Abstract
This paper summarizes some of the work carried out within the Horizon 2020 project MIDES (MIcrobial DESalination for low energy drinking water) (The MIDES project (http://midesh2020.eu/) has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nº 685793 [1].), which is developing the world’s largest demonstration of a low-energy system to produce safe drinking water. The work in focus concerns the support for operational decisions on desalination plants, specifically applied to a microbial-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 automated control, which employs techniques of Machine Learning.
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Notes
- 1.
For instance: Chemical coagulation, Sedimentation and Filtration.
- 2.
Pilot demo-site in Dénia (Spain) was already launched in November 2019.
- 3.
Pilot demo-site in Tenerife (Spain) is still under construction and is planned to be launched early in 2020.
- 4.
The installation schedule of the Pilot demo-site outside Europe still needs to be confirmed.
- 5.
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Acknowledgment
The work presented has been partially developed within the EU Horizon 2020 research project MIDES (MIcrobial DESalination for low energy drinking water), under grant agreement Nº 685793. The authors of this paper compose a sub-set of the full team in the MIDES project consortium and they wish to acknowledge the invaluable work done by all project partners.
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Dargam, F. et al. (2020). Supporting Operational Decisions on Desalination Plants from Process Modelling and Simulation to Monitoring and Automated Control with Machine Learning. In: Moreno-Jiménez, J., Linden, I., Dargam, F., Jayawickrama, U. (eds) Decision Support Systems X: Cognitive Decision Support Systems and Technologies. ICDSST 2020. Lecture Notes in Business Information Processing, vol 384. Springer, Cham. https://doi.org/10.1007/978-3-030-46224-6_12
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