TY - JOUR
T1 - Artificial intelligence application in a renewable energy-driven desalination system
T2 - A critical review
AU - He, Qian
AU - Zheng, Hongfei
AU - Ma, Xinglong
AU - Wang, Lu
AU - Kong, Hui
AU - Zhu, Ziye
N1 - Publisher Copyright:
© 2021
PY - 2022/1
Y1 - 2022/1
N2 - 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.
AB - 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.
KW - Algorithms
KW - Artificial intelligence
KW - Desalination
KW - Renewable energy
UR - http://www.scopus.com/inward/record.url?scp=85122534771&partnerID=8YFLogxK
U2 - 10.1016/j.egyai.2021.100123
DO - 10.1016/j.egyai.2021.100123
M3 - Review article
AN - SCOPUS:85122534771
SN - 2666-5468
VL - 7
JO - Energy and AI
JF - Energy and AI
M1 - 100123
ER -