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

被引:58
作者
He, Qian [1 ]
Zheng, Hongfei [1 ]
Ma, Xinglong [1 ]
Wang, Lu [1 ]
Kong, Hui [1 ]
Zhu, Ziye [1 ]
机构
[1] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China
基金
美国国家科学基金会;
关键词
Artificial intelligence; Desalination; Renewable energy; Algorithms; DECISION-SUPPORT-SYSTEM; NEURAL-NETWORK APPROACH; REVERSE-OSMOSIS PLANTS; FUZZY INFERENCE SYSTEM; SOLAR-STILL PRODUCTION; PREDICTIVE CONTROL; OPTIMIZATION TECHNIQUES; SIZING METHODOLOGIES; PERFORMANCE ANALYSIS; IMPROVED AUTOMATION;
D O I
10.1016/j.egyai.2021.100123
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页数:14
相关论文
共 92 条
[1]   Modeling of an RO water desalination unit using neural networks [J].
Abbas, A ;
Al-Bastaki, N .
CHEMICAL ENGINEERING JOURNAL, 2005, 114 (1-3) :139-143
[2]   Model predictive control of a reverse osmosis desalination unit [J].
Abbas, Abderrahim .
DESALINATION, 2006, 194 (1-3) :268-280
[3]   Exergoeconomic optimization of a forward feed multi-effect desalination system with and without energy recovery [J].
Abid, Asad ;
Jamil, Muhammad Ahmad ;
Sabah, Noor Us ;
Farooq, Muhammad Umer ;
Yaqoob, Haseeb ;
Khan, Liaquat Ali ;
Shahzad, Muhammad Wakil .
DESALINATION, 2021, 499
[4]   Artificial neural network approach for predicting reverse osmosis desalination plants performance in the Gaza Strip [J].
Aish, Adnan M. ;
Zaqoot, Hossam A. ;
Abdeljawad, Samaher M. .
DESALINATION, 2015, 367 :240-247
[5]   Predictive modeling of large-scale commercial water desalination plants: Data-based neural network and model-based process simulation [J].
Al-Shayji, KA ;
Liu, YA .
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH, 2002, 41 (25) :6460-6474
[6]   CONCEPTUAL SPECIFICATION FOR IMPROVED AUTOMATION AND TOTAL PROCESS CARE IN LARGE-SCALE DESALINATION PLANTS OF THE FUTURE [J].
ALGOBAISI, DMK .
DESALINATION, 1994, 95 (03) :287-297
[7]   Artificial Intelligence Enabling Water Desalination Sustainability Optimization [J].
Alzu'bi, Shadi ;
Alsmirat, Mohammad ;
Al-Ayyoub, Mahmoud ;
Jararweh, Yaser .
PROCEEDINGS OF 2019 7TH INTERNATIONAL RENEWABLE AND SUSTAINABLE ENERGY CONFERENCE (IRSEC), 2019, :922-925
[8]   Prediction of temperature elevation for seawater in multi-stage flash desalination plants using radial basis function neural network [J].
Aminian, Ali .
CHEMICAL ENGINEERING JOURNAL, 2010, 162 (02) :552-556
[9]   ANN and ANFIS models to predict the performance of solar chimney power plants [J].
Amirkhani, S. ;
Nasirivatan, Sh. ;
Kasaeian, A. B. ;
Hajinezhad, A. .
RENEWABLE ENERGY, 2015, 83 :597-607
[10]  
Amos Bick GO, 2015, DESALIN WATER TREAT, V31, P305