Dargam, F. and Perz, E. and Bergmann, S. and Rodionova, E. and Sousa, P. and Souza, F.A.A. and Matias, T. and Ortiz, J.M. and Esteve-Núñez, A. and Ródenas, P. and Zamora, P. (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). pp. 1-20. ISSN 1941-6296
|
Text (Open access)
1.pdf - Published Version Available under License Creative Commons Attribution. Download (1104Kb) | Preview |
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Climate Change Adaptation; Drinking Water; Horizon2020 Project; IPSEpro; Low-energy Process; Machine Learning; MDC; Microbial Desalination Cell; MIDES; Plant Monitoring; Treated Wastewater |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software T Technology > TA Engineering (General). Civil engineering (General) |
| Depositing User: | Belén Barroeta |
| Date Deposited: | 23 Jan 2023 09:04 |
| Last Modified: | 23 Jan 2023 09:06 |
| URI: | http://eprints.imdea-agua.org:13000/id/eprint/1454 |
Actions (login required)
 |
View Item |
