论文信息 - Octane number prediction in a reforming plant

Octane number prediction in a reforming plant

In this work a neural network for the prediction of the complex and nonlinear behaviour of a catalytic reforming of a refinery has been developed. In a fuel refinery reforming is a conversion process to increase the octane number of the desulphurated heavy naphtha in charge. The neural model has been trained and validated on experimental measurements. The results confirmed the suitability of the proposed approach.

Giovanni Muscato | S. Fichera | E. Chibaro

[1] Kumpati S. Narendra,et al. Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[2] Stephen A. Billings,et al. Properties of neural networks with applications to modelling non-linear dynamical systems , 1992 .

[3] Dale E. Seborg,et al. Experience with nonlinear control and identification strategies , 1994 .

[4] John S. Oakland,et al. Statistical Process Control , 2018 .

[5] Giovanni Muscato,et al. Improving back-propagation learning using auxiliary neural networks , 1992 .

[6] Stephen A. Billings,et al. Non-linear system identification using neural networks , 1990 .

[7] Mohamed Azlan Hussain,et al. Review of the applications of neural networks in chemical process control - simulation and online implementation , 1999, Artif. Intell. Eng..

[8] Sheng Chen,et al. Representations of non-linear systems: the NARMAX model , 1989 .

[9] Derek A. Linkens,et al. Back-propagation neural-network based fuzzy controller with a self-learning teacher , 1994 .