Software Tools for System Identification and Control using Neural Networks in Process Engineering
Neural networks offer an alternative approach both
for identification and control of nonlinear processes in process
engineering. The lack of software tools for the design of controllers
based on neural network models is particularly pronounced in this
field. SIMULINK is properly a widely used graphical code
development environment which allows system-level developers to
perform rapid prototyping and testing. Such graphical based
programming environment involves block-based code development
and offers a more intuitive approach to modeling and control task in
a great variety of engineering disciplines. In this paper a
SIMULINK based Neural Tool has been developed for analysis and
design of multivariable neural based control systems. This tool has
been applied to the control of a high purity distillation column
including non linear hydrodynamic effects. The proposed control
scheme offers an optimal response for both theoretical and practical
challenges posed in process control task, in particular when both,
the quality improvement of distillation products and the operation
efficiency in economical terms are considered.
[1] D. Fruehauf and D. Mahoney, Improve Distillation Control Design.
Chemical Engineering Progress, March 1994.
[2] M.A. Hussain, "Review of the applications of neural networks in
chemical process control. Simulation and on-line implementations",
Artificial Intelligence in Engineering, Vol. 13, pp. 55-68, 1999.
[3] H. Demuth, M. Beale and M. Hagan, M Neural Network Toolbox for
use with MATLAB. The Mathworks, 2006.
[4] M. Norgaard, O. Ravn and N. Poulsen, "NNSYSID and NNCTRL
tools for system identification and control with neural networks",
Computing and Control Engineering Journal, Vol. 23, pp. 29-36, 2001.
[5] J. Dabney, T. Harman. Mastering SIMULINK, Prentice Hall, 2004.
[6] G. Cybenko, "Approximation by superposition of sigmoidal
functions," Math. Contr., Signals, Syst., vol. 2, pp. 303-314, 1989.
[7] S. Haykin, Neural Networks: A comprehensive foundation, 2nd ed.
Prentice Hall, 1998.
[8] M.T. Hagan and M. Menhaj, "Training feed-forward networks with the
Marquardt algorithm," IEEE Transactions on Neural Networks, Vol. 5,
pp. 989-993, 1994.
[9] Norgaard, M, O. Ravn, N.K. Poulsen and L.K. Hansen. Neural
Networks for Modelling and Control of Dynamic Systems. Springer
Verlag, 2000.
[10] M. Diehl, I. Uslu, R. Findeisen.,"Real-time optimization for large scale
processes: Nonlinear predictive control of a high purity distillation
column", On Line Optimization of Large Scale System:State of the Art,
Springer-Verlag, 2001.
[1] D. Fruehauf and D. Mahoney, Improve Distillation Control Design.
Chemical Engineering Progress, March 1994.
[2] M.A. Hussain, "Review of the applications of neural networks in
chemical process control. Simulation and on-line implementations",
Artificial Intelligence in Engineering, Vol. 13, pp. 55-68, 1999.
[3] H. Demuth, M. Beale and M. Hagan, M Neural Network Toolbox for
use with MATLAB. The Mathworks, 2006.
[4] M. Norgaard, O. Ravn and N. Poulsen, "NNSYSID and NNCTRL
tools for system identification and control with neural networks",
Computing and Control Engineering Journal, Vol. 23, pp. 29-36, 2001.
[5] J. Dabney, T. Harman. Mastering SIMULINK, Prentice Hall, 2004.
[6] G. Cybenko, "Approximation by superposition of sigmoidal
functions," Math. Contr., Signals, Syst., vol. 2, pp. 303-314, 1989.
[7] S. Haykin, Neural Networks: A comprehensive foundation, 2nd ed.
Prentice Hall, 1998.
[8] M.T. Hagan and M. Menhaj, "Training feed-forward networks with the
Marquardt algorithm," IEEE Transactions on Neural Networks, Vol. 5,
pp. 989-993, 1994.
[9] Norgaard, M, O. Ravn, N.K. Poulsen and L.K. Hansen. Neural
Networks for Modelling and Control of Dynamic Systems. Springer
Verlag, 2000.
[10] M. Diehl, I. Uslu, R. Findeisen.,"Real-time optimization for large scale
processes: Nonlinear predictive control of a high purity distillation
column", On Line Optimization of Large Scale System:State of the Art,
Springer-Verlag, 2001.
@article{"International Journal of Information, Control and Computer Sciences:49421", author = "J. Fernandez de Canete and S. Gonzalez-Perez and P. del Saz-Orozco", title = "Software Tools for System Identification and Control using Neural Networks in Process Engineering", abstract = "Neural networks offer an alternative approach both
for identification and control of nonlinear processes in process
engineering. The lack of software tools for the design of controllers
based on neural network models is particularly pronounced in this
field. SIMULINK is properly a widely used graphical code
development environment which allows system-level developers to
perform rapid prototyping and testing. Such graphical based
programming environment involves block-based code development
and offers a more intuitive approach to modeling and control task in
a great variety of engineering disciplines. In this paper a
SIMULINK based Neural Tool has been developed for analysis and
design of multivariable neural based control systems. This tool has
been applied to the control of a high purity distillation column
including non linear hydrodynamic effects. The proposed control
scheme offers an optimal response for both theoretical and practical
challenges posed in process control task, in particular when both,
the quality improvement of distillation products and the operation
efficiency in economical terms are considered.", keywords = "Distillation, neural networks, software tools,
identification, control.", volume = "2", number = "11", pages = "3647-5", }