Effects of coal properties on the production rate of combustion solid residue

Durgun, D.; Genç, Ayten

doi:10.1016/j.energy.2009.08.005

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…(Tables 3 and 4). The coefficients of determination obtained in this study with ANN modeling approach for testing sets of Data Group I (R 2 ¼ 0.984) and Data Group II (R 2 ¼ 0.976) were higher than the value reported in the literature (R 2 ¼ 0.76) for the regression analysis using the same data [19]. With a limited number of experimental results (Total of 12 cases were divided into two groups for training (11 cases) and testing (one case)), Zhou et al [7] have modeled the level of unburned carbon in fly ash (i.e., carbon burnout characteristics) of a 600 MW pulverized coal-fired boiler using ANN technique.…”

Section: Analysis Of the Accuracy Of Ann Modelcontrasting

confidence: 41%

“…The effects of the other input parameters are considerably lower compared to ash content, and the moisture content being the least effective on the formation of bottom ash. Previously, the correlation between the ash content of the coal and the bottom ash produced could not be obtained by regression analysis in the study of Durgun and Genc [19]. In contrast, a higher R 2 value between the (Bottom ash/Coal burned) was shown that the formation of bottom ash was mainly dependent on the ash content of the source coals, among the parameters investigated using ANN modeling approach.…”

Section: Sensitivity Analysismentioning

confidence: 99%

“…The proximate and calorific value analyses of coals were performed according to ASTM D5142 [23] and ASTM D5865 [24] standards, respectively. The details of the performed analyses and the equipments used were given elsewhere [19,25]. The major and trace element concentrations in feed coals of the CATES power plant were reported in [26].…”

Section: Coal Propertiesmentioning

confidence: 99%

“…The amount of bottom ash produced from the combustion of coal is mainly influenced by the properties of coal, coal milling, and combustion conditions. Durgun and Genc [19] applied regression models to study effects of coal properties on the production rate of combustion solid residue in a pulverized coal-fired power plant. They investigated the dependency of the production rate of bottom ash on the fixed carbon, moisture and ash contents of coals, but they could not observe any correlations between the coal type and the amount of bottom ash produced.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks

Bekat

Erdogan²,

İnal³

et al. 2012

Energy

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a b s t r a c tThe amount of bottom ash formed in a pulverized coal-fired power plant was predicted by artificial neural network modeling using one-year operating data of the plant and the properties of the coals processed. The model output was defined as the ratio of amount of bottom ash produced to amount of coal burned (Bottom ash/Coal burned). The input parameters were the moisture contents, ash contents and lower heating values of the coals. The total 653 data were divided into two groups for the training (90% of the data) and the testing (10% of the data) of the network. A three-layer, feed-forward type network architecture with back-propagation learning was used in the modeling study. The activation function was sigmoid function. The best prediction performance was obtained for a one hidden layer network with 29 neurons. The learning rate and the tolerance value were 0.2 and 0.05, respectively. R 2 (coefficient of determination) values between the actual (Bottom ash/Coal burned) ratios and the model predictions were 0.988 for the training set and 0.984 for the testing set. In addition, the sensitivity analysis indicated that the ash content of coals was the most effective parameter for the prediction of the ratio of bottom ash to coal burned.

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Section: Analysis Of the Accuracy Of Ann Modelcontrasting

confidence: 41%

Section: Sensitivity Analysismentioning

confidence: 99%

Section: Coal Propertiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of the bottom ash formed in a coal-fired power plant using artificial neural networks

Bekat

Erdogan²,

İnal³

et al. 2012

Energy

View full text Add to dashboard Cite

show abstract

“…Coals usually contain moisture with a content that varies with the type and source of the coal [37,38]. The moisture in this work was simulated by 15 vol% steam addition in the carbonation environment, which is at the level of typical concentrations considered in other studies dealing with the steam effect on CO 2 capture by CaO-based sorbents [40,41].…”

Section: Effect Of Steammentioning

confidence: 99%