人工智能在过程工业绿色制造中的机遇与挑战

Shuai Mao, Bing Wang, Bing Tang, Qian Feng

工程(英文) ›› 2019, Vol. 5 ›› Issue (6) : 995-1002.

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工程(英文) ›› 2019, Vol. 5 ›› Issue (6) : 995-1002. DOI: 10.1016/j.eng.2019.08.013
研究论文
RESEARCH ARTICLE

人工智能在过程工业绿色制造中的机遇与挑战

作者信息 +

Opportunities and Challenges of Artificial Intelligence for Green Manufacturing in the Process Industry

Author information +
History +

摘要

智能制造是提高过程工业质量的关键。在智能制造中,有这样一种趋势:将各种新一代信息技术融合到过程安全分析中。目前,由于危险化学品的大量使用,绿色制造面临着安全管理方面的重大障碍,从而导致化工过程空间的不均匀化以及安全环保法规的日益严格化。新兴的信息技术,如人工智能(AI),作为克服这些困难的一种手段,是很有前景的。基于最先进的人工智能方法和过程工业中复杂的安全关系,我们识别并讨论了与过程安全相关的几个技术挑战:用过程安全的稀缺标签进行知识获取;基于知识的过程安全推理;不同来源异构数据的精确融合;以及动态风险评估和辅助决策的有效学习。在此背景下,本文还讨论了当前和未来的工作。

Abstract

Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process-safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging information technologies such as artificial intelligence (AI) are quite promising as a means of overcoming these difficulties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety; ② knowledge-based reasoning for process safety; ③ accurate fusion of heterogeneous data from various sources; and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context.

关键词

过程工业 / 智能制造 / 绿色制造 / 人工智能

Keywords

Process industry / Smart manufacturing / Green manufacturing / Artificial intelligence

引用本文

导出引用
Shuai Mao, Bing Wang, Bing Tang. 人工智能在过程工业绿色制造中的机遇与挑战. Engineering. 2019, 5(6): 995-1002 https://doi.org/10.1016/j.eng.2019.08.013

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