Coal blend combustion: fusibility ranking from mineral matter composition☆

Goñi, Ch.; Helle, Sonia; Garcı́a, Ximena; Gordon, Alfredo L.; Parra, Roberto; Kelm, Ursula; Jimenez, R A; Alfaro, Guillermo

doi:10.1016/s0016-2361(03)00156-x

Cited by 29 publications

(8 citation statements)

References 9 publications

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“…It has been used to predict the formation of solid phases in basic oxygen furnace slag [50], estimate coal ash fusion temperatures [121], model the effect of solid phase formation on viscosity in coal ash slags [52,85,111,112,114,[122][123][124] and petroleum coke-coal blend slags [125], and assess the effect of additives on coal ash slag viscosity [15,126]. MTDATA is another CALPHAD-based software tool that has been used to estimate coal fusibility [127] and ash fusion temperatures [128].…”

Section: Thermophysical Modeling Of Coal Ash Slagsmentioning

confidence: 99%

Correlation between the critical viscosity and ash fusion temperatures of coal gasifier ashes

Hsieh

Kwong

Bennett

2016

Fuel Processing Technology

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Coal gasification yields synthesis gas, an important intermediate in chemical manufacturing. It is also vital to the production of liquid fuels through the Fischer-Tropsch process and electricity in Integrated Gasification Combined Cycle power generation. Minerals naturally present in coal become molten in entrained-flow slagging gasifiers. Molten coal ash slag penetrates and dissolves refractory bricks, leading to costly plant shutdowns. The extent of coal ash slag penetration and refractory brick dissolution depends on the slag viscosity, the gasification temperature, and the composition of slag and bricks. We measured the viscosity of several synthetic coal ash slags with a high-temperature rotary viscometer and their ash fusion temperatures through optical image analysis. All measurements were made in a carbon monoxide-carbon dioxide reducing atmosphere that approximates coal gasification conditions. Empirical correlation models based on ash fusion temperatures were used to calculate critical viscosity temperatures based on the coal ash compositions. These values were then compared with those obtained from thermodynamic phase-transition models. An understanding of slag viscosity as a function of ash composition is important to reducing refractory wear in slagging coal gasifiers, which would help to reduce the cost and environmental impact of coal for chemical and electricity production.Published by Elsevier B.V.

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Section: Thermophysical Modeling Of Coal Ash Slagsmentioning

confidence: 99%

Correlation between the critical viscosity and ash fusion temperatures of coal gasifier ashes

Hsieh

Kwong

Bennett

2016

Fuel Processing Technology

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“…An AFTs measurement is generally expensive, time consuming and hard to repeat because it could generate an error during laboratory analysis [10,11]. Moreover, the ash fusion laboratory test has been questioned as it is more like a quantitative observation [12]. Despite the shortcomings, AFTs are still widely used to assess the deposition characteristics of coal.…”

Section: Standard:iso Pn-iso 540:2001 Descriptionmentioning

confidence: 99%

An Interpretable Extreme Gradient Boosting Model to Predict Ash Fusion Temperatures

2020

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The hemispherical temperature (HT) is the most important indicator representing ash fusion temperatures (AFTs) in the Polish industry to assess the suitability of coal for combustion as well as gasification purposes. It is important, for safe operation and energy saving, to know or to be able to predict value of this parameter. In this study a non-linear model predicting the HT value, based on ash oxides content for 360 coal samples from the Upper Silesian Coal Basin, was developed. The proposed model was established using the machine learning method—extreme gradient boosting (XGBoost) regressor. An important feature of models based on the XGBoost algorithm is the ability to determine the impact of individual input parameters on the predicted value using the feature importance (FI) technique. This method allowed the determination of ash oxides having the greatest impact on the projected HT. Then, the partial dependence plots (PDP) technique was used to visualize the effect of individual oxides on the predicted value. The results indicate that proposed model could estimate value of HT with high accuracy. The coefficient of determination (R2) of the prediction has reached satisfactory value of 0.88.

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“…Since the composition of the ash influences the AFT magnitudes and, thereby, performance of a coal-based process, it is essential to establish quantitative relationships between the ash composition and the corresponding four AFTs. The conventional methods are unable to accurately predict the high-temperature behaviour of the coal ash, slag, and blends in the combustion and gasification technologies (Goni et al 2003;Gray 1987;Huggins et al 1981;Lloyd et al 1995;Yin et al 1998;Wall et al 1998). Thus, several studies on the prediction of the AFTs have been conducted using a variety of methods such as thermodynamic, statistical, empirical, and more recently artificial intelligence based data-driven modeling techniques.…”

Section: Models For Predicting Aftmentioning

confidence: 99%

Prediction of coal ash fusion temperatures using computational intelligence based models

Tambe

Naniwadekar

Tiwary

et al. 2018

Int J Coal Sci Technol

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In the coal-based combustion and gasification processes, the mineral matter contained in the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly, ash deposits are formed on the heat absorbing surfaces of the exposed equipment of the combustion/gasification processes. These deposits lead to the occurrence of slagging or fouling and, consequently, reduced process efficiency. The ash fusion temperatures (AFTs) signify the temperature range over which the ash deposits are formed on the heat absorbing surfaces of the process equipment. Thus, for designing and operating the coal-based processes, it is important to have mathematical models predicting accurately the four types of AFTs namely initial deformation temperature, softening temperature, hemispherical temperature, and flow temperature. Several linear/nonlinear models with varying prediction accuracies and complexities are available for the AFT prediction. Their principal drawback is their applicability to the coals originating from a limited number of geographical regions. Accordingly, this study presents computational intelligence (CI) based nonlinear models to predict the four AFTs using the oxide composition of the coal ash as the model input. The CI methods used in the modeling are genetic programming (GP), artificial neural networks, and support vector regression. The notable features of this study are that the models with a better AFT prediction and generalization performance, a wider application potential, and reduced complexity, have been developed. Among the CI-based models, GP and MLP based models have yielded overall improved performance in predicting all four AFTs. Int J Coal Sci Technol (2018) 5(4):486-507 https://doi.org/10.1007/s40789-018-0213-6Prediction of coal ash fusion temperatures using computational intelligence based models 487

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Coal blend combustion: fusibility ranking from mineral matter composition☆

Cited by 29 publications

References 9 publications

Correlation between the critical viscosity and ash fusion temperatures of coal gasifier ashes

Correlation between the critical viscosity and ash fusion temperatures of coal gasifier ashes

An Interpretable Extreme Gradient Boosting Model to Predict Ash Fusion Temperatures

Prediction of coal ash fusion temperatures using computational intelligence based models

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