A deep insight into the dynamic crystallization of coal slags and the correlation with melt microstructure

Xuan, Weiwei; Yan, Shiying; Zhang, Jingkun; Luo, Sheng; Wang, Qi; Zhang, Jiansheng

doi:10.1016/j.fuproc.2023.108005

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…It has a large single furnace capacity, high carbon conversion rate, high effective gas content, and good coal adaptability. Liquid slagging is adopted in entrained-flow gasifiers and discharging slag smoothly from the gasifier is crucial to ensure long-term stable operation of the gasification unit. , In this case, The operation temperature of the furnace should be controlled 50–100 K higher than the flow temperature (FT) of coal ash. Also, the operation temperature cannot be too high, which will lead to an increase of oxygen and coal consumption, a reduction of active components of syngas, and an acceleration of the erosion of refractory bricks.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Coal Ash Flow Temperature Based on Gray Relational Analysis, Support Vector Regression and Genetic Algorithm

Sun,

Liu,

et al. 2024

ACS Omega

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Coal ash flow temperature significantly influences the operating conditions of entrained flow bed gasification. The relationship between the flow temperature of coal ash and its chemical composition remains uncertain despite being determined by it. To construct a reliable and accurate predictive method, machine learning models were used, and different support vector regression models were built to predict the flow temperature. The prediction results of the proposed gray relational analysis−genetic algorithm−support vector regression model can achieve high accuracy, with a root-mean-square error of 28.37, mean absolute error of 19.48K, and average deviation of 1.58%. Moreover, the prediction results of the proposed model are more accurate and efficient than those calculated by FactSage software, with a mean absolute error of 93.73K. This demonstrates the viability of applying the proposed machine learning model to predict the flow temperature of coal ash and its promising potential application in the area of coal chemical engineering.

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Section: Introductionmentioning

confidence: 99%

Prediction of Coal Ash Flow Temperature Based on Gray Relational Analysis, Support Vector Regression and Genetic Algorithm

Sun,

Liu,

et al. 2024

ACS Omega

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Illustrating the effect of CaO and MgO charge compensation capacity on the fluidity of SiO2-Al2O3-CaO-MgO slag in industrial processes: Insights from molecular dynamics simulations

Zhang,

Zhao,

et al. 2024

Journal of Molecular Liquids

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Unraveling the atomic-scale mechanism of interfacial alkali ion close packing in nano glassy fibers driven by CO₂-mediated attraction

Wei,

Chen,

Sun

et al. 2024

Nanoscale

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A deep insight into the dynamic crystallization of coal slags and the correlation with melt microstructure

Cited by 3 publications

References 47 publications

Prediction of Coal Ash Flow Temperature Based on Gray Relational Analysis, Support Vector Regression and Genetic Algorithm

Prediction of Coal Ash Flow Temperature Based on Gray Relational Analysis, Support Vector Regression and Genetic Algorithm

Illustrating the effect of CaO and MgO charge compensation capacity on the fluidity of SiO2-Al2O3-CaO-MgO slag in industrial processes: Insights from molecular dynamics simulations

Unraveling the atomic-scale mechanism of interfacial alkali ion close packing in nano glassy fibers driven by CO₂-mediated attraction

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A deep insight into the dynamic crystallization of coal slags and the correlation with melt microstructure

Cited by 3 publications

References 47 publications

Prediction of Coal Ash Flow Temperature Based on Gray Relational Analysis, Support Vector Regression and Genetic Algorithm

Prediction of Coal Ash Flow Temperature Based on Gray Relational Analysis, Support Vector Regression and Genetic Algorithm

Illustrating the effect of CaO and MgO charge compensation capacity on the fluidity of SiO2-Al2O3-CaO-MgO slag in industrial processes: Insights from molecular dynamics simulations

Unraveling the atomic-scale mechanism of interfacial alkali ion close packing in nano glassy fibers driven by CO2-mediated attraction

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Product

Resources

About

Unraveling the atomic-scale mechanism of interfacial alkali ion close packing in nano glassy fibers driven by CO₂-mediated attraction