화학공학소재연구정보센터
홈 로그인 로그아웃 연락처 사이트맵
Journal of Industrial and Engineering Chemistry, Vol.74, 136-147, June, 2019
DOI10.1016/j.jiec.2019.02.020  Export Citation
Development of NOx removal process for LNG evaporation system: Comparative assessment between response surface methodology (RSM) and artificial neural network (ANN)
Ziehyun Kim, Yeonju Shin,, Jihye Yu, Geonjoong Kim, and Sungwon Hwang†
  • Graduate School of Chemistry and Chemical Engineering, Inha University, 100 Inha-ro, Michuhol-gu, Incheon 22212, South Korea
E-mail:
In this work, response surface methodology and artificial neural network were adopted to build a model of a NOx removal system in a LNG terminal that estimates the released amounts of NOx in flue gas from a submerged combustion vaporizer. A small-scale SCV setting was prepared for the experiment, and it was operated under various conditions (i.e., changes of residence time, flow rate of the air, water temperature, and pH of the water). The simulation results demonstrated good agreement between both models and the experimental data, although the ANN model showed a higher accuracy than that of the RSM model.
Keywords:Response surface methodology (RSM);Artificial neural network (ANN);NOx reduction;Submerged combustion vaporizer (SCV);Modeling;Optimization
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