SCIENTIA SINICA Informationis, Volume 50 , Issue 9 : 1345(2020) https://doi.org/10.1360/SSI-2020-0211

Knowledge-driven process industry smart manufacturing

More info
  • ReceivedJul 10, 2020
  • AcceptedAug 28, 2020
  • PublishedSep 22, 2020


Smart manufacturing is a necessary approach to realize highly efficient, green, and high-quality development of China's process industry. Knowledge is the core production factor of smart manufacturing in the process industry, where knowledge is a new mode of innovation in process industry development. Based on the importance and characteristics of knowledge in process manufacturing, this paper reviews the research and application of knowledge in process manufacturing. A systematic framework of knowledge-driven process industry smart manufacturing is proposed, and crucial technologies, such as knowledge deep acquisition, knowledge injection, and industrial knowledge graph, are discussed. Using a case study of the smart decision-making process of aluminum electrolysis enterprises, the important role of the above crucial technologies in the process industry smart manufacturing system is presented. Further, the challenges and future prospects of knowledge-driven process industry smart manufacturing are provided.

Funded by




[1] Chai T. Industrial process control systems: research status and development direction. Sci Sin-Inf, 2016, 46: 1003-1015 CrossRef Google Scholar

[2] Qian F, Gui W H. Boosting optimization and upgrade for manufacturing industry by artificial intelligence. Bulletin of National Natural Science Foundation of China, 2018, 3: 257-261. Google Scholar

[3] Gui W H, Wang C H, Xie Y F, et al. The necessary way to realize great-leap-forward development of process industries. Bulletin of National Natural Science Foundation of China, 2015, 5: 337-342. Google Scholar

[4] Lee J, Singh J, Azamfar M, et al. Industrial AI: A systematic framework for AI in industrial applications. Zhongguo Jixie Gongcheng/China Mechanical Engineering, 2020, 31(1): 37-48. Google Scholar

[5] Ding J L, Yang C E, Chen Y D, et al. Research progress and prospects of intelligent optimization decision making in complex industrial process. Acta Automatica Sinica, 2018, 44(11): 1931-1943. Google Scholar

[6] Wang Y. Research on production planning and scheduling under uncertainty. Dissertation for Ph.D. Degree. Hangzhou: Zhejiang University, 2016. Google Scholar

[7] Qian F, Du W L, Zhong W M, et al. Problems and challenges of smart optimization manufacturing in petrochemical industries. Acta Automatica Sinica, 2017, 43(6): 893-901. Google Scholar

[8] Sun C Y, Zhou J W. Smart and optimal manufacturing development strategy for mineral processing industry. Nonferrous Metals (Mineral Processing Section), 2019, 5: 1-5. Google Scholar

[9] Cai T Y, Ding J L, Gui W H, et al. Research on the development strategy of big data and manufacturing process knowledge automation. China Science Press, 2019. Google Scholar

[10] Gui W H, Yue W C, Xie Y F, et al. Review of intelligent optimal manufacturing for aluminum reduction production. Acta Automatica Sinica, 2018, 44(11): 39-52. Google Scholar

[11] Wang F Y. The destiny: towards knowledge au-tomation--preface of the special issue for the 50th anniver-sary of Acta Automatica Sinica. Acta Automatica Sin, 2013, 39: 1741-1743 CrossRef Google Scholar

[12] Xie Y, Gui W, Yang C. Knowledge automation and its industrial application. Sci Sin-Inf, 2016, 46: 1016-1034 CrossRef Google Scholar

[13] Park H, Jung J Y. SAX-ARM: Deviant event pattern discovery from multivariate time series using symbolic aggregate approximation and association rule mining. Expert Syst Appl, 2020, 141: 112950 CrossRef Google Scholar

[14] Zhang B, Yang C, Zhu H. Controllable-Domain-Based Fuzzy Rule Extraction for Copper Removal Process Control. IEEE Trans Fuzzy Syst, 2018, 26: 1744-1756 CrossRef Google Scholar

[15] Yang C, Zhou L, Huang K. Multimode process monitoring based on robust dictionary learning with application to aluminium electrolysis process. Neurocomputing, 2019, 332: 305-319 CrossRef Google Scholar

