Artificial neural network based modeling and controlling of distillation column system

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Artificial neural network based modeling and controlling of distillation column system

Abstract

A Neural Network Internal Model Control (NN- IMC) strategy is investigated, by establishing inverse and forward model based neural network (NN). Further for developing the model has been selected suitable adaptive filter. Two types of NN-based inverse model (i.e. with and without disturbance input) were accurately simulated. The results indicated that the neural networks are capable to establish forward and inverse model rapidly from the couple of input-output open loop data of single distillation column binary system with a good root mean square error (RMSE). The simulation results revealed that NN-IMC with appropriate learning rate – momentum is capable to pursue the set-point changes and to reject the disturbance changes without steady state error or oscillations. NN-IMC with inverse model which contains disturbance input (modified NN-IMC) offer better performance than without it (conventional NN-IMC).

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