TY - GEN
T1 - Modelling of crude oil blending via discrete-time neural networks
AU - De Rubio, José Jesús
AU - Yu, Wen
PY - 2004
Y1 - 2004
N2 - 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 input-to-state stability and dead-zone approaches, we propose a new robust learning algorithm and give theoretical analysis. Real data is applied to illustrate the neuro modeling approache.
AB - 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 input-to-state stability and dead-zone approaches, we propose a new robust learning algorithm and give theoretical analysis. Real data is applied to illustrate the neuro modeling approache.
UR - http://www.scopus.com/inward/record.url?scp=24644437713&partnerID=8YFLogxK
M3 - Contribución a la conferencia
AN - SCOPUS:24644437713
SN - 0780385314
T3 - 2004 1st International Conference on Electrical and Electronics Engineering, ICEEE
SP - 427
EP - 432
BT - 2004 1st International Conference on Electrical and Electronics Engineering, ICEEE
T2 - 2004 1st International Conference on Electrical and Electronics Engineering, ICEEE
Y2 - 8 September 2004 through 10 September 2004
ER -