with semi-anthracite to anthracite, where the fuel ratio reached 10.84. From the product analysis results, the LRC coal gasification results using SEM-EDS showed that carbon reached 98.58%. The remaining inorganic elements in the product are less than 1.5%, such as Al, Si and K.

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“…As mentioned before, rice husks will go through the gasification process. The gasification process has been widely studied by previous researchers using various gasifier designs [3][4][5][6][7]. Gasification design is one of the main factors in the production of the producer gas output.…”

Section: Introductionmentioning

confidence: 99%

Producer Gas Composition Prediction using Artificial Neural Network Algorithm

Mohammud,

Mohamad Bakre,

Mohd Fohimi

et al. 2023

EAJ

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Nowadays, methods to increase efficiency in producer gas have become major issues in biomass gasification research. Producer gas is a renewable energy source that does not take as much time to obtain as fossil fuels. It is typically a mixture of combustible gases like carbon monoxide, hydrogen and methane, and non-combustible gases like carbon dioxide and nitrogen. A high percentage volume of combustible composition in the producer gas output will have a high calorific value or heat of combustion. These combustible gases are determined by the design of the gasifier. In today's era of Industrial Revolution 4.0 and Society 5.0, the use of simulation is highly prioritised in all aspects of engineering, especially in gasification applications. Simulation is a useful tool for learning about the governing principles and optimal operating points of the gasification process. Artificial intelligence (AI), is a major focus of Industry Revolution 4.0. In this project, the producer gas composition prediction is studied by computer simulation. The goals are to predict the output producer gas using an algorithm and to compare the trained prediction result with actual experiment data for rice husk gasification. This simulation was created with MATLAB software's artificial neural network (ANN). Three parameters (the height of the gasifier, the diameter of the gasifier, and the weight of the rice husk) are set as input data, and six types of the composition of producer gas (carbon dioxide, carbon monoxide, methane, oxygen, hydrogen, and nitrogen) are set as output data. The algorithm is trained, tested, and verified with the experiment data. It is then used to predict the output gas composition from the parameters of a gasification experiment that has been used before in UiTM’s laboratory. The simulation results of producer gas composition between prediction and actual values revealed a relative error of 1.159 %, 0.370 %, and 0.330 %. These results were less than 9% and were found to give a very good fit to the neural network algorithm.

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The Composition of Syngas and Biochar Produced by Gasifier from Viet Nam Rice Husk

Cited by 4 publications

References 15 publications

Performance of Household Rice Husk Downdraft Gasifier in Vietnam: Modeling and experiment

Performance of Household Rice Husk Downdraft Gasifier in Vietnam: Modeling and experiment

Characteristics of Char from Low-Rank Coal Gasification

Producer Gas Composition Prediction using Artificial Neural Network Algorithm

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