Graph embedding‐based intelligent industrial decision for complex sewage treatment processes. Issue 12 (27th June 2021)

• Record Type: Journal Article
• Authors: Guo, Zhiwei; Shen, Yu; Bashir, Ali Kashif; Yu, Keping; Lin, Jerry Chun‐wei
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 12(2022)
• Page Start: 10423
• Page End: 10441
• Publication Date: 2021-06-27
• Subjects: complex systems -- graph embedding -- intelligent industrial decision -- neural networks -- sewage treatment processes; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22540
• Languages: English
• ISSNs: 0884-8173

Abstract

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.