From the control of a pollution remediation system to that of a wind turbine via random set learning

As part of the drive to develop more environmentally-friendly mobility solutions, PhD research conducted at IFPEN resulted in a methodology to estimate control parameters in a urea injection-based pollution control system for vehicles (figure 1), where pollutant gas emission standards must be strictly respected [1,2,3]. This methodology was then extended to two other applications in the field of wind turbine control and simulation.