Journal Home Online First Current Issue Archive For Authors Journal Information 中文版

Engineering >> 2021, Volume 7, Issue 9 doi: 10.1016/j.eng.2021.04.023

Intelligent Manufacturing for the Process Industry Driven by Industrial Artificial Intelligence

a State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
b School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm 10044, Sweden

Received: 2021-01-18 Revised: 2021-03-20 Accepted: 2021-04-06 Available online: 2021-07-29

Next Previous

Abstract

Based on the analysis of the characteristics and operation status of the process industry, as well as the development of the global intelligent manufacturing industry, a new mode of intelligent manufacturing for the process industry, namely, deep integration of industrial artificial intelligence and the Industrial Internet with the process industry, is proposed. This paper analyzes the development status of the existing three-tier structure of the process industry, which consists of the enterprise resource planning, the manufacturing execution system, and the process control system, and examines the decision-making, control, and operation management adopted by process enterprises. Based on this analysis, it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human–machine cooperation and an intelligent autonomous control system. Finally, this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry.

Figures

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

References

[ 1 ] Liu K. [70 years of self-dependence, hard struggle: China has become the largest manufacturing country with all industrial categories]. Guangming Daily. 2019 Sep 21. Sect. 10:(col. 1). Chinese.

[ 2 ] 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. link1

[ 3 ] Ge W, Guo Li, Li J. Toward greener and smarter process industries. Engineering 2017;3(2):152–3. link1

[ 4 ] Gui W, Chen X, Sun Y, Xie Y, Zeng Z. Knowledge-driven process industry smart manufacturing. Sci Sin Inf 2020;50(9):1345–60. link1

[ 5 ] Mao S, Wang B, Tang Y, Qian F. Opportunities and challenges of artificial intelligence for green manufacturing in the process industry. Engineering 2019;5(6):995–1002. link1

[ 6 ] Chinese Academy of Engineering, National Natural Science Foundation of China. [Research on development strategy of big data and knowledge automation for manufacturing process]. Report. Beijing: Chinese Academy of Engineering, National Natural Science Foundation of China; 2016. Chinese.

[ 7 ] Chai TY, Ding JL. Smart and optimal manufacturing for process industry. Strateg Stud Chin Acad Eng 2018;20(4):51–8. link1

[ 8 ] Eager J, Whittle M, Smit J, Cacciaguerra G, Lale E. Opportunities of artificial intelligence [Internet]. Luxembourg: European Parliament; 2020 Jun [cited 2021 Jan 11]. Available from: https://www.sipotra.it/wp-content/uploads/ 2020/07/Opportunities-of-Artificial-Intelligence.pdf.

[ 9 ] Yuan Y, Ma G, Cheng C, Zhou B, Zhao H, Zhang HT, et al. A general end-to-end diagnosis framework for manufacturing systems. Natl Sci Rev 2020;7(2):418– 29.

[10] Ding H, Gao RX, Isaksson AJ, Landers RG, Parisini T, Yuan Y. State of AI-based monitoring in smart manufacturing and introduction to focused section. IEEE ASME Trans Mechatron 2020;25(5):2143–54. link1

[11] Panetto H, Weichhart G, Pinto R. Special section on Industry 4.0: challenges for the future in manufacturing. Annu Rev Contr 2019;47:198–9. link1

[12] Yang T. Guest editorial of the special session on industrial artificial intelligence. Acta Automatica Sin 2020;46(10):2003–4. link1

[13] Chai TY. Development direction of industrial artificial intelligence. Acta Automatica Sin 2020;46(10):2005–12. Chinese. link1

[14] Chai TY. Industrial process control systems: research status and development direction. Sci Sin Inf 2016;46(8):1003–15. Chinese. link1

[15] Lee J, Li X, Xu Y, Yang S, Sun KY. Recent advances and prospects in industrial AI and applications. Acta Automatica Sin 2020;46(10):2031–44. link1

[16] Baru C, Daimler E, Ferguson R, Forbe N, Harder E, Ferguson R, et al. The national artificial intelligence research and development strategic plan [Internet]. Washington, DC: National Science and Technology Council, Networking and Information Technology Research and Development Subcommittee; 2016 Oct [cited 2021 Jan 11]. Available from: https://www.nitrd.gov/pubs/national_ai_ rd_strategic_plan.pdf. link1

[17] Summary of the White House summit on artificial intelligence for American industry [Internet]. Washington, DC: the White House Office of Science and Technology Policy; 2018 May 10 [cited 2021 Jan 11]. Available from: https:// trumpwhitehouse.archives.gov/wp-content/uploads/2018/05/Summary-Reportof-White-House-AI-Summit.pdf?latest. link1

[18] Statement on artificial intelligence for American industry [Internet]. Washington, DC: National Science Foundation; 2018 May 10 [cited 2021 Jan 11]. Available from: https://www.nsf.gov/news/news_summ.jsp?cntn_id=245418. link1

[19] Important notice—change in individual eligibility restrictions [Internet]. Washington, DC: National Artificial Intelligence (AI) Research Institutes; 2020 Sep 21; [cited 2021 Jan 11]. Available from: https://www.nsf.gov/ news/news_summ.jsp?cntn_id=301176&org=NSF.

