Correlation between ash fusion temperatures and chemical composition in Albanian coal ashes☆

Lolja, Saimir A.; Haxhi, Hajri; Dhimitri, Rolanda; Drushku, Spiro; Malja, Agim

doi:10.1016/s0016-2361(02)00194-1

Cited by 58 publications

(16 citation statements)

References 11 publications

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“…Commonly used analytical techniques include testing the fusion characteristics of ash, measurements of ash viscosity and estimation of indices based on ratios of elemental oxides [8][9][10]. Despite the limitations associated with these techniques, they are still widely used to assess the tendency of various coals to slag and foul boiler heat transfer surfaces and have also been extended to biomass fuels for evaluation of the same [11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Prediction of biomass ash fusion behaviour by the use of detailed characterisation methods coupled with thermodynamic analysis

Rizvi

Xing

Pourkashanian

et al. 2015

Fuel

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ReuseUnless indicated otherwise, fulltext items are protected by copyright with all rights reserved. The copyright exception in section 29 of the Copyright, Designs and Patents Act 1988 allows the making of a single copy solely for the purpose of non-commercial research or private study within the limits of fair dealing. The publisher or other rights-holder may allow further reproduction and re-use of this version -refer to the White Rose Research Online record for this item. Where records identify the publisher as the copyright holder, users can verify any specific terms of use on the publisher's website. TakedownIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing eprints@whiterose.ac.uk including the URL of the record and the reason for the withdrawal request. AbstractAsh deposition such as slagging and fouling on boiler tube surfaces is an inevitable, though undesirable consequence of burning solid fuels in boilers. The role of fuel characteristics, in affecting the form and severity of the problem, is significant. In recent years, biomass fuels have gained increasing popularity as an environmentally friendly source of energy in power plants all over the world. This study is based on characterising the fusion behaviour of four biomass fuels (pine wood, peanut shells, sunflower stalk and miscanthus) using ash fusion temperature (AFT) tests, simultaneous thermal analysis (STA) of fuel ashes, calculation of empirical indices and predicting ash melting behaviour with the help of thermodynamic equilibrium calculations. The AFT results failed to show any clear trend between fusion temperature and high alkali content of biomass. STA proved useful in predicting the different changes occurring in the ash. Empirical indices predicted high slagging and fouling hazards for nearly all the biomass samples and this was supported by the possible existence of a melt phase at low temperatures as predicted by thermodynamic calculations.

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Section: Introductionmentioning

confidence: 99%

Prediction of biomass ash fusion behaviour by the use of detailed characterisation methods coupled with thermodynamic analysis

Rizvi

Xing

Pourkashanian

et al. 2015

Fuel

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“…It may be noted that the stated models simulate the ash formation under controlled conditions and, therefore, do not portray closely the conditions in the real combustors/gasifiers. The AFT models, however, compensate for the said deficiency by providing predictions under a consistent set of test conditions (Lolja et al 2002). The AFT predicting models have following applications: (a) based upon the chemical and mineral composition of the coal ashes, the models offer a method to calculate coal ashes' thermal properties (Seggiani 1999), and (b) provide a method to evaluate the outcome of an addition of minerals (such as CaO) for modifying the slag behaviour (Wall et al 1998).…”

Section: Models For Predicting Aftmentioning

confidence: 99%

“…( The regression studies, such as those by Lolja et al (2002), Ozbayoglu and Ozbayoglu (2006), and Seggiani (1999), have reported that there exists a linear dependence between the mineral composition of the coal ashes and their AFTs. On the other hand, a number of studies (e.g.…”

Section: Experimental Data and Need For Nonlinear Aft Modelsmentioning

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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“…Vassilev SV et al [8] reported that iron oxide produces ash melting and fouling during coal combustion. Lolja SA et al [11] found that the contribution of coal oxides to AFTs was stronger than that of their mineral combinations. Song W et al [12] used the SiO 2 -Al 2 O 3 -FeO-CaO system to study ash fusibility of a representative Chinese coal.…”

Section: Introductionmentioning

confidence: 99%

“…Several works [7][8][9][10][11][12][13][14][15] have shown that the chemical and mineral composition of coals determine their melting characteristics as well as their AFTs. Vassilev SV et al [8] reported that iron oxide produces ash melting and fouling during coal combustion.…”

Section: Introductionmentioning

confidence: 99%

Effect of sludge addition to coal on Na, K and S volatilisation in ashing process and ash fusibility

Folgueras

Alonso

Folgueras

2015

Fuel Processing Technology

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Correlation between ash fusion temperatures and chemical composition in Albanian coal ashes☆

Cited by 58 publications

References 11 publications

Prediction of biomass ash fusion behaviour by the use of detailed characterisation methods coupled with thermodynamic analysis

Prediction of biomass ash fusion behaviour by the use of detailed characterisation methods coupled with thermodynamic analysis

Prediction of coal ash fusion temperatures using computational intelligence based models

Effect of sludge addition to coal on Na, K and S volatilisation in ashing process and ash fusibility

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