Comparative analysis on nonlinear models for ron gasoline blending using neural networks

R. Carreño Aguiler, Wen Yu, J. C. Tovar Rodríguez, M. Elena Acevedo Mosqueda, M. Patiño Ortiz, J. J.Medel Juarez, D. Pacheco Bautista

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The blending process always being a nonlinear process is difficult to modeling, since it may change significantly depending on the components and the process variables of each refinery. Different components can be blended depending on the existing stock, and the chemical characteristics of each component are changing dynamically, they all are blended until getting the expected specification in different properties required by the customer. One of the most relevant properties is the Octane, which is difficult to control in line (without the component storage). Since each refinery process is quite different, a generic gasoline blending model is not useful when a blending in line wants to be done in a specific process. A mathematical gasoline blending model is presented in this paper for a given process described in state space as a basic gasoline blending process description. The objective is to adjust the parameters allowing the blending gasoline model to describe a signal in its trajectory, representing in neural networks extreme learning machine method and also for nonlinear autoregressive-moving average (NARMA) in neural networks method, such that a comparative work be developed.

Original languageEnglish
Article number1750064
JournalFractals
Volume25
Issue number6
DOIs
StatePublished - 1 Dec 2017

Keywords

  • Extreme Learning Machine for Neural Networks
  • Gasoline Blending Process in Line
  • Nonlinear Auto-Regressive-Moving Average for Neural Networks
  • Nonlinear Model Estimation
  • Research Octane Number
  • Variable Structure Control

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