Modelling of crude oil blending via discrete-time neural networks

Crude oil blending is an important unit operation in petroleum refining industry. A good model for the blending system is beneficial for supervision operation, prediction of the export petroleum quality and realizing model-based optimal control. Since the blending cannot follow the ideal mixing rule in practice, we propose a static neural network to approximate the blending properties. By input-to-state stability and dead-zone approaches, we propose a new robust learning algorithm and give theoretical analysis. Real data is applied to illustrate the neuro modeling approache.