Title :
Octane number prediction in a reforming plant
Author :
Chibaro, E. ; Fichera, S. ; Muscato, G.
Author_Institution :
Ist. di Macchine, Catania Univ., Italy
Abstract :
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
Keywords :
chemical engineering computing; learning (artificial intelligence); neural nets; oil refining; process control; catalytic reforming; desulphurated heavy naphtha; learning; neural network; nonlinear behaviour prediction; octane number; oil refinery; process control; reforming plant; Chemical processes; Economic forecasting; Electrical equipment industry; Feedforward systems; Fuel processing industries; Industrial control; Neural networks; Process control; Production; Refining;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.860805
