A comprehensive model of biomass char-CO2 gasification reactivity with inorganic element catalysis in the kinetic control zone based on TGA analysis

Song, Qianshi; Wang, Xiaohan; Gu, Chunhan; Wang, Ning; Li, Haowen; Su, Hang; Huo, Jiepeng; Qiao, Yu

doi:10.1016/j.cej.2020.125624

Cited by 25 publications

(7 citation statements)

References 59 publications

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“…It is thought that the metal elements in the sample have a mutual coupling effect, and this effect is not considered in this model. 38 At the same time, based on previous studies, it can be seen that changes in the chemical structure of the internal substances and the pore parameters of the sample will also cause changes in the yield. 39 4.…”

Section: Discussionmentioning

confidence: 96%

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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Section: Discussionmentioning

confidence: 96%

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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“…Group 1 alkali metal elements (K, Na)-occur predominately in water-soluble/ionexchangeable forms (i.e., organically bound); these elements are generally highly reactive during combustion and contribute to the formation of inorganic phases with lower melting points [26]. A detailed analysis has been introduced in our previous study [27]. Therefore, alkali metal elements can promote the escape of volatile components and improve the internal heat and mass transfer during sludge pyrolysis [28].…”

Section: Characteristics Of Inorganic Elementsmentioning

confidence: 99%

Insight into the Effects of Inorganic Element Catalysis and Basic Fuel Properties on the Self-Sustained Smoldering Process of Sewage Sludge

et al. 2022

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The objective of this study is to investigate the effects of inorganic element catalysis and basic fuel properties of sewage sludge on pyrolysis kinetics and self-sustained smoldering characteristics. The sludge pyrolysis process was explored by thermogravimetric and iso-conversion methods, and it was found that the pyrolysis process can be divided into two stages, which are mainly determined by the organic and inorganic components of the fuel. The inorganic components (e.g., Na, Fe and Mn) have a significant catalytic effect on the release of volatiles and the decomposition of macromolecules. The smoldering experiment revealed that the smoldering front and the evaporation front propagated at stable but different speeds. Among the five fuels, SS4 has the highest smoldering temperature (1070 °C) and the lowest propagation velocity (0.7 cm/min of smoldering velocity and 0.3 cm/min of evaporation velocity), while the carbon density mainly determines the heat release in the oxidation process, and the inorganic elements play a significant catalytic role at different temperatures. The obtained thermodynamic and smoldering characteristics facilitate the development and optimization of the disposal of sewage sludge, emphasizing the importance of considering feedstock composition.

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“…In this context, the reaction rate for the gasification of hardwood char has often been found to increase rapidly in the latter half of the reaction when the inorganic components have become concentrated; hence, these components have been suggested to accelerate the reaction. 17,18 Moreover, the char-CO 2 gasification reaction is the ratelimiting step in the steam gasification of char, 19,20 and knowledge of CO 2 gasification kinetics is of great significance for determining sample residence times in gasifiers and reactor size in gasifier design. For decades, obtaining an accurate and convenient char-CO 2 reactivity model has been a longstanding goal for researchers.…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the char-CO 2 gasification reaction is the rate-limiting step in the steam gasification of char, , and knowledge of CO 2 gasification kinetics is of great significance for determining sample residence times in gasifiers and reactor size in gasifier design. For decades, obtaining an accurate and convenient char-CO 2 reactivity model has been a longstanding goal for researchers. − …”

Section: Introductionmentioning

confidence: 99%

Hardwood Branch Char Gasification with CO₂: Characterization of Reactivity and Proposal of a Semiempirical Kinetic Model

Koido,

Hoshi,

Sato

2023

Energy Fuels

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Pruned branches (PBs) from fruit trees are the only woody biomass produced in agriculture in Japan and are thus considered a local resource. Yet, the use of fruit-tree PBs to produce energy on a small scale has not been investigated. Here, fuel characteristics of PB char were studied considering its use in gasification for heat and power generation, and a gas−solid reaction model was proposed to describe the CO 2 gasification of char produced from PBs. The reaction rate patterns generated during the CO 2 gasification of the studied char can be broadly classified into those associated with cultivated fruit-tree PBs, park tree PBs, and timber waste. The porous structure of the char indicated that the CO 2 molecules diffused into the pores via Knudsen diffusion, and fruit-tree PBs had a smaller average pore diameter but a higher Brunauer−Emmett−Teller (BET) surface area. In addition, the extended modified random pore model (eMRPM) was proposed as a new gas−solid reaction model. This model describes the peak of gasification rate at the end of gasification more accurately than conventional models. Furthermore, higher K and Ca contents in char resulted in a higher degree of correlation between the eMRPM and experimental data at 800 °C.

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A comprehensive model of biomass char-CO2 gasification reactivity with inorganic element catalysis in the kinetic control zone based on TGA analysis

Cited by 25 publications

References 59 publications

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

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

Insight into the Effects of Inorganic Element Catalysis and Basic Fuel Properties on the Self-Sustained Smoldering Process of Sewage Sludge

Hardwood Branch Char Gasification with CO₂: Characterization of Reactivity and Proposal of a Semiempirical Kinetic Model

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A comprehensive model of biomass char-CO2 gasification reactivity with inorganic element catalysis in the kinetic control zone based on TGA analysis

Cited by 25 publications

References 59 publications

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

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

Insight into the Effects of Inorganic Element Catalysis and Basic Fuel Properties on the Self-Sustained Smoldering Process of Sewage Sludge

Hardwood Branch Char Gasification with CO2: Characterization of Reactivity and Proposal of a Semiempirical Kinetic Model

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Hardwood Branch Char Gasification with CO₂: Characterization of Reactivity and Proposal of a Semiempirical Kinetic Model