Modelling of an Industrial Fluid Catalytic Cracking Unit Using Neural Networks

An artificial neural network (ANN) model for determining the steady-state behaviour of an industrial Fluid Catalytic Cracking (FCC) unit is presented in this paper. Industrial data from a Greek petroleum refinery were used to develop, train and check the model. FCC is one of the most important oil refinery processes. Due to its complexity the modelling of the FCC poses a great challenge. The proposed model is capable of predicting the volume percent of conversion based on six input variables. This work is focused on determining the optimum architecture of the ANN, in order to gain good generalization properties. The results show that the ANN is able to accurately predict the measured data. The prediction errors in both training and validation data sets are almost the same, indicating the capabilities of the model to accurately generalize when presented with unseen data. The neural model developed is also compared to an existing non-linear statistical model. The comparison shows that the neural model is superior to the statistical model.

[1]  Francis J. Doyle,et al.  A dynamic neural network approach to nonlinear process modeling , 1997 .

[2]  Barry Lennox,et al.  Case study investigating the application of neural networks for process modelling and condition monitoring , 1998 .

[3]  Venkat Venkatasubramanian,et al.  A neural network methodology for process fault diagnosis , 1989 .

[4]  Raj K. Sharma,et al.  CATCRAK: A Process Simulator for an Integrated FCC-Regenerator System , 1995 .

[5]  Sohrab Rohani,et al.  Dynamic modeling and simulation of a riser‐type fluid catalytic cracking unit , 1997 .

[6]  Kishan G. Mehrotra,et al.  Elements of artificial neural networks , 1996 .

[7]  S. E. Voltz,et al.  A lumping and reaction scheme for catalytic cracking , 1976 .

[8]  D. C. Psichogios,et al.  Direct and indirect model based control using artificial neural networks , 1991 .

[9]  Carsten Peterson,et al.  An introduction to artificial neural networks , 1991 .

[10]  Josiah C. Hoskins,et al.  Artificial neural network models for knowledge representation in chemical engineering , 1990 .

[11]  Pastora Vega,et al.  Identification and predictive control of a melter unit used in the sugar industry , 1997, Artif. Intell. Eng..

[12]  Pradeep B. Deshpande,et al.  Consider neural networks for process identification , 1995 .

[13]  Dezhao Chen,et al.  Potential Function Based Neural Networks and Its Application to the Classification of Complex Chemical Patterns , 1998, Comput. Chem..

[14]  G. Christensen,et al.  Future directions in modeling the FCC process: An emphasis on product quality , 1999 .

[15]  A. Comrie.  Comparing Neural Networks and Regression Models for Ozone Forecasting , 1997 .

[16]  C. McGreavy,et al.  Characterisation of the behaviour and product distribution in fluid catalytic cracking using neural networks , 1994 .

[17]  Gilbert F. Froment,et al.  ADVANCED MODELING OF RISER-TYPE CATALYTIC CRACKING REACTORS , 1997 .

[18]  A. B. Bulsari,et al.  Neural Networks for Chemical Engineers , 1995 .

[19]  A. I. Lygeros,et al.  Simulation and design of fluid catalytic‐cracking riser‐type reactors , 1997 .

[20]  Ulrich Anders,et al.  Model selection in neural networks , 1999, Neural Networks.

[21]  Babu Joseph,et al.  Exploratory data analysis: A comparison of statistical methods with artificial neural networks , 1992 .

[22]  A. J. Morris,et al.  Towards improved penicillin fermentation via artificial neural networks , 1992 .

[23]  Ka-Yiu San,et al.  Process identification using neural networks , 1992 .

[24]  A. I. Lygeros,et al.  Simulation and design of fluid-catalytic cracking riser-type reactors , 1996 .

[25]  Mark A. Kramer,et al.  Autoassociative neural networks , 1992 .