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

Engineering - Tập 5 - Trang 995-1002 - 2019
Shuai Mao, Bing Wang1, Yang Tang1, Feng Qian1
1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China

Tài liệu tham khảo

Qian, 2017, Fundamental theories and key technologies for smart and optimal manufacturing in the process industry, Engineering, 3, 154, 10.1016/J.ENG.2017.02.011 Giffi, 2016 Williams, 2011, Environmental effects of information and communications technologies, Nature, 479, 354, 10.1038/nature10682 Smart Manufacturing Leadership Coalition. Implementing 21st century smart manufacturing: workshop summary report. Washington: Smart Manufacturing Leadership Coalition; 2011. 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-Artificial-Intelligence-Development-Plan-1.pdf. Chinese. Yuan, 2017, Smart manufacturing for the oil refining and petrochemical industry, Engineering, 3, 179, 10.1016/J.ENG.2017.02.012 Zhou, 2018, Toward new-generation intelligent manufacturing, Engineering, 4, 11, 10.1016/j.eng.2018.01.002 Cernansky, 2015, Chemistry: green refill, Nature, 519, 379, 10.1038/nj7543-379a Russell, 2016 Silver, 2016, Mastering the game of Go with deep neural networks and tree search, Nature, 529, 484, 10.1038/nature16961 Bogle, 2017, A perspective on smart process manufacturing research challenges for process systems engineers, Engineering, 3, 161, 10.1016/J.ENG.2017.02.003 Chai, 2016, Industrial process control systems: research status and development direction, Sci Sin Inf, 46, 1003, 10.1360/N112016-00062 Tauseef, 2011, Development of a new chemical process-industry accident database to assist in past accident analysis, J Loss Prev Process Ind, 24, 426, 10.1016/j.jlp.2011.03.005 Huang, 2015, Facts related to August 12, 2015 explosion accident in Tianjin, China, Process Saf Prog, 34, 313, 10.1002/prs.11789 Wang, 2018, The future of hazardous chemical safety in China: opportunities, problems, challenges and tasks, Sci Total Environ, 643, 1, 10.1016/j.scitotenv.2018.06.174 Bond, 2009, Professional ethics and corporate social responsibility, Process Saf Environ Prot, 87, 184, 10.1016/j.psep.2008.11.002 Dornfeld, 2013 Clark, 1999, Green chemistry: challenges and opportunities, Green Chem, 1, 1, 10.1039/a807961g 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. 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/. Qu, 2010, A SVM-based pipeline leakage detection and pre-warning system, Measurement, 43, 513, 10.1016/j.measurement.2009.12.022 Paulheim, 2017, Knowledge graph refinement: a survey of approaches and evaluation methods, Semant Web, 8, 489, 10.3233/SW-160218 Färber, 2018, Linked data quality of DBpedia, Freebase, OpenCyc, Wikidata, and YAGO, Semant Web, 9, 77, 10.3233/SW-170275 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. Liu, 2016, Knowledge graph construction techniques, J Comput Res Dev, 53, 582 Gordon, 1993, Conceptual graph analysis: knowledge acquisition for instructional system design, Hum Factors, 35, 459, 10.1177/001872089303500305 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. Paliouras, 2011 Dong, 2014, From data fusion to knowledge fusion, Proc VLDB Endowment, 7, 881, 10.14778/2732951.2732962 Wang, 2004, Ontology based context modeling and reasoning using OWL, 18 Kamsu-Foguem, 2013, Graph-based reasoning in collaborative knowledge management for industrial maintenance, Comput Ind, 64, 998, 10.1016/j.compind.2013.06.013 Zhu, 2018, Large-scale plant-wide process modeling and hierarchical monitoring: a distributed Bayesian network approach, J Process Contr, 65, 91, 10.1016/j.jprocont.2017.08.011 Larrañaga, 2013, A review on evolutionary algorithms in Bayesian network learning and inference tasks, Inf Sci, 233, 109, 10.1016/j.ins.2012.12.051 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Zhu, 2018, Review and big data perspectives on robust data mining approaches for industrial process modeling with outliers and missing data, Annu Rev Contr, 46, 107, 10.1016/j.arcontrol.2018.09.003 Zhou, 2016, Exploring parallel tractability of ontology materialization, 73