Artificial neural network based system identification and model predictive control of a flotation column

The paper describes the design of a neural network based model predictive controller for controlling the interface level in a flotation column. For the system identification, the tailings valve opening is subjected to a pseudo-random ternary signal and response of the interface level is recorded over a period of time. The data so generated is used to develop a dynamic feed forward neural network model. The model uses two past values and one present value of the tailings valve opening as well as interface level as inputs and predicts the future interface level. This model is used for the design of a model predictive controller to control the interface level. The controller was tested both for liquid–gas system as well as liquid–gas–solid system and was found to perform very satisfactorily. The performance of the controller was compared with that of a conventional PI controller for a two-phase system and was found to be better.