[16] Wang H, Lin N, Jing S. Supporting knowledge uncertainty engineering-knowledge acquisition. Computer Integrated Manufacturing System, 2015, 21(10): 2558-2563 DOI: 10.13196/j.cims.2015.10.002. Google Scholar

[17] Xie S, Xie Y, Huang T. Generalized Predictive Control for Industrial Processes Based on Neuron Adaptive Splitting and Merging RBF Neural Network. IEEE Trans Ind Electron, 2019, 66: 1192-1202 CrossRef Google Scholar

[18] Martinsen K, Downey J, Baturynska I. Human-Machine Interface for Artificial Neural Network Based Machine Tool Process Monitoring. Procedia CIRP, 2016, 41: 933-938 CrossRef Google Scholar

[19] Huang Z, Yang C, Chen X. Adaptive over-sampling method for classification with application to imbalanced datasets in aluminum electrolysis. Neural Comput Applic, 2020, 32: 7183-7199 CrossRef Google Scholar

[20] Bi W, Dandy G C, Maier H R. Improved genetic algorithm optimization of water distribution system design by incorporating domain knowledge. Environ Model Software, 2015, 69: 370-381 CrossRef Google Scholar

[21] Roldan Reyes E, Negny S, Cortes Robles G. Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning: Application to process engineering design. Eng Appl Artificial Intelligence, 2015, 41: 1-16 CrossRef Google Scholar

[22] Rintala L, Leikola M, Sauer C. Designing gold extraction processes: Performance study of a case-based reasoning system. Miner Eng, 2017, 109: 42-53 CrossRef Google Scholar

[23] Gao Z, Cecati C, Ding S. A Survey of Fault Diagnosis and Fault-Tolerant Techniques Part II: Fault Diagnosis with Knowledge-Based and Hybrid/Active Approaches. IEEE Trans Ind Electron, 2015, : 1-1 CrossRef Google Scholar

[24] Kamsu-Foguem B, Rigal F, Mauget F. Mining association rules for the quality improvement of the production process. Expert Syst Appl, 2013, 40: 1034-1045 CrossRef Google Scholar

[25] Yu X, Huang D, Jiang Y. Iterative learning belief rule-base inference methodology using evidential reasoning for delayed coking unit. Control Eng Practice, 2012, 20: 1005-1015 CrossRef Google Scholar

[26] Yue W, Gui W, Chen X. A Data and Knowledge Collaboration Strategy for Decision-Making on the Amount of Aluminum Fluoride Addition Based on Augmented Fuzzy Cognitive Maps. Engineering, 2019, 5: 1060-1076 CrossRef Google Scholar

[27] Lima N M N, Li?an L Z, Manenti F. Fuzzy cognitive approach of a molecular distillation process. Chem Eng Res Des, 2011, 89: 471-479 CrossRef Google Scholar

[28] Dong C, Zhou Z, Zhang Q. Cubic Dynamic Uncertain Causality Graph: A New Methodology for Modeling and Reasoning About Complex Faults With Negative Feedbacks. IEEE Trans Rel, 2018, 67: 920-932 CrossRef Google Scholar

[29] Li L, Yue W. Dynamic uncertain causality graph based on Intuitionistic fuzzy sets and its application to root cause analysis. Appl Intell, 2020, 50: 241-255 CrossRef Google Scholar

[30] Wang W M, Peng X, Zhu G. Dynamic representation of fuzzy knowledge based on fuzzy petri net and genetic-particle swarm optimization. Expert Syst Appl, 2014, 41: 1369-1376 CrossRef Google Scholar

[31] Zhang Y, Luo X, Buis J J. LCA-oriented semantic representation for the product life cycle. J Cleaner Production, 2015, 86: 146-162 CrossRef Google Scholar

[32] Terletskyi D. Inheritance in object-oriented knowledge representation. In: Information and Software technologies. Berlin: Springer International Publishing, 2015. 293--305. Google Scholar

