Study on prediction models of biomass ash softening temperature based on ash composition

Yu, Lilei; Wang, L.W.; Li, P.S.

doi:10.1016/j.joei.2014.03.011

Cited by 43 publications

(22 citation statements)

References 5 publications

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“…In comparison with the results predicted by using the empirical formula in ref. [19] also shown in Table 4, their results appeared to be inferior to our results, based on absolute and relative errors obtained. The prediction models proposed in this paper seemed to have high accuracy.…”

Section: Resultscontrasting

confidence: 97%

“…Ash fusibility index (TAFI) may be predicted based on the content of oxides in ash [19]. This is calculated from Eq.…”

Section: Modeling Ash Fusibility Indexmentioning

confidence: 99%

“…These ash deposition problems are influenced by ash fusibility which, in turns, associated with ash composition [11][12][13][14][15][16]. So far, there have been a number of works on biomass related ash slagging and deposition tendency [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28], but, there is no directly available tool that predicts ash slagging from biomass. Usually, ash fusibility correlations from coal research are used instead.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, correlations between ash fusion characteristics and ash mineral composition found in coal research may be applicable and equally valid to biomass. Several research studies were conducted for the relationship between biomass ash fusibility and its ash composition [18][19][20][21][22][23][24]26]. Only a handful of these works were directly about slagging indices [19,20,[22][23][24]26].…”

Section: Introductionmentioning

confidence: 99%

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Predicting Ash Deposit Tendency in Thermal Utilization of Biomass

Pintana

Tippayawong

2016

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Abstract. In thermochemical utilization of biomass, ash produced during the process is a major problem that can result in decreased performance and increased in difficulty during operation. Preliminary assessment of potential ash related troubles prior to the use of a specific biomass is valuable, even if it is only a general guideline. In this work, tendency of ash slagging, fouling and agglomeration in thermal processing of biomass was evaluated. Reference peered reviewed data including mineral content and fusion temperature of selected biomass ash were used to calculate multiple indicators (base to acid ratio, slagging index, fouling index, agglomeration index, slag viscosity index, and ternary diagram of main biomass ash composition) adopted from coal research. Major ash forming elements (Fe, Ca, Mg, K, Na, Al, Si) were found to be of relevance to ash melting and deposit behavior. For conventional biomass available locally, woody biomass (wood and wood sawdust) may be combusted without slagging or fouling problem, while non-woody biomasses (bark, husk, straw) are highly probable to experience some of these problems. The ash fusibility predictive models for woody and non-woody biomass were found to be effective. Mitigation can then be designed possibly via fuel blending to avoid or minimize the impact of biomass ash related trouble.

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Section: Resultscontrasting

confidence: 97%

“…Ash fusibility index (TAFI) may be predicted based on the content of oxides in ash [19]. This is calculated from Eq.…”

Section: Modeling Ash Fusibility Indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Ash Deposit Tendency in Thermal Utilization of Biomass

Pintana

Tippayawong

2016

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“…However, this fuel ash also contained the significant amounts of calcium and magnesium, which was reported to increase the ash and ash/bed melting temperatures and to reduce the risk of bed agglomeration problem [18,19]. Silica sand (SiO 2 ¼ 99.5 wt%) with solid density z2500 kg/m 3 and particle size 0.3e0.5 mm was used as bed material at the static bed height of 30 cm and to avoid the bed agglomeration, it was replaced every 18 h of use.…”

Section: Fuel and Bed Characteristicsmentioning

confidence: 99%