The impact of aluminosilicate-based additives upon the sintering and melting behaviour of biomass ash

Roberts, Lee J.; Mason, Patrick E.; Jones, Jenny M.; Gale, William F.; Williams, Alan; Hunt, Adam; Ashman, James Mark

doi:10.1016/j.biombioe.2019.105284

Cited by 49 publications

(21 citation statements)

References 17 publications

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“…In addition, the slag should be readily transformed into the glassy form (vitrified) with a minimal leaching propensity so that the hazardous elements contained within will not be leaked into the environment. Practically, the slag's viscosity-temperature relationship is a key factor in determining whether a certain hazardous waste is suitable for molten gasification [1,4,5]. In summary, to ensure the effective immobilization of hazardous elements from waste material through the fusion-solidification-vitrification process, it is very important to figure out the type and viscosity of molten slag, which is the issue to be tackled in this work.…”

Section: Introductionmentioning

confidence: 99%

“…When the viscosity-fluidity changes rapidly with temperature, it may cause slagging and clogging of the gasifier. Slags can be classified into glassy, plastic and crystalline (non-Newtonian) according to the viscosity-temperature curve [1,[5][6][7]. The viscosity of glassy slag decreases with increasing temperature in a gradual manner without abrupt changes.…”

Section: Introductionmentioning

confidence: 99%

“…When the fuel generates a plastic slag, the operation temperature needs to be controlled in a very narrow range to avoid the rapid increase in slag viscosity. In the worst-case scenario, the intense precipitation of solids from the slag may cause unplanned shutdown of molten gasification [1,5,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…The type, viscosity and other properties of slag can be measured at different temperatures, but the experiment is costly and time-consuming. It requires substantial professional knowledge and advanced instruments to obtain accurate data [5][6][7]. Therefore, the development of a universal model to predict slag viscosity would greatly promote the design of gasifiers and the selection of raw materials.…”

Section: Introductionmentioning

confidence: 99%

“…Some chemical components cannot be used as input variables for the model to calculate viscosity [2,3]. Molten slag will be converted into non-Newtonian fluid in the cooling process, which reduces the prediction accuracy of the model [5][6][7]. By comparison, the empirical formula S 2 is available when the silicon percentage is less than 55% and the iron oxide content is less than 5%; the Watt-Fereday formula is more accurate when the silicon percentage is greater than 80% or the iron oxide content is greater than 15% [15].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes

Lin

Dai

et al. 2020

Energies

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Molten gasification is considered as a promising technology for the processing and safe disposal of hazardous wastes. During this process, the organic components are completely converted while the hazardous materials are safely embedded in slag via the fusion-solidification-vitrification transformation. Ideally, the slag should be glassy with low viscosity to ensure the effective immobilization and steady discharge of hazardous materials. However, it is very difficult to predict the characteristics of slag using existing empirical equations or conventional mathematical methods, due to the complex non-linear relationship among the phase transformation, vitrification transition and chemical composition of slag. Equipped with a strong nonlinear mapping ability, an artificial neural network may be able to predict the properties of slags if a large amount of data is available for training. In this work, over 10,000 experimental data points were used to train and develop a slag classification model (glassy vs. non-glassy) based on a neural network. The optimal structure of the neural network was figured out and validated. The results suggest that the classification accuracy for the independent test samples reached 93.3%. Using 1 and 0 as model inputs to represent mildly reducing and inert atmospheres, a double hidden layer structure in the neural network enabled the accurate classification of slags under various atmospheres. Furthermore, the neural network for the prediction of glassy slag viscosity was optimized; it featured a double hidden layer structure. Under a mildly reducing atmosphere, the absolute error from the independent test data was generally within 4 Pa·s. By adding a gas atmosphere into the input of the neural network using a simple normalization method, a multi-atmosphere slag viscosity prediction model was developed. Said model is much more accurate than its counterpart that does not consider the effect of the atmosphere. In summary, the artificial neural network proved to be an effective approach to predicting the slag properties under different atmospheres. The data-driven models developed in this work are expected to facilitate the commercial deployment of molten gasification technology.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes

Lin

Dai

et al. 2020

Energies

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Effects of coal gangue on the ash initial deformation temperature and gasification characteristic of biomass

Fan,

Li,

Guo

et al. 2024

Asia-Pacific J Chem Eng

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The effects of coal gangue (CG) on the ash initial deformation temperature (IDT) and CO2 gasification characteristic of biomass (chestnut shell [CS] and soybean straw [SS]) were investigated by ash fusion tester and thermogravimetric analyzer, respectively. The IDTs of CS and SS ashes were low because of the presence of abundant low melting point (MP) airchildite (K2Ca(CO3)2) and arcanite (K2SO4), and they were efficiently improved by the CG additions of 2%–10% mass ratios. The increases in the IDTs of CS and SS ashes were resulted from the conversions of fairchildite and arcanite into high MP K‐Al silicates (kalsilite [KAlSiO4], leucite [KAlSi2O6], etc.). The gasification reactivities of CS/CG mixtures stayed below that of CS, and they decreased with increasing CG mass ratios (4%–10%) and increased with increasing temperatures (900–1000°C). At 900°C, CG addition caused a decrease in the gasification reactivity of SS. When the temperature reached 950°C, the CG addition of 4% mass ratio increased the gasification reactivity of SS instead. The synergistic effect between biomass and CG during cogasification was more significant at low CG mass ratio and high temperature, and the synergistic effect between SS and CG was more intense than that between CS and CG.

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An investigation of pine needles fluidization, combustion performance, and fly ash behavior in fluidized bed combustor

Sharma

Razak

et al. 2022

J Therm Anal Calorim

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The impact of aluminosilicate-based additives upon the sintering and melting behaviour of biomass ash

Cited by 49 publications

References 17 publications

Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes

Prediction of Slag Characteristics Based on Artificial Neural Network for Molten Gasification of Hazardous Wastes

Effects of coal gangue on the ash initial deformation temperature and gasification characteristic of biomass

An investigation of pine needles fluidization, combustion performance, and fly ash behavior in fluidized bed combustor

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