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Paper Count: 33117
Modeling of Crude Oil Blending via Discrete-Time Neural Networks
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.Keywords: Neural networks, modeling, stability, crude oil.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1076590
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