Surrogate model-based optimization of combined separation for urea synthesis feed gas

CAO Yujie; ZHANG Qiao; FENG Xiao; YANG Yang

doi:10.11949/0438-1157.20260444

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Surrogate model-based optimization of combined separation for urea synthesis feed gas

更新时间：2026-06-14

- Surrogate model-based optimization of combined separation for urea synthesis feed gas
- CIESC Journal (2026)
- 作者机构：
  
  西安交通大学化学工程与技术学院，陕西西安 710049
- 作者简介：
- 基金信息：
- DOI：10.11949/0438-1157.20260444
  CLC： TQ028.8
- Received：31 March 2026，
  
  Revised：2026-06-13，
  
  Accepted：14 June 2026，
- 稿件说明：
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CAO Yujie, ZHANG Qiao, FENG Xiao, et al. Surrogate model-based optimization of combined separation for urea synthesis feed gas[J/OL]. CIESC Journal, 2026.
DOI：

CAO Yujie, ZHANG Qiao, FENG Xiao, et al. Surrogate model-based optimization of combined separation for urea synthesis feed gas[J/OL]. CIESC Journal, 2026. DOI： 10.11949/0438-1157.20260444.

摘要

针对工业气体利用过程中资源利用效率低等问题，本文提出一种基于代理模型的多组分工业气体组合分离与优化方法。建立了变压吸附（Pressure Swing Adsorption，PSA）提氢、单乙醇胺（Monoethanolamine，MEA）捕集CO₂及膜分离（Membrane Separation，MEM）过程的代理模型，用于快速预测分离性能并确定最优操作条件。结果显示，各代理模型预测精度均达到R²＞0.96，具有较高可靠性。基于模型优化得到的气体组合分离方案可满足年产20.57万吨尿素生产需求。与传统工艺相比，该方法在设备投资更低的前提下显著提升资源回收效率：氢气回收率由70%–90%提升至约99.90%，CO₂回收率可达92.70%，CH₄回收率提升至92.43%；年碳减排潜力约28047吨。同时，代理模型可在满足工艺约束条件下实现快速寻优，为工业气体分离过程优化提供高效工具。该研究为多组分工业气体的高效低碳利用提供了一种新思路。

Abstract

To address the problems such as low resource utilization efficiency in industrial gas applications

this paper proposes a surrogate model-based integrated separation and optimization method for multicomponent industrial gases. Surrogate models are developed for pressure swing adsorption (PSA) hydrogen purification

monoethanolamine(MEA)-based CO

capture

and membrane separation processes to rapidly predict separation performance and determine optimal operating conditions. The results show that all surrogate models achieve a prediction accuracy of R²

0.96

demonstrating high reliability. The gas integrated separation scheme obtained via model optimization can meet the production demand of 2.057×10⁶ t of urea per year. Compared with the conventional process

the proposed method significantly improves resource recovery efficiency with lower equipment investment: the hydrogen recovery rate is increased from 70%–90% to approximately 99.90%

the CO₂ recovery rate reaches 92.70%

and the CH₄ recovery rate is raised to 92.43%

with an annua

l carbon emission reduction potential of approximately 28

047 t CO₂. Meanwhile

the surrogate model enables rapid optimization under process constraints

providing an efficient tool for the optimization of industrial gas separation processes. This study offers a novel approach for the efficient and low-carbon utilization of multicomponent industrial gases.

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