Control Improvement of a C Sugar Cane Crystallization Using an Auto-Tuning PID Controller Based on Linearization of a Neural Network
The industrial process of the sugar cane crystallization produces a residual that still contains a lot of soluble sucrose and the objective of the factory is to improve its extraction. Therefore, there are substantial losses justifying the search for the optimization of the process. Crystallization process studied on the industrial site is based on the “three massecuites process". The third step of this process constitutes the final stage of exhaustion of the sucrose dissolved in the mother liquor. During the process of the third step of crystallization (Ccrystallization), the phase that is studied and whose control is to be improved, is the growing phase (crystal growth phase). The study of this process on the industrial site is a problem in its own. A control scheme is proposed to improve the standard PID control law used in the factory. An auto-tuning PID controller based on instantaneous linearization of a neural network is then proposed.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1335178Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1146
 R. Ortega, R. Kelly, PID self-tuners: Some theoretical and practice aspects, IEEE Trans. Ind. Electron. 31 (1984) 312.
 C.G. Proudfoot, P.J. Gawthrop, O.L.R. Jacobs, Self-tuning PI control of a pH neutralization process, in: Proc. IEE, Pt-D 130 (1983) 267.
 F. Radke, R. Isermann, A parameter-adaptive PID controller with stepwise parameter optimization, Automatic 23 (1987) 449.
 E.P. Nahas, M.A. Henson, D.E. Seborg, Nonlinear internal model control strategy for neural network models, Comput. Chem. Eng. 16 (1992) 1039.
 M. Nikolaou, V. Hanaguandi, Control of nonlinear dynamical systems modelled by recurrent neural networks, Am. Inst. Chem. Eng. J. 39 (1993) 1890.
 D.C. Psichogios, L.H. Ungar, Direct and indirect model based control using artificial neural networks, Ind. Engin. Chem. Res. 30(1991) 2564.
 K. Hornick, M. Stinchcomb, H. White, Multilayer feedforward networks are universal approximators, Neural Networks, 2, (1989) 359-366.
 J. Chen, T.C. Huang, Applying neural networks to on-line updated PID controllers for nonlinear process control, Journ. Of Proc. Contr. 14 (2004).
 S. Beyou, Proposition d-un algorithme PID ├á paramètres variables, pour l-amélioration de la conduite d-un procédé de cristallisation ondustriel, Thèse de doctorat, Université de la Réunion (2008).