Supporting Operational Decisions on Desalination Plants from Process Modelling and Simulation to Monitoring and Automated Control with Machine Learning

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.