Graph embedding‐based intelligent industrial decision for complex sewage treatment processes

International Journal of Intelligent SystemsVolume 37, Issue 12 p. 10423-10441 RESEARCH ARTICLE Graph embedding-based intelligent industrial decision for complex sewage treatment processes Zhiwei Guo, Zhiwei Guo orcid.org/0000-0001-8868-6913 School of Artificial Intelligence, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, ChinaSearch for more papers by this authorYu Shen, Yu Shen orcid.org/0000-0002-8207-8375 School of Artificial Intelligence, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, ChinaSearch for more papers by this authorAli Kashif Bashir, Corresponding Author Ali Kashif Bashir [email protected] orcid.org/0000-0003-2601-9327 Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK Correspondence Keping Yu, Global Information and Telecommunication Institute, Waseda University, Shinjuku, Tokyo 169-8050, Japan. Email: [email protected] Ali Kashif Bashir, Department of Computing and Mathematics, Manchester Metropolitan University, UK. Email: [email protected]Search for more papers by this authorKeping Yu, Corresponding Author Keping Yu [email protected] orcid.org/0000-0001-5735-2507 Global Information and Telecommunication Institute, Waseda University, Shinjuku, Tokyo, Japan Correspondence Keping Yu, Global Information and Telecommunication Institute, Waseda University, Shinjuku, Tokyo 169-8050, Japan. Email: [email protected] Ali Kashif Bashir, Department of Computing and Mathematics, Manchester Metropolitan University, UK. Email: [email protected]Search for more papers by this authorJerry Chun-wei Lin, Jerry Chun-wei Lin orcid.org/0000-0001-8768-9709 Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, NorwaySearch for more papers by this author Zhiwei Guo, Zhiwei Guo orcid.org/0000-0001-8868-6913 School of Artificial Intelligence, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, ChinaSearch for more papers by this authorYu Shen, Yu Shen orcid.org/0000-0002-8207-8375 School of Artificial Intelligence, National Research Base of Intelligent Manufacturing Service, Chongqing Technology and Business University, Chongqing, ChinaSearch for more papers by this authorAli Kashif Bashir, Corresponding Author Ali Kashif Bashir [email protected] orcid.org/0000-0003-2601-9327 Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK Correspondence Keping Yu, Global Information and Telecommunication Institute, Waseda University, Shinjuku, Tokyo 169-8050, Japan. Email: [email protected] Ali Kashif Bashir, Department of Computing and Mathematics, Manchester Metropolitan University, UK. Email: [email protected]Search for more papers by this authorKeping Yu, Corresponding Author Keping Yu [email protected] orcid.org/0000-0001-5735-2507 Global Information and Telecommunication Institute, Waseda University, Shinjuku, Tokyo, Japan Correspondence Keping Yu, Global Information and Telecommunication Institute, Waseda University, Shinjuku, Tokyo 169-8050, Japan. Email: [email protected] Ali Kashif Bashir, Department of Computing and Mathematics, Manchester Metropolitan University, UK. Email: [email protected]Search for more papers by this authorJerry Chun-wei Lin, Jerry Chun-wei Lin orcid.org/0000-0001-8768-9709 Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, NorwaySearch for more papers by this author First published: 27 June 2021 https://doi.org/10.1002/int.22540Citations: 19Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Abstract Intelligent algorithms-driven industrial decision systems have been a general demand for modeling complex sewage treatment processes (STP). Existing researches modeled complex STP with the use of various neural network models, yet neglecting the fact that latent and occasional relations exist inside complex STP. To deal with the challenge, this paper proposes graph embedding-based intelligent industrial decision for complex STP (GE-STP). The graph embedding (GE) scheme is employed to enhance feature extraction and neural computing structure is utilized to simulate uncertain biochemical transformation inside STP. The introduction of GE can not only improves the fineness of feature spaces, but also improves the representative ability of models towards complex industrial processes. On this basis, the GE-STP is evaluated on a real-world data set collected from a realistic sewage treatment plant equipped with a set of Internet of Things devices. And some typical neural network models that have been utilized for modeling complex STP, are selected as baseline methods. Three groups of experiments show that efficiency of the GE-STP exceeds baselines about 6%–12%, and that the GE-STP is not susceptible to parameter changing. CONFLICT OF INTERESTS The author declares that there is no conflict of interests. Citing Literature Volume37, Issue12December 2022Pages 10423-10441 RelatedInformation