Optimization of regenerative cycle with open feed water heater using genetic algorithms and neural networks

This article determines the operating conditions leading to maximum work in a regenerative cycle with an open feed water heater through a procedure that combines the use of artificial neural networks (ANNs) and genetic algorithms (GAs). Water is an active fluid in the thermodynamical cycle; an objective function is obtained by using vapor enthalpy (a nonlinear function of operating conditions). Utilizing classical methods for maximizing the objective function usually leads to suboptimal solutions. Therefore, this article uses ANNs to estimate the steam properties as a function of operating conditions and GAs to optimize the thermodynamical cycle. The operating conditions are chosen with the aim of gaining maximum work in a boiler for a specific heat. To estimate the thermodynamic properties, an ANN was used to provide the necessary data required in the GA calculation.