[33] Minsky M. A framework for representing knowledge. Readings Cognition Science, 1974, 76: 156-189 http://hdl.handle.net/1721.1/6089. Google Scholar

[34] Yue W, Chen X, Gui W. A knowledge reasoning Fuzzy-Bayesian network for root cause analysis of abnormal aluminum electrolysis cell condition. Front Chem Sci Eng, 2017, 11: 414-428 CrossRef Google Scholar

[35] Diallo T M L, Henry S, Ouzrout Y. Bayesian Network Building for Diagnosis in Industrial Domain based on Expert Knowledge and Unitary Traceability Data. IFAC-PapersOnLine, 2015, 48: 2411-2416 CrossRef Google Scholar

[36] Yan A, Wang W, Zhang C. A fault prediction method that uses improved case-based reasoning to continuously predict the status of a shaft furnace. Inf Sci, 2014, 259: 269-281 CrossRef Google Scholar

[37] Han M, Cao Z J. An improved case-based reasoning method and its application in endpoint prediction of basic oxygen furnace. Neurocomputing, 2015, 149: 1245-1252 CrossRef Google Scholar

[38] Ding J, Cao Y, Mpofu E. A hybrid support vector machine and fuzzy reasoning based fault diagnosis and rescue system for stable glutamate fermentation. Chem Eng Res Des, 2012, 90: 1197-1207 CrossRef Google Scholar

[39] Cao D, Zeng S, Li J. Variable universe fuzzy expert system for aluminum electrolysis. Trans Nonferrous Met Soc China, 2011, 21: 429-436 CrossRef Google Scholar

[40] Maji P, Garai P. IT2 Fuzzy-Rough Sets and Max Relevance-Max Significance Criterion for Attribute Selection. IEEE Trans Cybern, 2015, 45: 1657-1668 CrossRef Google Scholar

[41] Yue W, Gui W, Chen X. Knowledge representation and reasoning using self-learning interval type-2 fuzzy Petri nets and extended TOPSIS. Int J Mach Learn Cyber, 2019, 10: 3499-3520 CrossRef Google Scholar

[42] Roda F, Musulin E. An ontology-based framework to support intelligent data analysis of sensor measurements. Expert Syst Appl, 2014, 41: 7914-7926 CrossRef Google Scholar

[43] Bollacker K, Evans C, Paritosh P, et al. Freebase: A collaboratively created graph database for structuring human knowledge. In: Proceedings of the ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2008. 1247--1250. Google Scholar

[44] Auer S, Bizer C, Kobilarov G, et al. DBpedia: a nucleus for a web of open data. In: Proceedings of the 6th International Semantic Web Conference. Berlin: Springer, 2007. 722--735. Google Scholar

[45] Bizer C, Lehmann J, Kobilarov G. DBpedia - A crystallization point for the Web of Data. J Web Semantics, 2009, 7: 154-165 CrossRef Google Scholar

[46] Suchanek F M, Kasneci G, Weikum G, et al. Yago: A core of semantic knowledge. The web conference, 2007. 697-706 https://doi.org/10.1145/1242572.1242667. Google Scholar

[47] Hoffart J, Suchanek F M, Berberich K. YAGO2: A spatially and temporally enhanced knowledge base from Wikipedia. Artificial Intelligence, 2013, 194: 28-61 CrossRef Google Scholar

[48] Mahdisoltani F, Biega J, Suchanek F. Yago3: a knowledge base from multilingual Wikipedias. In: Proceedings of the 7th Biennial Conference on Innovative Data Systems Research (CIDR), 2014. Google Scholar

[49] Lenat D B. CYC: a large-scale investment in knowledge infrastructure. Commun ACM, 1995, 38: 33-38 CrossRef Google Scholar

[50] Speer R, Havasi C. Representing general relational knowledge in ConceptNet 5. Language Resources and Evaluation, 2012. 3679--3686. Google Scholar

[51] Speer R, Chin J, Havasi C. ConceptNet 5.5: An open multilingual graph of general knowledge. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence, 2017. 4444--4451. Google Scholar

[52] Gallagher B. Matching structure and semantics: a survey on graph-based pattern matching. AAAI FS, 2006, 6: 45--53. Google Scholar

