Numerical Modeling and Optimization of Superheater with Genetic Algorithm

Superheaters tubes are constantly exposed to fouling. Therefore, studying the superheaters by consideration of the covered scale layer inside pipes and sediment layer on it and its effect on the output superheated steam temperature and the amount of injected water into the output superheated steam has special impact. In this research, for 3-D analysis of the heat transfer parameters and flow field characteristics, a commercial mechanical software (ANSYS-CFX 19) is employed. Finally, the calculated average output superheated steam temperature from the superheater has been compared with experimental data. In this way the accuracy of this simulation is valid. subsequently, two-objective optimization is done by Genetic Algorithms. The pressure and temperature of the injected water are considered for the design variable of this optimization. amount reduction of water spray and increasing of the superheater’s efficiency coefficient are considered as objective functions. Finally, the optimization results are analyzed.

Keywords:

Superheater , Fouling , Spray Water , Optimization , Genetic Algorithm