Environmental Engineering Reference
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emitted from the mill varies in intensity as the quantity and average size of the ma-
terial being ground changes. A high noise level indicates an underloaded mill and a
low noise level, an overloaded mill. An underloaded mill leads to an excessive wear
rate of grinding media and mill lining. As a mill becomes overloaded, movement of
the grinding media is increasingly restricted and grinding diminishes.
MillSense™(Outotec) [12] is a non-contact analyzer for grinding mills provid-
ing real-time information on mill charge volume, ball charge, liner wear and grind-
ing media trajectories. Along with mill rotation speed, charge characteristics can
be used to maximize the breakage energy and therefore optimize the grinding effi-
ciency. Charge volume measurement can also be used to avoid both overloading and
underloading.
The Impactmeter [13] is an instrument comprising acoustic sensors, a processor
and software that gathers and deciphers impact signals and transmits them to provide
vital information relating to load impacts in real time and on-line. This information
may be used either directly by the mill operator or by the mill's automatic con-
trol system to make operational adjustments that optimize impact energy and mill
performance while protecting mill linings and grinding media from excessive wear,
degradation and/or breakage.
The SAG Analyzer is an instrument equipped with software that identifies the
charge toe position and shoulder angles on the basis of acoustic signals. Using this
information together with calibrated plant data, the instrument provides on-line and
real-time estimates of mill fill level as a percentage of mill volume and ball charge
and ore charge as percentages of the total mill load. The SAG Analyzer also indi-
cates the dynamic total apparent load density.
FLSmidth Minerals (formerly FFE Minerals) [14] supplies the Impactmeter and
SAG Analyzer either as a combined instrument or as independent, stand-alone units.
7.2.3 Flotation Cell Froth Sensors
Operators of flotation plants frequently monitor cell surface froth visually and make
adjustments based on interpretation of its appearance. They categorize froths into
a number of distinct types and develop appropriate operating strategies for each
one, looking for the ideal froth appearance. This is the basis for proposals to use
image processing to obtain surface froth characterizations [15]. Image processing
sensors provide quantitative measures of certain representative froth characteristics
and offer the option of making automatic control decisions depending exclusively
on them. These measures can be classified as static or dynamic features, the former
including bubble size, bubble count and bubble density and the latter comprising
froth color, froth speed and froth stability.
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