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

Strategic Study of Chinese Academy of Engineering >> 2022, Volume 24, Issue 4 doi: 10.15302/J-SSCAE-2022.04.013

Intelligent Model Library for Nonferrous Metal Industry: Construction Method and Application

1. School of Automation, Central South University, Changsha 410083, China;

2. Peng Cheng Laboratory, Shenzhen 518055, Guangdong, China
 

Received:2022-05-10 Revised:2022-07-05 Available online:2022-07-25

Next Previous

Abstract

Nonferrous metal industry is the foundation of China's substantial economy and plays a key role in national economy and defense construction. Industrial software is crucial for the high-quality development of the nonferrous metal industry and is associated with the in-depth implementation of national software development strategies. Currently, the industrial software development in the nonferrous metal industry is significantly restricted by the lack of knowledge models. Hence, we propose a method for constructing an intelligent model library for the nonferrous metal industry. Considering meta-model-driven engineering, we define the nonferrous metallurgical meta-model and its attributes, and propose a meta-modeling method based on the MODELING architecture. Additionally, we design an overall architecture for the intelligent model library based on industrial Internet, an agile modeldevelopment environment integrating multiple languages, and a meta-model encapsulation system based on multi-scenario black-box reuse. Moreover, a meta-model full lifecycle management platform is constructed based on a five-layer two-dimension classification standard and the domain knowledge graph. The intelligent model library for the nonferrous metal industry is developed based on the long-term accumulation of nonferrous metallurgy process mechanism, operating experiences, and intelligent methods. The good role of intelligent model library in improving the intelligent level in engineering applications is presented through the application of two typical nonferrous metallurgical scenarios. The intelligent model library of the nonferrous metal industry provides a core knowledge support for the development of industrial software and plays a fundamental role in promoting smart manufacturing and strengthening the nonferrous metal industry.

Image

图1

图1

图1

图2

图2

图2

图3

图3

图3

图4

图4

图4

图5

图5

图5

图6

图6

图6

图7

图7

图7

图8

图8

图8

图9

图9

图9

References

[1]  袁小锋 , 桂卫华 , 陈晓方 , 等 . 人工智能助力有色金属工业转型升级 [J]. 中国工程科学 , 2018 , 20 4 : 59 ‒ 65 .

[2]  Qian F , Zhong W M , Du W L . Fundamental theories and key technologies for smart and optimal manufacturing in the process industry [J]. Engineering , 2017 , 3 2 : 154 ‒ 160 .

[3]  贾明星 . 七十年辉煌历程 新时代砥砺前行——中国有色金属工业发展与展望 [J]. 中国有色金属学报 , 2019 , 29 9 : 1801 ‒ 1808 .

[4]  柴立元 , 王云燕 , 孙竹梅 , 等 . 绿色冶金创新发展战略研究 [J]. 中国工程科学 , 2022 , 24 2 : 10 ‒ 21 .

[5]  柴天佑 , 丁进良 . 流程工业智能优化制造 [J]. 中国工程科学 , 2018 , 20 4 : 51 ‒ 58 .

[6]  邵珠峰 , 赵云 , 王晨 , 等 . 新时期我国工业软件产业发展路径研究 [J]. 中国工程科学 , 2022 , 24 2 : 86 ‒ 95 .

[7]  边缘计算产业联盟 , 工业互联网产业联盟 . 边缘计算与云计算协同白皮书2018年 [R]. 北京 : 边缘计算产业联盟, 工业互联网产业联盟 , 2018 .

[8]  新华网 . 两院院士大会中国科协第十次全国代表大会在京召开 习近平发表重要讲话 [EBOL]. 2021-05-28 ‍[ 2022-06-10 ]. http:www.xinhuanet.compoliticsleaders2021-0528c_1127504936. htm .

[9]  桂卫华 , 曾朝晖 , 陈晓方 , 等 . 知识驱动的流程工业智能制造 [J]. 中国科学: 信息科学 , 2020 , 50 9 : 1345 ‒ 1360 .

[10]  Botha S , Le Roux J D , Craig I K . Hybrid non-linear model predictive control of a run-of-mine ore grinding mill circuit [J]. Minerals Engineering , 2018 , 123 : 49 ‒ 62 .

