Global optimization of absorption chiller system by genetic algorithm and neural network

The optimal use of fuel and electricity in a direct-fired absorption chiller system is important in achieving economical operation. Previous work on the control schemes mainly focused on the component local feedback control. A system-based control approach, which allows an overall consideration of the interactive nature of the plant, the building and their associated variables is seen to be the right direction. This paper introduces a new concept of integrating neural network (NN) and genetic algorithm (GA) in the optimal control of absorption chiller system. Based on a commercial absorption unit, neural network was used to model the system characteristics and genetic algorithm as a global optimization tool. The results appear promising.