Graph embedding?based intelligent industrial decision for complex sewage treatment processes
نویسندگان
چکیده
Intelligent algorithms-driven industrial decision systems have been a general demand for modeling complex sewage treatment processes (STP). Existing researches modeled STP with the use of various neural network models, yet neglecting fact that latent and occasional relations exist inside STP. To deal challenge, this paper proposes graph embedding-based intelligent (GE-STP). The embedding (GE) scheme is employed to enhance feature extraction computing structure utilized simulate uncertain biochemical transformation introduction GE can not only improves fineness spaces, but also representative ability models towards processes. On basis, GE-STP evaluated on real-world data set collected from realistic plant equipped Internet Things devices. And some typical STP, are selected as baseline methods. Three groups experiments show efficiency exceeds baselines about 6%–12%, susceptible parameter changing.
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ژورنال
عنوان ژورنال: International Journal of Intelligent Systems
سال: 2021
ISSN: ['1098-111X', '0884-8173']
DOI: https://doi.org/10.1002/int.22540