In this paper, we developed a hybrid model for the steam turbines of a utility system, which combines an improved neural network model with the thermodynamic model. Then, a nonlinear programming (NLP) model of the steam turbine network is formulated by utilizing the developed steam turbine models to minimize the total steam cost for the whole steam turbine network. Finally, this model is applied to optimize the steam turbine network of an ethylene plant. The obtained results demonstrate that this hybrid model can accurately estimate and evaluate the performance of steam turbines, and the significant cost savings can be made by optimizing the steam turbine network operation at no capital cost.