Artificial intelligence application in a renewable energy-driven desalination system: A critical review

Qian He, Hongfei Zheng, Xinglong Ma, Lu Wang, Hui Kong*, Ziye Zhu

*此作品的通讯作者

科研成果: 期刊稿件文献综述同行评审

63 引用 (Scopus)

摘要

Artificial intelligence, an emerging technology, widely exists in the field of engineering science and technology. Due to its high efficiency and precision, artificial intelligence is increasingly used in the optimal control of water treatment and seawater desalination. Generally, the design of a desalination system includes four processes: site selection, energy prediction, desalination technology selection and systematic parameter optimization. To a large extent, these choices depend on the experience and relevant criteria of researchers and experts. However, facing the scientific and technological progress and growing expectations, it is impossible to solve such complex nonlinear problems by simple experience and mathematical models, but artificial intelligence is good at this. In this paper, we synthetically analyzed and summarized the application of artificial intelligence in the field of seawater desalination with renewable energy. Artificial intelligence application in desalination is mainly divided into four aspects: expert decision-making, optimization, prediction and control by sequence. The features of artificial intelligence employed in the design of desalination systems not only realize the maximum of efficiency and minimum of cost, but release the human resources. After analyzing the four processes of desalination, it is found that artificial neural network and genetic algorithm are more widespread and mature than other algorithms in dealing with multi-objective nonlinear problems. This paper overviewed the application of artificial intelligence technologies in decision-making, optimization, prediction and control throughout the four processes of desalination designs. Finally, the application and future development prospect of artificial intelligence in the field of seawater desalination are summarized.

源语言英语
文章编号100123
期刊Energy and AI
7
DOI
出版状态已出版 - 1月 2022

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