Modelling of gasoline blending via discrete-time neural networks

Gasoline blending is an important operation in chemical industry. A good model for the blending process is beneficial for supervision operation, prediction of gasoline qualities and realizing model-based optimal control. Gasoline blending process includes static and dynamic properties which are corresponded to thermodynamic and the storage tank respectively. Since the blending does not follow the ideal mixing rule in practice, we propose static and dynamic neural networks to approximate the blending process. Input-to-state stability approach is applied to access new robust learning algorithms of the neural networks. Numerical simulations are provided to illustrate the neuro modeling approaches.