Simulation of the reactor-regenerator-main fractionator fluid catalytic cracking unit using artificial neural networks

The present work it is a successful approach for modelling the dynamic behaviour of the FCC unit, using Artificial Neural Networks (ANN). An analytical model, validated with construction and operation data, has been used to produce a comprehensive input-target set of training data. The novelty of the model consists in that besides the complex dynamics of the reactor-regenerator system, it also includes the dynamic model of the main fractionator. A new five-lump kinetic model for the riser is also included. Consequently, it is able to predict the final production rate of the main products, gasoline and diesel. The architecture and training algorithm used by the ANN are efficient and this is proved by the results obtained both on training set and set of input-target data not met during the training procedure. The same good ANN performance has been obtained by the comparison between dynamic simulations results emerged from the ANN model versus first principle modelling, both using the same randomly varying inputs. The computation time is considerably reduced when using the ANN model, compared to the use of the analytical model. The presented results show the incentives and benefits for further exploiting the ANN model as internal model for Model Predictive Control industrial implementation.