Model-based control of column flotation: Toward industrial application and real-time optimization

"The metallurgical performance of the column flotation process is determined by the concentrate grade and recovery. Although the former can be continuously measured using an on-stream analyzer, the latter must be estimated by a material balance calculation based on seldom verified steady-state assumptions. Consequently, the automatic control and optimization of flotation columns should be performed using secondary variables having a strong influence on the metallurgical performance, such as froth depth, gas hold-up or bubble surface area flux and bias. The aim of this paper is to present a model-based automatic control strategy using froth depth and bias measurements obtained from virtual sensors developed at Laval University. Preliminary pilot plant results on two-phase (water– air) and three-phase system (water – minerals – air) will be presented and eventual industrial implementation of such a strategy as well as the possibility of achieving effective real-time optimization from bias and froth depth measurements will also be discussed.INTRODUCTIONFlotation columns have been widely studied and used in mineral processing for about twenty years. Although a great deal of work has been accomplished to understand the influence of different variables such as froth depth (H), bubble surface area flux (Sb) and bias (Jb) on the metallurgical performance, the measurement of some of these variables is still difficult to be performed – except at the laboratory level – because in most cases, no commercial device yet exists.As a result of this, it is almost impossible to achieve neither efficient control nor optimization of the process at the industrial level, even in open-loop operation. This paper aims at presenting a simple control strategy of froth depth and bias. Description of the sensors used to achieved measurements, preliminary pilot plant results for froth depth control, eventual industrial applications and possibilities for process optimization are also treated."