Ore Hardness Research Articles

Evaluation of the Hicom 120 mill at Venetia Mine

The Hicom 120 mill is a high intensity, compact, grinding mill of novel design. This technology has been under evaluation by the De Beers Group for a number of years. Based on positive results from pilot plant testwork, an industrial evaluation programme was embarked upon at Venetia Mine from August 1999 to November 1999. The testwork programme was designed to produce reliable data for the mill's comminution characteristics, particle residence time distributions, and engineering soundness. The testwork was conducted with −25 +4 mm and −8 +4 mm kimberlitic ores of varying hardness. The Hicom mill's response was sensitive to the ore hardness, with throughputs ranging between 4 t/h – 8 t/h, production of −1 mm ranging between 18%–46%, and specific energy requirements ranging between 17–66 kWh/t of −1 mm material produced, for −25 +4 mm feeds. The particle residence time distribution tests showed that the load segregates into a particle size order, and the results indicate that the top-size material (25 mm) has a low probability of exiting the mill. The mechanical reliability of the mill was good, and the unit can be considered to be very robust. The liner wear rate was unexpectedly high, with liners lasting approximately 20–80 hours depending on the ore hardness and feed size.

98/01847 The effect of radiation to enhance the bioconversion of coal : Gazso, L. G. Fuel Process. Technol., 1997, 52, (1–3), 239–245

The use of phenomenological models for on-line decision making represents the next logical step in mineral processing control system design. The key elements required for model-based control are good on-line models, accurate estimation procedures based on precise measurements and inexpensive distributed control hardware to handle stabilizing and optimizing levels of calculation. These elements are now available with the necessary characteristics. This paper discusses the development and implementation of the model-based control concept for mineral processing systems.Four examples of model-based control strategies for mineral processing operations are presented. One is a supervisory strategy for a semi-autogenous grinding mill circuit in which Kalman filter estimates of ball and rock holdup in the mill as well as ore hardness are used. The second is an optimal feedback control strategy to maintain at setpoint the slurry volume in the sump and the product particle size of a rod/ball mill grinding circuit. The third is a model-assisted control strategy which dynamically optimizes feed-rate and particle size for a single-stage grinding circuit. The fourth example is a feed-forward strategy where Kalman filter estimates of ore flotability in the first two cells of a flotation circuit can be used to predict the steady state recovery and concentrate grade. By minimizing an economic objective function involving recovery and grade, the optimal setpoint values of collector, frother and pulp level can be found.In all instances in which the authors and their coworkers have evaluated this concept in plants, it has been found to be very successful.

Model-based control of mineral processing operations

The use of phenomenological models for on-line decision making represents the next logical step in mineral processing control system design. The key elements required for model-based control are good on-line models, accurate estimation procedures based on precise measurements and inexpensive distributed control hardware to handle stabilizing and optimizing levels of calculation. These elements are now available with the necessary characteristics. This paper discusses the development and implementation of the model-based control concept for mineral processing systems. Four examples of model-based control strategies for mineral processing operations are presented. One is a supervisory strategy for a semi-autogenous grinding mill circuit in which Kalman filter estimates of ball and rock holdup in the mill as well as ore hardness are used. The second is an optimal feedback control strategy to maintain at setpoint the slurry volume in the sump and the product particle size of a rod/ball mill grinding circuit. The third is a model-assisted control strategy which dynamically optimizes feed-rate and particle size for a single-stage grinding circuit. The fourth example is a feed-forward strategy where Kalman filter estimates of ore flotability in the first two cells of a flotation circuit can be used to predict the steady state recovery and concentrate grade. By minimizing an economic objective function involving recovery and grade, the optimal setpoint values of collector, frother and pulp level can be found. In all instances in which the authors and their coworkers have evaluated this concept in plants, it has been found to be very successful.

SAG mill operations at Kidston Gold Mines

The purpose of this paper is to outline the design, specification, and operation of the 8.5-m-diam (28-ft-diam) SAG mill commissioned at Kidston Gold Mines during January 1985. Amenability of Kidston ore to SAG milling, coupled with substantial cost advantages, led to the specification of this mill, equipped with variable speed drive to overcome the large variation in ore hardness. Operation of the mill is effected via a number of automatic control loops. Problems experienced with automatic control plus other electrical and mechanical failures have led to design changes and the formulation of future operating strategies.

Identification of Ore Hardness Disturbances in a Grinding Circuit Using a Kalman Filter

A procedure is described for the identification of hardness disturbances in a closed circuit grinding operation based on particle size and circulating load measurements using an Extended Kalman filter. A simplified on-line moael is obtained from a detailed phenomenological circuit description. The procedure is tested computationally using a dynamic simulator parameterized with data obtained in a 0.5 tph computer controlled pilot circuit. Results are discussed in terms of the potential usefulness of the procedure for implementing optimal control strategies.