Numerical Experimentation on Optimization of Ammonia Injection in the Denitrification Process 


Vol. 30,  No. 4, pp. 357-364, Dec.  2024
10.7464/ksct.2024.30.4.357


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  Abstract

In denitrification systems that use the selective catalytic reduction method, the flow rate of the ammonia injection nozzles is controlled by measuring the molar composition ratio of ammonia and nitrogen oxides at the inlet of the catalyst layer. In this study, the effect of the number of data measuring points on the optimization of the molar ratio for the reduction process inside the catalytic layers was analyzed by computational analysis. The working fluid is composed of air, ammonia and nitric oxide. The flow is assumed to be incompressible. The flow fields were solved using the k – e turbulence model in a commercial software named ANSYS-Fluent, which is widely used in thermal flow field analysis. Based on the experimental design, DesignXplorer was used to optimize the NH3/NO molar ratio. Two types of inflow gases were selected, upward skewed inlet flow and double parabolic inlet flow. The root mean square of the NH3/NO molar ratio at the inlet of the catalyst layer was chosen as the optimization parameter. According to the numerical analysis results, when the number of measuring points increased, the value of the parameter decreased and converged to a finite value when there were about ten (10) measuring points. The effect of improving the performance by controlling the injection of ammonia was 80.7% in the case of upward skewed inlet flow and 77.5% in the case of double parabolic inlet flow.

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  Cite this article

[IEEE Style]

M. Kim, Y. Koh, H. Chung, "Numerical Experimentation on Optimization of Ammonia Injection in the Denitrification Process," Clean Technology, vol. 30, no. 4, pp. 357-364, 2024. DOI: 10.7464/ksct.2024.30.4.357.

[ACM Style]

Min-Kyu Kim, Yeong-Il Koh, and Hee-Taeg Chung. 2024. Numerical Experimentation on Optimization of Ammonia Injection in the Denitrification Process. Clean Technology, 30, 4, (2024), 357-364. DOI: 10.7464/ksct.2024.30.4.357.