[53] Wylot M, Hauswirth M, Cudré-Mauroux P. RDF Data Storage and Query Processing Schemes. ACM Comput Surv, 2018, 51: 1-36 CrossRef Google Scholar

[54] The Neo4j Team. The Neo4j Manual v3.4. 2018. https://neo4j.com/docs/developer-manual/current/. Google Scholar

[55] Angles R, Arenas M, Barceló P. G-CORE: a core for future graph query languages. In: Proceedings of the 2018 International Conference on Management of Data. Houston: ACM, 2018. 1421--1432. Google Scholar

[56] Jang S, Megawati M, Choi J, et al. Semi-automatic quality assessment of linked data without requiring ontology. In: Proceedings of the International Semantic Web Conference (ISWC). Berlin: Springer-Verlag, 2015. 45--55. Google Scholar

[57] Wang W Y, Mazaitis K, Lao N. Efficient inference and learning in a large knowledge base. Mach Learn, 2015, 100: 101-126 CrossRef Google Scholar

[58] Pujara J, Miao H, Getoor L, et al. Knowledge graph identification. In: Proceedings of the 12th International Semantic Web Conference. Berlin: Springer-Verlag, 2013. 542--557. Google Scholar

[59] Chen Y, Goldberg S, Wang D Z, et al. Ontological pathfinding: mining first-order knowledge from large knowledge bases. In: Proceedings of the 2016 ACM SIGMOD International Conference on Management of Data. New York: ACM Press, 2016. 835--846. Google Scholar

[60] Jiang T, Liu T, Ge T, et al. Towards time-aware knowledge graph completion. In: Proceedings of the 26th International Conference on Computational Linguistics. Stroudsburg: ACL, 2016. 1715--1724. Google Scholar

[61] Tay Y, Luu A T, Hui S C. Non-parametric estimation of multiple embeddings for link prediction on dynamic knowledge graphs. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence. Menlo Park: AAAI, 2017. 1243--1249. Google Scholar

[62] Feng J, Huang M, Yang Y, et al. GAKE: Graph aware knowledge embedding. In: Proceedings of the 26th International Conference on Computational Linguistics. Stroudsburg: ACL, 2016. 641--651. Google Scholar

[63] Shi B, Weninger T. ProjE: embedding projection for knowledge graph completion. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence. Menlo Park, 2017. 1236--1242. Google Scholar

[64] Graves A, Wayne G, Reynolds M. Hybrid computing using a neural network with dynamic external memory. Nature, 2016, 538: 471-476 CrossRef ADS Google Scholar

[65] Schlichtkrull M S, Kipf T, Bloem P, et al. Modeling relational data with graph convolutional networks. In: Proceedings of European Semantic Web Conference, 2018. 593--607. Google Scholar

[66] Yang F, Yang Z, Cohen W W, et al. Differentiable learning of logical rules for knowledge base reasoning. In: Proceedings of Neural Information Processing Systems, 2017. 2316--2325. Google Scholar

[67] Guan S P, Jin X L, Jia Y T, et al. Knowledge reasoning over knowledge graph: A survey. Ruan Jian XueBao/Journal of Software, 2018, 29(10): 2966-2994. Google Scholar

[68] Du Z, Meng X, Wang S. Research progress of large-scale knowledge graph completion technology. Sci Sin-Inf, 2020, 50: 551-575 CrossRef Google Scholar

[69] Huang D X, Jiang Y H, Jin Y H. Present research situation, major bottlenecks, and prospect of reflnery industry process control. Acta Automatica Sinica, 2017, 43(6): 902- 916. Google Scholar

[70] Zeng Z, Gui W, Chen X. A mechanism knowledge-driven method for identifying the pseudo dissolution hysteresis coefficient in the industrial aluminium electrolysis process. Control Eng Practice, 2020, 102: 104533 CrossRef Google Scholar

Copyright 2020  CHINA SCIENCE PUBLISHING & MEDIA LTD.  中国科技出版传媒股份有限公司  版权所有

京ICP备14028887号-23       京公网安备11010102003388号