人工智能辅助沼气重整催化体系筛选与优化研究进展

黄泽涛; 韩冰; 张止戈; 谭涛; 谢君; 陈勇

doi:10.11949/0438-1157.20260578

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人工智能辅助沼气重整催化体系筛选与优化研究进展

综述与专论 | 更新时间：2026-06-10

- 人工智能辅助沼气重整催化体系筛选与优化研究进展
- Research progress on artificial intelligence-assisted screening and optimization of catalysts for biogas reforming
- 化工学报 2026年
- 作者机构：
  
  华南农业大学生物质学院，广东广州 510642
- 作者简介：
  
  黄泽涛（1997—），男，博士研究生，huangzetao@stu.scau.edu.cn
  韩冰（1997—），男，博士研究生，hanbing@stu.scau.edu.cn
  谢君（1965—），男，博士，教授，xiejun@scau.edu.cn
  陈勇（1957—），男，博士，研究员，chen-yong@scau.edu.cn
- 基金信息：
  
  国家重点研发计划项目(2023YFC3905802)
- DOI：10.11949/0438-1157.20260578
  中图分类号： TQ032.4;TQ426
- 收稿：2026-04-22，
  
  修回：2026-06-09，
  
  录用：2026-06-10，
- 稿件说明：
移动端阅览
黄泽涛, 韩冰, 张止戈, 等. 人工智能辅助沼气重整催化体系筛选与优化研究进展[J/OL]. 化工学报, 2026.

HUANG Zetao, HAN Bing, ZHANG Zhige, et al. Research progress on artificial intelligence-assisted screening and optimization of catalysts for biogas reforming[J/OL]. CIESC Journal, 2026.
黄泽涛, 韩冰, 张止戈, 等. 人工智能辅助沼气重整催化体系筛选与优化研究进展[J/OL]. 化工学报, 2026. DOI： 10.11949/0438-1157.20260578.

HUANG Zetao, HAN Bing, ZHANG Zhige, et al. Research progress on artificial intelligence-assisted screening and optimization of catalysts for biogas reforming[J/OL]. CIESC Journal, 2026. DOI： 10.11949/0438-1157.20260578.

摘要

沼气重整可将CH

和CO

转化为H

/CO比可调的合成气，是生物质碳资源高值利用的重要路径。受原料组成波动、杂质扰动、积碳烧结耦合失活及长期稳定性评价不足制约，催化剂设计面临多变量、多目标优化问题。本文从建模需求出发，综述沼气干重整、蒸汽重整与双重整催化体系及失活规律，总结结构化数据构建、描述符选择、模型筛选、工况优化和多目标决策进展，并讨论人工智能与理论计算、原位表征和机理分析协同的路径。未来应明确数据来源与适用边界，建立数据、模型、机理和实验闭环迭代流程。

Abstract

Biogas reforming converts CH

and CO

into syngas with tunable H

/CO ratios

offering a route for upgrading biomass-derived carbon resources. Catalyst design remains challenging because feedstock composition

trace impurities

coke formation

sintering

and long-term stability are strongly coupled. This review summarizes catalyst systems and deactivation features in biogas dry reforming

steam reforming

and bi-reforming from a modeling-oriented perspective. It further discusses structured data construction

descriptor selection

model screening

operating-condition optimization

and multi-objective decision-making

with emphasis on evidence levels that distinguish direct biogas data from transferable studies on related reforming systems. Feasible integration of artificial intelligence with theoretical calculations

in situ characterization

and mechanistic analysis is also examined for rational catalyst design. Future work should define data sources and applicability boundaries clearly and develop workflows linking data

models

mechanisms

and experiments.

关键词

Keywords

references

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