[11]  刘美丽 , 唐朝晖 , 王晓丽 , 等 . 基于多信息融合与可拓理论的锑浮选工况识别方法 [J]. 中南大学学报自然科学版 , 2015 , 46 12 : 4512 ‒ 4519 .

[12]  桂卫华 , 阳春华 , 陈晓方 , 等 . 有色冶金过程建模与优化的若干问题及挑战 [J]. 自动化学报 , 2013 , 39 3 : 197 ‒ 207 .

[13]  Huang K K , Tao S J , Liu Y S , et al . Label propagation dictionary learning based process monitoring method for industrial process with between-mode similarity [J]. Science China Information Sciences , 2022 , 65 1 : 1 ‒ 17 .

[14]  阳春华 , 韩洁 , 周晓君 , 等 . 有色冶金过程不确定优化方法探讨 [J]. 控制与决策 , 2018 , 33 5 : 856 ‒ 865 .

[15]  张健 . 基于动力学控制的钛加工材料成型优化技术 [J]. 世界有色金属 , 2015 10 : 66 ‒ 67 .

[16]  王立平 , 张超 , 蔡恩磊 , 等 . 面向自主工业软件的知识提取和知识库构建方法 [J]. 清华大学学报自然科学版 , 2022 , 62 5 : 978 ‒ 986 .

[17]  陶永 , 蒋昕昊 , 刘默 , 等 . 智能制造和工业互联网融合发展初探 [J]. 中国工程科学 , 2020 , 22 4 : 24 ‒ 33 .

[18]  中华人民共和国国务院 . 关于深化"互联网+先进制造业"发展工业互联网的指导意见 [EBOL]. 2017-11-27 ‍[ 2022-06-10 ]. http:www.gov.cnxinwen2017-1127content_5242603.htm .

[19]  工业互联网产业联盟 . 工业互联网体系架构白皮书 [R]. 北京 : 工业互联网产业联盟 , 2020 .

[20]  Sun B , Dai J T , Huang K K , et al . Smart manufacturing of nonferrous metallurgical processes: Review and perspectives [J]. International Journal of Minerals, Metallurgy and Materials , 2022 , 29 4 : 611 ‒ 625 .

[21]  Woo M . The rise of nolow code software development: No experience needed? [J]. Engineering , 2020 , 6 9 : 960 ‒ 961 .

[22]  Yang C H , Sun B. Modeling , optimization , and control of zinc hydrometallurgical purification process [M]. Salt Lake City : American Academic Press , 2021 .

[23]  王晨 , 宋亮 , 李少昆 . 工业互联网平台: 发展趋势与挑战 [J]. 中国工程科学 , 2018 , 20 2 : 15 ‒ 19 .

[24]  Haji W H . Web-based service optimization with JSON-RPC platform in Java and PHP [C]. Lampung : International Conference on Engineering and Technology Development ICETD , 2012 .

[25]  Huang X W , Hsieh C Y , Wu C H , et al . A token-based user authentication mechanism for data exchange in RESTful API [C]. Taipei : The 18th International Conference on Network-Based Information Systems , 2015 .

[26]  Pechter R . What´s PMML and what´s new in PMML 4.0? [J]. ACM SIGKDD Explorations Newsletter , 2009 , 11 1 : 19 ‒ 25 .

[27]  Zhong W M , Li C Y , Peng X , et al . A knowledge base system for operation optimization: Design and implementation practice for the polyethylene process [J]. Engineering , 2019 , 5 6 : 1041 ‒ 1048 .

[28]  Miller J J . Graph database applications and concepts with Neo4j [C]. Atlanta : Proceedings of the Southern Association for Information Systems Conference , 2013 .

[29]  Liang H P , Yang C H , Huang K K , et al . A hybrid first principles and data-driven process monitoring method for zinc smelting roasting process [J]. IEEE Transactions on Instrumentation and Measurement , 2021 , 70 : 1 ‒ 14 .

[30]  Liu Y S , Yang C H , Huang K K , et al . Non-ferrous metals price forecasting based on variational mode decomposition and LSTM network [J]. Knowledge-Based Systems , 2020 , 188 : 1 ‒ 15 .

Related Research