期刊
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
卷 37, 期 12, 页码 10423-10441出版社
WILEY
DOI: 10.1002/int.22540
关键词
complex systems; graph embedding; intelligent industrial decision; neural networks; sewage treatment processes
资金
- Chongqing Natural Science Foundation of China [cstc2019jcyj-msxmX0747]
- National Language Commission Research Program of China [YB135-121]
- Science and Technology Research Project of Chongqing Municipal Education Commission [KJZD-M202000801]
- Innovation Group of New Technologies for Industrial Pollution Control of Chongqing Education Commission [CXQT19023]
- Japan Society for the Promotion of Science (JSPS) [JP18K18044, JP21K17736]
- Key Research Project of Chongqing Technology and Business University [ZDPTTD201917, ctbuyqzx08]
This paper proposes a graph embedding-based intelligent industrial decision system for modeling complex sewage treatment processes. Experimental results show that the efficiency of this system exceeds traditional methods by 6%-12%, and it is not susceptible to parameter changes.
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据