Optimization of design parameters of CIP spray cleaning nozzle using genetic algorithm
The spray cleaning of surfaces is a standard task in the food and pharmaceutical industries. At present, the development of such nozzles is based on semi-empirical methods, experience and iterative prototyping. This almost makes it prohibitive to develop nozzles for specific customer requirements due to higher time and cost. A Virtual Engineering approach to design and optimize unlimited number of nozzle designs can overcome this. In this work, a parametric study is carried out to recognize design parameters that have maximum impact on flow. Based on this, a Multiobjective optimization code based on Genetic Algorithm is developed to optimize the design parameters of a full cone nozzle. CFD Simulations were used to estimate the objective functions. In future, the work shall be extended by comparing genetic algorithm with other optimization algorithms and replacing expensive CFD simulations with meta-models.