Gasoline Blending System Modeling via Static and Dynamic Neural Networks
نویسندگان
چکیده
Gasoline blending is an important unit operation in gasoline industry. A good model for the blending system is beneficial for supervision operation, prediction of the gasoline qualities and realizing model-based optimal control. Gasoline blending process involves two types proprieties: static blending property and dynamic property of blending tanks. Since the blending cannot follow the ideal mixing rule in practice. We propose static and dynamic neural networks to approximate the two types of blending properties. Input-to-state stability approach is applied to access robust learning algorithms of the two neural networks. Numerical simulations are provided to illustrate the neuro modeling approaches.
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