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

Fatima C.C. Dargam, Erhard Perz, Stefan Bergmann, Ekaterina Rodionova, Pedro Sousa, Francisco Alexandre A. Souza, Tiago Matias, Juan Manuel Ortiz, Abraham Esteve-Nuñez, Pau Rodenas, Patricia Zamora Bonachela

Source Title: International Journal of Decision Support System Technology (IJDSST)15(2)

ISSN: 1941-6296|EISSN: 1941-630X|EISBN13: 9781668487426|DOI: 10.4018/IJDSST.315639

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MLA

Dargam, Fatima C.C., et al. "Operational Decision-Making on Desalination Plants: From Process Modelling and Simulation to Monitoring and Automated Control With Machine Learning." IJDSST vol.15, no.2 2023: pp.1-20. http://doi.org/10.4018/IJDSST.315639

APA

Dargam, F. C., Perz, E., Bergmann, S., Rodionova, E., Sousa, P., Souza, F. A., Matias, T., Ortiz, J. M., Esteve-Nuñez, A., Rodenas, P., & Bonachela, P. Z. (2023). Operational Decision-Making on Desalination Plants: From Process Modelling and Simulation to Monitoring and Automated Control With Machine Learning. International Journal of Decision Support System Technology (IJDSST), 15(2), 1-20. http://doi.org/10.4018/IJDSST.315639

Chicago

Dargam, Fatima C.C., et al. "Operational Decision-Making on Desalination Plants: From Process Modelling and Simulation to Monitoring and Automated Control With Machine Learning," International Journal of Decision Support System Technology (IJDSST) 15, no.2: 1-20. http://doi.org/10.4018/IJDSST.315639

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Abstract

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.