Artificial neural network modeling of SO<sub>2</sub> absorption into seawater/sodium sulfite solution in a high-gravity assisted atomization reactor with multi-phase intensification internals

Xu Yihang; Zhan Wentian; Xu Hanzhuo; Li Yanbin; Chu Guangwen; Chen Jianfeng

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Artificial neural network modeling of SO₂ absorption into seawater/sodium sulfite solution in a high-gravity assisted atomization reactor with multi-phase intensification internals

Updated：2026-08-24

- Artificial neural network modeling of SO₂ absorption into seawater/sodium sulfite solution in a high-gravity assisted atomization reactor with multi-phase intensification internals
- Artificial neural network modeling of SO₂ absorption into seawater/sodium sulfite solution in a high-gravity assisted atomization reactor with multi-phase intensification internals
- 中国化学工程学报（英文） 2026年94卷第6期页码：301-314
- Affiliations：
  
  State Key Laboratory of Organic-Inorganic Composites, Beijing University of Chemical Technology, Beijing 100029, China
  Research Center of the Ministry of Education for High Gravity Engineering and Technology, Beijing University of Chemical Technology, Beijing 100029, China
  Petrochemical Research Institute, PetroChina, Beijing 102206, China
- Author bio：
  
  Corresponding authors. E-mail addresses: liyb@mail.buct.edu.cn(Y. Li)
  chugw@mail.buct.edu.cn(G. Chu)
- Funds：
  
  the National Natural Science Foundation of China(22288102;22408013);China Postdoctoral Science Foundation(2025M771144);Postdoctoral Fellowship Program of Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20250779)
- DOI：
  中图分类号：
- 收稿：2025-10-27，
  
  修回：2025-12-24，
  
  录用：2025-12-26，
  
  网络首发：2026-02-14，
  
  纸质出版：2026-06
- Accepted：
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Xu Yihang, Zhan Wentian, Xu Hanzhuo, 等. Artificial neural network modeling of SO₂ absorption into seawater/sodium sulfite solution in a high-gravity assisted atomization reactor with multi-phase intensification internals[J]. 中国化学工程学报（英文）, 2026,94(6):301-314.

Xu Yihang, Zhan Wentian, Xu Hanzhuo, et al. Artificial neural network modeling of SO₂ absorption into seawater/sodium sulfite solution in a high-gravity assisted atomization reactor with multi-phase intensification internals[J]. Chinese Journal of Chemical Engineering, 2026, 94(6): 301-314.
Xu Yihang, Zhan Wentian, Xu Hanzhuo, 等. Artificial neural network modeling of SO₂ absorption into seawater/sodium sulfite solution in a high-gravity assisted atomization reactor with multi-phase intensification internals[J]. 中国化学工程学报（英文）, 2026,94(6):301-314. DOI：

Xu Yihang, Zhan Wentian, Xu Hanzhuo, et al. Artificial neural network modeling of SO₂ absorption into seawater/sodium sulfite solution in a high-gravity assisted atomization reactor with multi-phase intensification internals[J]. Chinese Journal of Chemical Engineering, 2026, 94(6): 301-314. DOI：

摘要

Abstract

Rotating packed beds (RPBs)

as classic process intensification reactors

have been adopted for shipboard desulfurization to meet the stringent emission limits. However

conventional RPBs are unsuitable for open-loop seawater scrubbing because the required large liquid throughput necessitates an enlarged rotor diameter

leading to prohibitive power consumption and large footprint. To address this limitation

this work developed a high-gravity assisted atomization reactor (HAAR) with multi-phase intensification internals

in which a downsized rotor was used exclusively for liquid atomization while guide baffles and static wire meshes were integrated as internal structures to improve gas distribution and promote liquid secondary dispersion

respectively. The open- and closed-loop desulfurization performance and energy dissipation (

) of HAAR were systematically evaluated

clarifying the effect of multiphase intensification internals. Experiments under open-loop seawater and closed-loop sodium sulfite systems achieved desulfurization efficiencies of 76.2% to 93.8% and 92.5% to 98.2%

respectively

which meet the emission limits. Internal installation caused marginal increases in

while significantly enhancing the desulfurization process. An artificial neural network (ANN) model was established to quantify parameter importance and evaluate operating strategies

forecasting within ±10% deviation from the experimental data. Compared with conventional reactors

HAAR achieved about 1 order of magnitude higher the over

all gas-phase volumetric mass-transfer coefficient (

) than spray towers with slightly enlarged

and comparable values of

for conventional RPBs with 1-2 orders of magnitude lower

demonstrating the potential for desulfurization applications in marine engineering. This work stimulated advances in marine exhaust gas purification and provided valuable guidance for internal-structure design and data-driven ANN analysis aimed at optimized performance and intelligent operation.

关键词

Keywords

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Artificial neural network modeling of SO2 absorption into seawater/sodium sulfite solution in a high-gravity assisted atomization reactor with multi-phase intensification internals

Artificial neural network modeling of SO2 absorption into seawater/sodium sulfite solution in a high-gravity assisted atomization reactor with multi-phase intensification internals

DOI：

摘要

Abstract

关键词

Keywords

references

Artificial neural network modeling of SO₂ absorption into seawater/sodium sulfite solution in a high-gravity assisted atomization reactor with multi-phase intensification internals

Artificial neural network modeling of SO₂ absorption into seawater/sodium sulfite solution in a high-gravity assisted atomization reactor with multi-phase intensification internals