Investigation on classification efficiency for coal-fired power plant classifiers using a numerical approach

• Main Authors: Ismail F.B., Al-Muhsen N.F.O., Lingam R.
• Other Authors: 58027086700
• Format: Article
• Published: Taylor's University 2023
• Institution: Universiti Tenaga Nasional

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.