An intelligent aerator algorithm inspired-by deep learning.

Journal: Mathematical biosciences and engineering : MBE
PMID: 31137247

Abstract

Aerator is an indispensable tool in aquaculture, and China is one of the largest aquaculture countries in the world. So, the intelligent control of the aerator is of great significance to energy conservation and environmental protection and the prevention of the deterioration of dissolved oxygen. There is no intelligent aerator related work in practice and research. In this paper, we mainly study the intelligent aerator control based on deep learning, and propose a dissolved oxygen prediction algorithm with long and short term memory network, referred as DopLSTM. The prediction results are used to the intelligent control design of the aerator. As a result, it is proved that the intelligent control of the aerator can effectively reduce the power consumption and prevent the deterioration of dissolved oxygen.

Authors

  • Hong Jie Deng
    School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China.
  • Ling Xi Peng
    School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China.
  • Jia Jing Zhang
    School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China.
  • Chun Ming Tang
    School of Mathematics and information science, Guangzhou University, Guangzhou, 510006, China.
  • Hao Liang Fang
    School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou, 510006, China.
  • Hao Huai Liu
    School of Chemistry, Guangzhou University, Guangzhou 510006, China.

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