Process industry knowledge automation and its applications in aluminum electrolysis production

被引：5

作者：

Gui W.-H. ^{[1
]}

Yue W.-C. ^{[1
]}

Chen X.-F. ^{[1
]}

Xie Y.-F. ^{[1
]}

Yang C.-H. ^{[1
]}

机构：

[1] School of Information Science and Engineering, Central South University, Changsha, 410083, Hunan

来源：

Kongzhi Lilun Yu Yingyong/Control Theory and Applications | 2018年 / 35卷 / 07期

基金：

中国国家自然科学基金;

关键词：

Aluminum reduction cell identification; Knowledge automation; Knowledge based systems; Knowledge model; Process industry; Root cause analysis;

D O I：

10.7641/CTA.2018.80201

中图分类号：

学科分类号：

摘要：

There exist a large amount of knowledge-based work in process industry, including business management, plant scheduling and equipment operation, which need the use of knowledge and creativity to complete the work. The process industry knowledge automation obtains the related domain knowledge through knowledge discovery technology, and automatically deduces the obtained knowledge, so that it can carry out autonomous decision-making and finally realize the wisdom, green and efficient production of process industry. This paper summarizes the domestic and foreign researches about the process industry knowledge automation, including knowledge discovery, automatic reasoning and autonomous decision-making automation, as well as knowledge-based automation application technology and related industrial application softwares, moreover, the problems existing in these methods and the developed software are also analyzed. With the examples of the identification of aluminum electrolysis cell states and the root cause analysis of abnormal condition, a detailed exposition of some researches about aluminum electrolysis industry for the process of industrial knowledge automation are presented in this paper. © 2018, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.

引用

页码：887 / 899

页数：12

共 44 条

[1]

Gui W., Wang C., Xie Y., Et al., The necessary way to realize great-leap-forward development of process industries, China Science Fund, 5, pp. 337-342, (2015)

[2]

Qian F., Du W., Zhong W., Et al., Problems and challenges of smart optimization manufacturing in petrochemical industries, Acta Automatica Sinica, 43, 6, pp. 893-901, (2017)

[3]

Heisig P., Adekunle S.O., Kianto A., Et al., Knowledge management and business performance: global experts' views on future research needs, Journal of Knowledge Management, 20, 6, pp. 1169-1198, (2016)

[4]

Cano K.M., Cantwell J., Hannigan T.J., Et al., Knowledge connectivity: an agenda for innovation research in international business, Journal of International Business Studies, 47, 3, pp. 255-262, (2016)

[5]

Nowacki R., Nachnik K., Innovations within knowledge management, Journal of Business Research, 69, 5, pp. 1577-1581, (2016)

[6]

Gui W., Chen X., Yang C., Et al., Knowledge automation and its industrial application, Scientia Sinica Informationis, 46, 8, pp. 1016-1034, (2016)

[7]

Zhang B., Yang C., Zhu H., Et al., Controllable-domain-based fuzzy rule extraction for copper removal process control, IEEE Transactions on Fuzzy Systems, 26, 3, pp. 1744-1756, (2018)

[8]

Prajapati A.P., Chaturvedi D.K., Semantic network based knowledge representation for cognitive decision making in teaching electrical motor concepts, 2017 International Conference on Computer, Communications and Electronics(Comptelix), pp. 146-151, (2017)

[9]

Papageorgiou E.I., Salmeron J.L., A review of fuzzy cognitive maps research during the last decade, IEEE Transactions on Fuzzy Systems, 21, 1, pp. 66-79, (2013)

[10]

Zagorecki A., Druzdzel M.J., Knowledge engineering for Bayesian networks: how common are noisy-MAX distributions in practice?, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 43, 1, pp. 186-195, (2013)

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