Study on
            <scp>
              CO
              <sub>2</sub>
            </scp>
            gasification kinetics of biomass char based on pore structure analysis: Theoretical modelling of structural parameter
            <i>ψ</i>
            in random pore model

Song, Qianshi; Wang, Xiaohan; Gu, Chunhan; Li, Haowen; Huo, Jiepeng

doi:10.1002/er.6113

Cited by 10 publications

(3 citation statements)

References 59 publications

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“…Until now, the RPM has been used to describe industrially relevant gas-solid reactions such as the carbonation of calcium oxide [42,43], char gasification [44][45][46] and combustion [47], and more recently, the reduction of iron oxides. However, it has been limited to describing a single reaction step or product layer systems, meaning its applicability has been severely restricted to the hematite-magnetite system [16] and the wustite-iron system [17] for iron oxides.…”

Section: (C) Mathematical Modelling Of Gas-solid Reactionsmentioning

confidence: 99%

A three-interface random pore model: the reduction of iron oxide in chemical looping and green steel technologies

Wong,

Iruretagoyena,

Shah

et al. 2023

Proc. R. Soc. A.

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Accurate modelling of the gaseous reduction of porous iron oxide powders or fines is important in industry for (i) reinventing the carbon intensive production of iron and steel and (ii) chemical looping technologies in the sphere of carbon capture and storage. A new three-interface random pore model is derived and applied to the gaseous reduction of hematite ( Fe 2 O 3 ) to iron (Fe). The structural reaction–diffusion model is able to describe three simultaneously reacting oxide layers, Fe 2 O 3 , magnetite ( Fe 3 O 4 ) and wustite ( Fe w O ). The geometric nature of the model encodes structural information about the particles (porosity, surface area, pore length and size distribution), measured here by experiment. The model is usefully able to separate structural particle properties from individual rates of reaction and product layer diffusion. The results have been compared and fitted to thermogravimetric experiments between 800 – 1000 ∘ C and three CO / CO 2 gas mixtures. Rate constants for each indvidual reaction have been obtained and fit well to Arrhenius plots. The reduction of Fe 2 O 3 – Fe 3 O 4 was controlled by diffusion and reaction kinetics, while the reduction of Fe 3 O 4 – Fe w O and Fe w O –Fe was limited by reaction kinetics. Metallization rates of the iron oxide powders were rapid, showing promise for both hydrogen-based direct reduced iron and chemical looping processes.

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Section: (C) Mathematical Modelling Of Gas-solid Reactionsmentioning

confidence: 99%

A three-interface random pore model: the reduction of iron oxide in chemical looping and green steel technologies

Wong,

Iruretagoyena,

Shah

et al. 2023

Proc. R. Soc. A.

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“…It can skip the detection of the characteristic parameter of char pores and directly predict char gasification by using the characteristic parameters of the sample. 19 In order to reduce the activation energy of the biomass gasification reaction, accelerate the gasification reaction rate, and improve the product quality, many researchers have added catalysts to the biomass. It has been proven that the addition of a catalyst enhances gasification reactions such as water gas, CO shift, steam methane, and tar cracking.…”

Section: Introductionmentioning

confidence: 99%

“…Song et al established a kinetic model by fitting the char‐CO 2 gasification experimental curves. It can skip the detection of the characteristic parameter of char pores and directly predict char gasification by using the characteristic parameters of the sample 19 …”

Section: Introductionmentioning

confidence: 99%

Study on the steam gasification reaction of biomass char under the synergistic effect of Ca‐Fe : Analysis of kinetic characteristics

Pang

Chen

et al. 2021

Int J Energy Res

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Biomass char gasification is an important part of biomass conversion technology; the addition of additives can effectively change the gasification reaction rate of char. The kinetic characteristics of steam gasification reaction of biomass char particles were explored by adding CaO, Fe 2 O 3 , and CaFe mixed catalyst to biomass, and the apparent activation energy and pre-exponential factors of char particles were obtained. Experimental results show that the kinetic reaction process of char steam gasification follows the shrinking cylinder model. Through linear fitting, it can be obtained that there is a good linear correlation between activation energy and pre-exponential factor, which follows the dynamic compensation effect. Under the same temperature conditions, CaO can accelerate the reaction rate of char, while Fe 2 O 3 can delay the gasification rate of char particles because of sintering phenomenon at high temperature. The synergistic effect of CaFe can reduce the total activation energy of the reaction. The addition of CaO can greatly change the kinetic performance of the Fe 2 O 3 catalyst, inhibit the sintering deactivation reaction of Fe 2 O 3 at high temperature, increase the diffusion path of steam, and promote the char steam gasification reaction.

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Prediction of gas‐liquid‐solid product distribution after solid waste pyrolysis process based on artificial neural network model

Wang

Song

et al. 2021

Int J Energy Res

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Four solid wastes including sludge, watermelon rind, corncob, and eucalyptus and their demineralized samples were selected to conduct pyrolysis experiments under different experimental conditions, including temperature, residence time, carrier gas flow rate, and heating rate, respectively. The copyrolysis experiment was carried out after mixing different types and different ratios of solid wastes to investigate the influence of different factors on yield of char, tar, and gas. A three-layer artificial neural network (ANN) based on back propagation (BP) algorithm was developed and trained to simulate and predict the yield of products. The experimental conditions and characteristic parameters of samples, including the content of C, H, K, Ca, Mg, Fe, volatile, ash, and fixed carbon, were selected as input parameters while the yields of char, tar, and gas were selected as output parameters. The effect of input parameters including residence time, carrier gas flow rate, heating rate, and the content of metal elements on the network performance was investigated in detail to optimize the ANN model. It was found that the metal element has the greatest influence, and its importance to the yield of each product exceeds 25%. The pyrolysis experimental data of single-component solid waste were selected as the model training set for model learning and training and then use the trained model to predict the co-pyrolysis experimental data of multicomponent solid waste. Good agreement was achieved between experimental and predicted results; the correlation coefficient was 0.9836, and the root mean

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Study on CO ₂ gasification kinetics of biomass char based on pore structure analysis: Theoretical modelling of structural parameter ψ in random pore model

Cited by 10 publications

References 59 publications

A three-interface random pore model: the reduction of iron oxide in chemical looping and green steel technologies

A three-interface random pore model: the reduction of iron oxide in chemical looping and green steel technologies

Study on the steam gasification reaction of biomass char under the synergistic effect of Ca‐Fe : Analysis of kinetic characteristics

Prediction of gas‐liquid‐solid product distribution after solid waste pyrolysis process based on artificial neural network model

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Study on CO 2 gasification kinetics of biomass char based on pore structure analysis: Theoretical modelling of structural parameter ψ in random pore model

Cited by 10 publications

References 59 publications

A three-interface random pore model: the reduction of iron oxide in chemical looping and green steel technologies

A three-interface random pore model: the reduction of iron oxide in chemical looping and green steel technologies

Study on the steam gasification reaction of biomass char under the synergistic effect of Ca‐Fe : Analysis of kinetic characteristics

Prediction of gas‐liquid‐solid product distribution after solid waste pyrolysis process based on artificial neural network model

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Product

Resources

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Study on CO ₂ gasification kinetics of biomass char based on pore structure analysis: Theoretical modelling of structural parameter ψ in random pore model