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《工程(英文)》 >> 2019年 第5卷 第6期 doi: 10.1016/j.eng.2019.08.013

人工智能在过程工业绿色制造中的机遇与挑战

Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

收稿日期: 2019-01-03 修回日期: 2019-08-06 录用日期: 2019-08-22 发布日期: 2019-11-02

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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。

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参考文献

[ 1 ] Qian F, Zhong W, Du W. Fundamental theories and key technologies for smart and optimal manufacturing in the process industry. Engineering 2017;3 (2):154–60. 链接1

[ 2 ] Giffi CA, Rodriguez MD, Gangula B, Roth AV, Hanley T. Global manufacturing competitiveness index. London: Deloitte Touche Tohmatsu Limited Global Consumer & Industrial Products Industry Group and the Council on Competitiveness; 2016.

[ 3 ] Williams E. Environmental effects of information and communications technologies. Nature 2011;479(7373):354–8. 链接1

[ 4 ] Smart Manufacturing Leadership Coalition. Implementing 21st century smart manufacturing: workshop summary report. Washington: Smart Manufacturing Leadership Coalition; 2011. 链接1

[ 5 ] State Council of the People’s Republic of China. [New generation of artificial intelligence development plan] [Internet]. Beijing: State Council of the People’s Republic of China; 2017 Jul 8 [cited 2019 May 8]. Available from: https://flia.org/wp-content/uploads/2017/07/A-New-Generation-of-ArtificialIntelligence-Development-Plan-1.pdf. Chinese. 链接1

[ 6 ] Yuan Z, Qin W, Zhao J. Smart manufacturing for the oil refining and petrochemical industry. Engineering 2017;3(2):179–82. 链接1

[ 7 ] Zhou J, Li P, Zhou Y, Wang B, Zang J, Meng L. Toward new-generation intelligent manufacturing. Engineering 2018;4(1):11–20. 链接1

[ 8 ] Cernansky R. Chemistry: green refill. Nature 2015;519(7543):379–80.

[ 9 ] Russell SJ, Norvig P. Artificial intelligence: a modern approach. Kuala Lumpur: Pearson Education Limited; 2016. 链接1

[10] Silver D, Huang A, Maddison CJ, Guez A, Sifre L, van den Driessche G, et al. Mastering the game of Go with deep neural networks and tree search. Nature 2016;529(7587):484–9. 链接1

[11] Bogle IDL. A perspective on smart process manufacturing research challenges for process systems engineers. Engineering 2017;3(2):161–5. 链接1

[12] Chai T. Industrial process control systems: research status and development direction. Sci Sin Inf 2016;46(8):1003–15. Chinese. 链接1

[13] Tauseef SM, Abbasi T, Abbasi SA. Development of a new chemical processindustry accident database to assist in past accident analysis. J Loss Prev Process Ind 2011;24(4):426–31. 链接1

[14] Huang P, Zhang J. Facts related to August 12, 2015 explosion accident in Tianjin, China. Process Saf Prog 2015;34(4):313–4. 链接1

[15] Wang B, Wu C, Reniers G, Huang L, Kang L, Zhang L. The future of hazardous chemical safety in China: opportunities, problems, challenges and tasks. Sci Total Environ 2018;643:1–11. 链接1

[16] Bond J. Professional ethics and corporate social responsibility. Process Saf Environ Prot 2009;87(3):184–90. 链接1

[17] Dornfeld DA. Green manufacturing: fundamentals and applications. New York: Springer; 2013.

[18] Clark JH. Green chemistry: challenges and opportunities. Green Chem 1999;1 (1):1–8. 链接1

[19] BASF Corporation. Fire at the North Harbor in Ludwigshafen [Internet]. Ludwigshafen: BASF Corporation; 2016 Oct 27 [cited 2019 May 8]. Available from: https://www.basf.com/global/en/media/news-releases/2016/10/p-16- 359.html. 链接1

[20] BASF Corporation. German firefighter dies 11 months after BASF explosion [Internet]. Haarlem: Expatica; 2017 Sep 5 [cited 2019 May 8]. Available from: https://www.expatica.com/de/germany-chemicals-accident-basf/. 链接1

[21] Qu Z, Feng H, Zeng Z, Zhuge J, Jin S. A SVM-based pipeline leakage detection and pre-warning system. Measurement 2010;43(4):513–9. 链接1

[22] Paulheim H. Knowledge graph refinement: a survey of approaches and evaluation methods. Semant Web 2017;8(3):489–508. 链接1

[23] Färber M, Bartscherer F, Menne C, Rettinger A. Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO. Semant Web 2018;9(1):77–129. 链接1

[24] Ehrlinger L, Wöß W. Towards a definition of knowledge graphs. In: Proceedings of SEMANTICS 2016: posters and demos track; 2016 Sep 13–14; Leipzig, Germany; 2016. 链接1

[25] Liu Q, Li Y, Duan H, Liu Y, Qin Z. Knowledge graph construction techniques. J Comput Res Dev 2016;53(3):582–600. 链接1

[26] Gordon SE, Schmierer KA, Gill RT. Conceptual graph analysis: knowledge acquisition for instructional system design. Hum Factors 1993;35(3):459–81. 链接1

[27] Miwa M, Sasaki Y. Modeling joint entity and relation extraction with table representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing; 2014 Oct 25–29; Doha, Qatar; 2014. p. 1858–69. 链接1

[28] Paliouras G, Spyropoulos CD, Tsatsaronis G. Knowledge-driven multimedia information extraction and ontology evolution: bridging the semantic gap. Heidelberg: Springer; 2011. 链接1

[29] Dong XL, Gabrilovich E, Heitz G, Horn W, Murphy K, Sun S, et al. From data fusion to knowledge fusion. Proc VLDB Endowment 2014;7(10):881–92. 链接1

[30] Wang X, Gu T, Zhang D, Pung HK. Ontology based context modeling and reasoning using OWL. In: Proceedings of the 2nd IEEE Annual Conference on Pervasive Computing and Communications Workshops; 2004 Mar 14–17; Washington, DC, USA. New York: IEEE; 2004. p. 18–22. 链接1

[31] Kamsu-Foguem B, Noyes D. Graph-based reasoning in collaborative knowledge management for industrial maintenance. Comput Ind 2013;64 (8):998–1013. 链接1

[32] Zhu J, Ge Z, Song Z, Zhou L, Chen G. Large-scale plant-wide process modeling and hierarchical monitoring: a distributed Bayesian network approach. J Process Contr 2018;65:91–106. 链接1

[33] Larrañaga P, Karshenas H, Bielza C, Santana R. A review on evolutionary algorithms in Bayesian network learning and inference tasks. Inf Sci 2013;233:109–25. 链接1

[34] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015;521(7553):436–44.

[35] Zhu J, Ge Z, Song Z, Gao F. Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data. Annu Rev Contr 2018;46:107–33. 链接1

[36] Zhou Z, Qi G, Glimm B. Exploring parallel tractability of ontology materialization. In: Proceedings of the 22nd European Conference on Artificial Intelligence; 2016 Aug 29–Sep 2; Amsterdam, the Netherlands. Amsterdam: IOS Press; 2016. p. 73–81. 链接1

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