A review of artificial intelligence of things technologies and applications in the chemical industry

Zhang Wei; Ji Yating; Zhao Liqiang; Wang Kunfeng

doi:10.11959/j.issn.2096-6652.202604

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A review of artificial intelligence of things technologies and applications in the chemical industry

Surveys and Prospectives | 更新时间：2026-04-27

- A review of artificial intelligence of things technologies and applications in the chemical industry
- Chinese Journal of Intelligent Science and Technology Vol. 8, Issue 1, Pages: 12-32(2026)
- 作者机构：
  
  1.北京化工大学信息科学与技术学院，北京 100029
  2.北京化工大学化学工程学院，北京 100029
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(U22A2061);Beijing National Natural Science Foundation(L241017)
- DOI：10.11959/j.issn.2096-6652.202604
  CLC： TP39
- Received：13 December 2025，
  
  Revised：2026-03-14，
  
  Accepted：17 March 2026，
  
  Published：15 March 2026
- 稿件说明：
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张伟,纪雅婷,赵利强等.化工行业人工智能物联网技术及应用研究综述[J].智能科学与技术学报,2026,08(01):12-32.

Zhang Wei,Ji Yating,Zhao Liqiang,et al.A review of artificial intelligence of things technologies and applications in the chemical industry[J].Chinese Journal of Intelligent Science and Technology,2026,08(01):12-32.
张伟,纪雅婷,赵利强等.化工行业人工智能物联网技术及应用研究综述[J].智能科学与技术学报,2026,08(01):12-32. DOI： 10.11959/j.issn.2096-6652.202604.

Zhang Wei,Ji Yating,Zhao Liqiang,et al.A review of artificial intelligence of things technologies and applications in the chemical industry[J].Chinese Journal of Intelligent Science and Technology,2026,08(01):12-32. DOI： 10.11959/j.issn.2096-6652.202604.

摘要

化工行业人工智能物联网技术在在线监测与智能巡检、设备管理与预测性维护等方面发挥着关键作用，是推动工业5

0进程的重要技术支撑。其中，人工智能作为核心驱动力，通过赋能物联网系统具备数据智能感知、异常检测、预测性维护、智能控制与自主决策能力，显著提升了整体系统的智能化水平、响应能力与运行效率。从人工智能物联网的系统架构出发，系统梳理了感知

层数据采集、网络层数据传输、边缘层与平台层数据处理、执行层控制与执行等关键技术的发展现状；然后结合典型化工应用案例，分析了人工智能物联网在实际生产环境中的部署成效与技术路径；最后，总结该领域当前面临的挑战，并展望未来发展趋势。

Abstract

Artificial intelligence of things (AIoT) has become an important enabling technology for the chemical industry

especially in online monitoring and intelligent inspection

equipment management

and predictive maintenance

providing strong support for the transition toward Industry 5.0. From the perspective of AIoT system architecture

the recent progress of key technologies was reviewed

including data acquisition at the perception layer

data transmission at the network layer

data processing at the edge and platform layers

and control and actuation at the execution layer. Typical application scenarios in the chemical industry were further analyzed to summarize the deployment effectiveness and technical pathways of AIoT in real production environments. The analysis indicates that AIoT can significantly enhance state perception

anomaly identification

predictive analysis

and collaborative control under complex operating conditions. However

challenges remain in heterogeneous data management

model generalization and interpretability

secure communication

and large-scale engineering deployment. Future research should focus on cloud-edge-end collaborative intelligence

lightweight and adaptive deployment

and physics-data hybrid modeling for safety-oriented closed-loop optimization.

关键词

Keywords

references

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