Neural Network-Based Control Strategies Applied to a Fed-Batch Crystallization Process

This paper is focused on issues of process modeling and two model based control strategies of a fed-batch sugar crystallization process applying the concept of artificial neural networks (ANNs). The control objective is to force the operation into following optimal supersaturation trajectory. It is achieved by manipulating the feed flow rate of sugar liquor/syrup, considered as the control input. The control task is rather challenging due to the strong nonlinearity of the process dynamics and variations in the crystallization kinetics. Two control alternatives are considered – model predictive control (MPC) and feedback linearizing control (FLC). Adequate ANN process models are first built as part of the controller structures. MPC algorithm outperforms the FLC approach with respect to satisfactory reference tracking and smooth control action. However, the MPC is computationally much more involved since it requires an online numerical optimization, while for the FLC an analytical control solution was determined.

Authors:

• P. Georgieva
• S. Feyo de Azevedo

Keywords:

• artificial neural networks
• nonlinear model control
• process identification
• crystallization process

References:

[1] J. B. Rawlings, S. M. Miller, and W. R. Witkowski., "Model
identification and control of solution crystallization processes: a
review", Ind. Eng. Chem. Res., 32(7):1275-1296, July 1993.
[2] Nagy Z. K. and R. D. Braatz,. Robust nonlinear model predictive control
of batch processes. AIChE J., 49:1776-1786, 2003.
[3] Fujiwara, M. Z. K. Nagy, J. W. Chew, and R. D. Braatz First-principles
and direct design approaches for the control of pharmaceutical
crystallization. J. of Process Control, 15:493-504, 2005 (invited).
[4] Feyo de Azevedo, S., Chorão, J., Gonçalves, M.J. and Bento, L., 1994,
On-line Monitoring of White Sugar Crystallization through Software
Sensors. Int. Sugar JNL., 96:18-26.
[5] S. Haykin S. (1994), Neural Networks: A comprehensive foundation,
Prentice Hall, UK.
[6] Russell N.T. and H.H.C. Bakker (1997), Modural modelling of an
evaporator for long-range prediction, Artificial Intelligence in
Engineering, 11, 347-355.
[7] Zorzetto L.F.M., R. Maciel Filho and M.R. Wolf-Maciel (2000), Process
modelling development through artificial neural networks and hybrid
models, Computers and Chemical Engineering, 24, 1355-1360.
[8] Georgieva P., Meireles, M. J. and Feyo de Azevedo, S., 2003, KBHM of
fed-batch sugar crystallization when accounting for nucleation, growth
and agglomeration phenomena. Chem Eng Sci, 58:3699-3711.
[9] Georgieva P., Feyo de Azevedo, M. J. Goncalves, P. Ho (2003a).
Modelling of sugar crystallization through knowledge integration. Eng.
Life Sci., WILEY-VCH 3 (3) 146-153.
[10] Georgieva P., J. Peres, R. Oliveira, S. Feyo de Azevedo (2003c): Process
Modelling Through Knowledge Integration Competitive and
Complementary Modular Principles. Workshop on Modeling and
Simulation in Chemical Engineering, 30 June-4 July, Coimbra, Portugal,
Proc. CIM 22, 1-8.
[11] Ditl, P., Beranek L., & Rieger, Z. (1990). Simulation of a stirred sugar
boiling pan. Zuckerind., 115, 667-676.
[12] A.D. Randolph, M.A. Larson, Theory of Particulate Processes -
Analyses and Techniques of Continuous Crystallization, Academic Pres,
1988.
[13] P. Lauret, H. Boyer, J.C. Gatina, Hybrid modelling of a sugar boiling
process, Control Engineering Practice, 8, 2000, 299-310.
[14] Hagan M. T., M. Menhaj (1994), "Training feedforward networks with
the Marquardt algorithm," IEEE Transactions on Neural Networks, vol.
5, no.6, pp. 989-993.
[15] D.C. Psichogios, L. H. Ungar, A hybrid neural network - first principles
approach to process modelling, AIChE J., 38(10), 1992, 1499-1511.
[16] Graefe, J., Bogaerts, P., Castillo, J., Cherlet, M., Werenne, J.,
Marenbach, P., & Hanus, R. (1999). A new training method for hybrid
models of bioprocesses. Bioprocess Engineering, 21, 423-429.
[17] Mayne D.Q., J.B. Rawlings, C.V. Rao, P.O.M. Scokaert (2000),
Constrained model predictive control: stability and optimality,
Automatica, 36 789-814.
[18] Cabrera J.B.D., K.S. Narendra (1999), Issues in the Application of
Neural Networks for Tracking Based on Inverse Control, IEEE
Transactions on Automatic Control Special Issue on Neural Networks
for Control, Identification, and Decision Making, 44(11), 2007-2027.
[19] Haykin S. (1994), Neural Networks: A comprehensive foundation,
Prentice Hall, UK.
[20] Lingji Ch., K.S. Narendra (2001), Nonlinear Adaptive Control Using
Neural Networks and Multiple Models, Automatica, Special Issue on
Neural Network Feedback Control, 37(8), 1245-1255.
[21] Rumelhart, D.E., & McClelland, J. L., (1986). Parallel Distributed
Processing, Cambridge, MA: MIT Press.
[22] Widrow B., & M.E. Hoff, Adaptive switching circuits, IRE WESCON
(New York. Convention Record.), 96--104, 1966.
[23] Hagan M. T., H. B. Demuth, M. H. Beale (1996), Neural
NetworkDesign, Boston, MA: PWS Publishing.
[24] V. Galvanauskas, P. Geogieva, S. Feyo de Azevedo, Dynamic
optimization of industrial sugar crystallization process based on a hybrid
(mechanistic +ANN) model, accepted for publication to IJCNN,
Vancouver, Canada, June 2006.