Journal article 1047 views 316 downloads
Can machine language and artificial intelligence revolutionize process automation for water treatment and desalination?
Saif Al Aani, Talal Bonny, Shadi W. Hasan, Nidal Hilal
Desalination, Volume: 458, Pages: 84 - 96
Swansea University Author: Nidal Hilal
Microsoft Word | Accepted ManuscriptDownload (618.03KB)
DOI (Published version): 10.1016/j.desal.2019.02.005
Artificial intelligence (AI) is a powerful tool that is commonly applied in engineering multi-disciplines owing to its functionality to resolve real-world problems where deterministic solutions are arduous to achieve. Revolution in water treatment and desalination process automation has been emergin...
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Artificial intelligence (AI) is a powerful tool that is commonly applied in engineering multi-disciplines owing to its functionality to resolve real-world problems where deterministic solutions are arduous to achieve. Revolution in water treatment and desalination process automation has been emerging recently. Several challenges are present in the water sector related to data structur-ing and smart water services through which AI would have great potential once those issues are addressed. The distinctive tools of AI, mainly; artificial neural networks (ANNs), as a regression model, and genetic algorithm (GA), as one of the global optimization techniques, have been im-mensely applied in desalination and water treatment for multi-purpose applications. Modelling desalination and water treatment processes and optimizing the operating condition are few among the many applications. In the current review, paramount applications of AI tools in desali-nation and water treatment have been thoroughly reviewed. In addition, benchmarking ANNs with the conventional modelling approaches were highlighted, along with the shortcomings and challenges expected to associate with these common tools in some complex nature practical ap-plication. It was concluded that the use of AI tools will undoubtedly pave the way in the water sector towards better operation, process automation, and water resources management in an in-creasingly volatile environment.
Artificial intelligence, desalination, machine learning, artificial neural network, genetic algorithms, process automation
Faculty of Science and Engineering