Computational design optimization for unpolluted water
The increased use of pharmaceutical drugs and agriculture chemicals in the last five decades now results in a global contamination of the water cycle. Antibiotics, endocrine disruptors, pesticides are among the most alarming compounds found in wastewaters and even in treated waters. 
Currently, wastewater treatment plants are the most significant barriers to water contamination, but are known to fail in decreasing micropollutants concentrations to reasonable levels. Consequently, there is an urgent need to implement technologies for removal of micropollutants from wastewaters before they reach rivers, groundwater and marine waters.
EDEN MICROFLUIDICS offers a compact, easy-to-deploy, cost-effective technology for micropollutant removal, thus giving access to quality water to the EU and beyond. The system uses microfluidics for treatment of high volume of effluent at low pressures, decreasing significantly energy consumption compared to current techniques. But the fluidic design, while efficient, fails to adapt for the wide range of pollutants and their properties. The fluidic architecture should be dependent on pollutant types, sizes, concentration, ... Designing with such a large array of parameters requires expertise in a specific computational method:  Computational Design Optimisation. 
The objective of the research proposed here is to optimize the design of the devices developed by EDEN by means of computational methods. We will build the necessary physics-based or data-driven models to enable the use of numerical optimization algorithms to determine optimal design solutions for a variety of conditions and requirements. We will employ multidiscisplinary design optimization techniques to account for the interaction of different disciplines entailed in the treatment process, and will also formulate strategies for platform-based design of families of devices to derive different solutions at minimal cost and development lead times.