Artificial neural network based modeling and controlling of distillation column system

  • Totok R Biyanto
  • Bambang L Widjiantoro
  • Ayman Abo Jabal
  • Titik Budiati


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). International Journal of Engineering, Science and Technology, Vol. 2, No. 6, 2010, pp. 177-188

Author Biographies

Totok R Biyanto
Engineering Physic Dept. - FTI – ITS Surabaya, Indonesia; 4Universiti Teknologi Petronas, Malaysia
Bambang L Widjiantoro
Engineering Physic Dept. - FTI – ITS Surabaya, Indonesia
Ayman Abo Jabal
College of Engineering, Sudan University of Science & Technology (SUST), Sudan
Titik Budiati
School of Industrial Technology - Universiti Sains Malaysia, Malaysia

Journal Identifiers

eISSN: 2141-2839
print ISSN: 2141-2820