Multivariable control of grinding plants: a comparative simulation study.
In this paper five multivariable adaptive and classical control strategies have been studied and implemented in a simulator of the copper grinding plant of CODELCO-Andina. The strategies presented were compared and, according to theory, exhibit good behavior. The extended horizon, pole-placement and model reference multivariable adaptive control strategies were formulated in discrete-time and use a model of the plant whose parameters are updated on line using the recursive least squares method along with UD factorization of the covariance matrix and variable forgetting factor. The direct Nyquist array and sequential loop closing techniques were also studied and simulated. The two-by-two multivariable system chosen to represent the grinding plant has the percentage of solids (density) of the pulp fed to the hydrocyclones (which is highly correlated with the percentage of +65 mesh in the overflow of hydrocyclones) and the sump level as output (controlled) variables. The water flow added to the sump and the speed of the pump are its input (manipulated) variables. All the algorithms tested by simulation exhibited good performance and were able to control the grinding plant in a stable fashion. Adaptive algorithms showed better performance than classical techniques, with the extended horizon and pole-placement algorithms proving to be the best. The fact that adaptive algorithms continuously adjust their parameters renders such controllers superior to those based on fixed parameters.
Duarte
Manuel
M
Department of Electrical Engineering, University of Chile, Santiago. mduartem@cec.uchile.cl
Castillo
Alejandro
A
Sepúlveda
Florencio
F
Contreras
Angel
A
Giménez
Patricio
P
Castelli
Luis
L
D000465:Algorithms; D003198:Computer Simulation; D003300:Copper / Q000032:analysis; D005246:Feedback; D008420:Manufactured Materials / Q000032:analysis; D008669:Metallurgy / Q000379:methods*; D008962:Models, Theoretical; D010316:Particle Size; D015203:Reproducibility of Results; D013269:Stochastic Processes
D003300:Copper