[20] Fiscal year 2020 administration research and development budget priorities: memorandum for the heads of executive departments and agencies [Internet]. Washington, DC: Executive Office of the President; 2018 Jul 31; [cited 2021 Jan 11]. Available from: https://www.whitehouse.gov/wp-content/uploads/2018/ 07/M-18-22.pdf. link1

[21] Fiscal year 2021 administration research and development budget priorities: memorandum for the heads of executive departments and agencies [Internet]. Washington, DC: Executive Office of the President; 2019 Aug 31; [cited 2021 Jan 11]. Available from: https://www.whitehouse.gov/wp-content/uploads/ 2019/08/FY-21-RD-Budget-Priorities.pdf. link1

[22] Gu G. [Germany: artificial intelligence keeps the pace with Industrial 4.0]. Science and Technology Daily. 2018 Apr 10; Sect. 2. Chinese.

[23] The Federal Government’s artificial intelligence strategy [Internet]. Berlin: Federal Ministry for Economic Affairs and Energy; [cited 2021 Jan 11]. Available from: https://www.de.digital/DIGITAL/Redaktion/EN/Standardartikel/ artificial-intelligence-strategy.html. link1

[24] The Research Group for Research on Intelligent Manufacturing Development Strategy. Research on intelligent manufacturing development strategy in China. Strateg Stud Chin Acad Eng 2018;20(4):1–8.

[25] Zhou Ji, Li P, Zhou Y, Wang B, Zang J, Meng L. Toward new-generation intelligent manufacturing. Engineering 2018;4(1):11–20. link1

[26] Chai TY, Ding JL, Gui WH, Qian F. Research on the development strategy of big data and manufacturing process knowledge automation. Beijing: Science Press; 2019. Chinese. link1

[27] Ding JL, Yang CE, Chen YD, Chai TY. Current status and prospects of intelligent optimization decision-making systems for complex industrial processes. Acta Automatica Sin 2018;44(11):1931–43. Chinese. link1

[28] Kusiak A. Smart manufacturing must embrace big data. Nature 2017;544 (7648):23–5. link1

[29] Cyber–physical systems, program announcements and information [Internet]. Washington, DC: National Science Foundation; 2009 Feb 27 [cited 2021 Jan 11]. Available from: https://www.nsf.gov/pubs/2008/nsf08611/nsf08611.pdf. link1

[30] Chinese Association of Automation. [Automation discipline development roadmap]. Beijing: China Science and Technology Press; 2020. Chinese. link1

[31] Chai TY. Challenges of optimal control for plant-wide production processes in terms of control and optimization theories. Acta Automatica Sin 2009;35 (6):641–9. link1

[32] Chai TY, Jin YH, Ren DX, Shao HH, Qian JX, Li P, et al. Contemporary integrated manufacturing system based on three-layer structure in process industry. Control Eng China 2002;9(3):1–6. Chinese. link1

[33] Zhou Ji, Zhou Y, Wang B, Zang J. Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing. Engineering 2019;5 (4):624–36. link1

[34] Chai TY. Artificial intelligence research challenges in intelligent manufacturing processes. Bull Natl Nat Sci Found China 2018;32(3):251–6. link1

[35] Gil Y, Greaves M, Hendler J, Hirsh H. Amplify scientific discovery with artificial intelligence. Science 2014;346(6206):171–2. link1

[36] [Industrial intelligence white paper] [Internet]. Beijing: Industrial Internet Industry Alliance; 2020 Apr 26 [cited 2021 Jan 11]. Available from: https:// www.miit.gov.cn/ztzl/rdzt/gyhlw/cgzs/art/2020/art_e1842c433fce43e39a45c e96be50213a.html. Chinese. link1

[37] Yuan Ye, Tang X, Zhou W, Pan W, Li X, Zhang HT, et al. Data driven discovery of cyber physical systems. Nat Commun 2019;10(1):4894. link1

[38] Convergence research at NSF [Internet]. Washington, DC: National Science Foundation; 2020 [cited 2021 Jan 11]. Available from: https://www.nsf.gov/ od/oia/convergence/index.jsp. link1

[39] Dai Q. [Some thoughts on artificial intelligence computing capabilities, algorithms, and testing]. Chin Assoc Artif Intell Newsl 2020;10(11):1–4. link1

[40] Xi sends congratulatory letter to Industrial Internet Global Summit [Internet]. Beijing: Xin Hua Net; 2019 Oct 18 [cited 2021 Jan 11]. Available from: http:// en.people.cn/n3/2019/1018/c90000-9624243.html. link1

[41] National Academies of Sciences, Engineering, and Medicine. Graduate STEM education for the 2lst century. Washington, DC: The National Academies Press; 2018.

[42] National Academies of Sciences, Engineering, and Medicine. The endless frontier: the next 75 years in science. Washington, DC: The National Academies Press; 2020.

Related Research