Jocelyn Bouchard

Automatic Control of Flotation Columns

Since their first commercial application for mineral separation in the early 1980s, flotation columns have become a standard piece of equipment in mineral concentrators particularly for cleaning operations. This chapter presents and discusses the most recent advances in instrumentation and automatic control of flotation columns. It also examines how current industrial practice could benefit from recent academic developments in these areas. A particular emphasis is placed on the development of specific sensors for the continuous monitoring of process operations and their regulation.

Parameterization of Bubble Size Distribution in Flotation Columns

Abstract A bubble detection technique based on Circular Hough Transform (CHT) has been implemented for bubble size distribution (BSD) determination in two-phase (gas-liquid) systems. This technique allows overcoming issues related to the detection of large single bubbles as well as clusters. A high resolution CCD camera is used to capture bubble images. The obtained digital images are automatically pre-conditioned to eliminate the background, eventual noises, and to enhance the contrast. Tests were carried out in a laboratory flotation column using different concentrations of frother and air flow-rates. Results were compared against manual (visual) counting, as well as a commonly used bubble detection method based on circular particle detection (CPD). The CHT-based technique allows proper detection and measurement of bubble clusters, large bubbles, and also incomplete bubbles in the image frame. Results obtained are very similar to those resulting from manual counts. Compared to CPD algorithms, the CHT approach significantly improves D 32 estimation (error of ~3% instead of ~18%) with a comparable processing time. Different methods to represent the resulting BSD are explained. In this paper, the BSD is estimated as a function of a lognormal distribution. Results are compared with manual estimation, showing a good representation of the distribution.

Constrained Nonlinear Predictive Control Based on IMC-Optimization

Abstract A difficulty with constrained nonlinear control is the minimization of the cost function. With complex system representations such as fundamental models, the required optimization algorithm may be complex to implement, setting its parameters may be difficult and the calculation time may be long. To overcome these problems, an innovative optimization algorithm is proposed. The quadratic criterion is written as a function of approximate linear models which makes possible the use of well known and easy to implement optimization techniques. The properties of the algorithm are analyzed and an example illustrates its very good performances. Copyright

Plant Automation for Energy-Efficient Mineral Processing

Mineral processing is one of the most energy-intensive stages of the overall mining beneficiation chain, with an increasing share of the industry footprint. This chapter examines how automation represents a practical means to significantly improve energy efficiency in mineral processing operations. It introduces the fundamentals of automation, hierarchical framework of automation systems, and how the multiple functions can be integrated into an energy management information system. The discussion also explains the rationale of process control and real-time optimization approaches that facilitates lower specific energy requirements from lower variability of key process variables, and determining more appropriate operating points. Case studies are presented to illustrate the current state of the art.

OPTIMIZATION-FREE CONSTRAINED NONLINEAR PREDICTIVE CONTROL – MINERAL PROCESSING APPLICATIONS

Abstract A difficulty with constrained nonlinear control is the minimization of the cost function. With complex system representations such as fundamental models, the required optimization algorithm may be complex to implement, setting its parameters may be difficult and the calculation time may be long. To overcome these problems, an innovative optimization-free predictive control scheme is proposed. The minimization of the cost function is replaced by a simple and easy-to-compute simulation. Two mineral processing applications illustrate the very good performances of this new algorithm.