Operational Decision-Making on Desalination Plants: From Process Modelling and Simulation to Monitoring and Automated Control With Machine Learning.

Autor: Dargam, Fatima, Perz, Erhard, Bergmann, Stefan, Rodionova, Ekaterina, Sousa, Pedro, Souza, Francisco Alexandre A., Matias, Tiago, Ortiz, Juan Manuel, Esteve-Nuñez, Abraham, Rodenas, Pau, Bonachela, Patricia Zamora
Zdroj: International Journal of Decision Support System Technology; Jul-Dec2023, Vol. 15 Issue 2, p1-20, 20p
Abstrakt: This paper describes some of the work carried out within the Horizon 2020 project MIDES (MIcrobial DESalination for low energy drinking water), which is developing the world's largest demonstration of a low-energy sys-tem to produce safe drinking water. The work in focus concerns the support for operational decisions on desalination plants, specifically applied to a mi-crobial-powered approach for water treatment and desalination, starting from the stages of process modelling, process simulation, optimization and lab-validation, through the stages of plant monitoring and automated control. The work is based on the application of the environment IPSEpro for the stage of process modelling and simulation; and on the system DataBridge for auto-mated control, which employs techniques of Machine Learning. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index