Development of NOx removal process for LNG evaporation system: Comparative assessment between response surface methodology (RSM) and artificial neural network (ANN)
| Autor: | Geon-Joong Kim, Ziehyun Kim, Sungwon Hwang, Yeonju Shin, Jihye Yu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Flue gas
business.industry General Chemical Engineering Evaporation 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology Combustion Residence time (fluid dynamics) 01 natural sciences 0104 chemical sciences Volumetric flow rate Environmental science Vaporizer Response surface methodology 0210 nano-technology Process engineering business NOx |
| Zdroj: | Journal of Industrial and Engineering Chemistry. 74:136-147 |
| ISSN: | 1226-086X |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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