TY - JOUR
T1 - Comparative analysis on nonlinear models for ron gasoline blending using neural networks
AU - Carreño Aguiler, R.
AU - Yu, Wen
AU - Tovar Rodríguez, J. C.
AU - Mosqueda, M. Elena Acevedo
AU - Ortiz, M. Patiño
AU - Juarez, J. J.Medel
AU - Bautista, D. Pacheco
N1 - Publisher Copyright:
© 2017 World Scientific Publishing Company.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - 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.
AB - 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.
KW - Extreme Learning Machine for Neural Networks
KW - Gasoline Blending Process in Line
KW - Nonlinear Auto-Regressive-Moving Average for Neural Networks
KW - Nonlinear Model Estimation
KW - Research Octane Number
KW - Variable Structure Control
UR - http://www.scopus.com/inward/record.url?scp=85031404870&partnerID=8YFLogxK
U2 - 10.1142/S0218348X17500645
DO - 10.1142/S0218348X17500645
M3 - Artículo
SN - 0218-348X
VL - 25
JO - Fractals
JF - Fractals
IS - 6
M1 - 1750064
ER -