Abstract: 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 the dead-zone approach, we propose a new robust
learning algorithm and give theoretical analysis. Real data of crude
oil blending is applied to illustrate the neuro modeling approach.