Investigation on classification efficiency for coal-fired power plant classifiers using a numerical approach
Incomplete combustion in boilers often leads to a significant presence of unburnt carbon found in the ash and pollutant emissions. A key factor to overcome this problem is to increase the quality of classification via achieving a greater particle separation quality where at least 70% of the coal par...
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my.uniten.dspace-254532023-05-29T16:09:35Z Investigation on classification efficiency for coal-fired power plant classifiers using a numerical approach Ismail F.B. Al-Muhsen N.F.O. Lingam R. 58027086700 57197748656 57218280461 Incomplete combustion in boilers often leads to a significant presence of unburnt carbon found in the ash and pollutant emissions. A key factor to overcome this problem is to increase the quality of classification via achieving a greater particle separation quality where at least 70% of the coal particles exiting the classifier are smaller than 75 ?m. Three dimensional (3-D) computational fluid dynamics modelling was used to investigate the effect of the steepness of the classifier blade angle on the classification efficiency in Coal-Fired power plants. The gas flow inside the coal mill was solved by the realizable k-? turbulence model (RKE) with a detailed 3-D classifier geometry meanwhile the discrete phase model was used to solve the coal particles flow. The steepest classifier blade angle of 40� achieved the highest quality of classification where 61.70% of the coal particles are less than 75 ?m. Meanwhile, the classification efficiency dipped to 93.0%. An increase in quality of classification leads to a decrease in classification efficiency. � School of Engineering, Taylor's University. Final 2023-05-29T08:09:35Z 2023-05-29T08:09:35Z 2020 Article 2-s2.0-85088564878 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85088564878&partnerID=40&md5=e030f72db6c7cb25d030a11b995c9736 https://irepository.uniten.edu.my/handle/123456789/25453 15 3 1542 1561 Taylor's University Scopus |
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Incomplete combustion in boilers often leads to a significant presence of unburnt carbon found in the ash and pollutant emissions. A key factor to overcome this problem is to increase the quality of classification via achieving a greater particle separation quality where at least 70% of the coal particles exiting the classifier are smaller than 75 ?m. Three dimensional (3-D) computational fluid dynamics modelling was used to investigate the effect of the steepness of the classifier blade angle on the classification efficiency in Coal-Fired power plants. The gas flow inside the coal mill was solved by the realizable k-? turbulence model (RKE) with a detailed 3-D classifier geometry meanwhile the discrete phase model was used to solve the coal particles flow. The steepest classifier blade angle of 40� achieved the highest quality of classification where 61.70% of the coal particles are less than 75 ?m. Meanwhile, the classification efficiency dipped to 93.0%. An increase in quality of classification leads to a decrease in classification efficiency. � School of Engineering, Taylor's University. |
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58027086700 |
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58027086700 Ismail F.B. Al-Muhsen N.F.O. Lingam R. |
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Article |
| author |
Ismail F.B. Al-Muhsen N.F.O. Lingam R. |
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Ismail F.B. Al-Muhsen N.F.O. Lingam R. Investigation on classification efficiency for coal-fired power plant classifiers using a numerical approach |
| author_sort |
Ismail F.B. |
| title |
Investigation on classification efficiency for coal-fired power plant classifiers using a numerical approach |
| title_short |
Investigation on classification efficiency for coal-fired power plant classifiers using a numerical approach |
| title_full |
Investigation on classification efficiency for coal-fired power plant classifiers using a numerical approach |
| title_fullStr |
Investigation on classification efficiency for coal-fired power plant classifiers using a numerical approach |
| title_full_unstemmed |
Investigation on classification efficiency for coal-fired power plant classifiers using a numerical approach |
| title_sort |
investigation on classification efficiency for coal-fired power plant classifiers using a numerical approach |
| publisher |
Taylor's University |
| publishDate |
2023 |
| _version_ |
1806424176513253376 |
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13.222552 |