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as a dynamic variable to emphasize the effect of the froth depth control-loop on the
estimation. Figure 6.6 shows the effect of gas and wash water rate on the bias rate.
It can be seen that gas rate has a strong impact on bias rate. Unfortunately, the
method has never been tested in industrial columns yet. This subject will be further
discussedinSection6.4.2.
Persechini [36] also developed and tested three soft sensors to monitor froth
depth, bias and air hold-up for a simplified two-phase system. The latter process
variables were inferred using only two flow meters (air and wash water) and two
pressure gages mounted on the column side. The proposed approach is certainly
interesting and inspiring but requires further developments. The following reserva-
tions are worth mentioning:
•
instrumentation would need to be adapted for a slurry application, which repre-
sents the ultimate objective; and
•
validation and accuracy of the bias and gas hold-up measurements were not as-
sessed, even for steady-state operation.
6.3.3 Collection zone Gas Hold-up
Basically three methods have been used to measure the air hold-up in flotation
columns respectively based on: (a) differential hydrostatic pressure, (b) electrical
conductivity, and (c) sound detection, the latter has very recently become available.
Despite the fact that all three methods have been tested in industry, some more than
others, and despite the importance of the measured variable for the performance of
the flotation process, their continuous use for plant monitoring is extremely limited,
and for control purposes, non-existent. Differential hydrostatic pressure is currently
0.25
0.25
(a)
(b)
0.2
0.2
0.15
0.15
0.1
0.1
0.05
Training data
Linear fit
Validation data
Linear fit
0.05
0
0
20
30
40
50
60
0
0.05
0.1
0.15
0.2
0.25
ε
ww
(%)
J
b
estimated (cm/s)
Figure 6.4
(a) Relationship between bias rate and volumetric fraction of wash water (training). (b)
Relationship between measured and estimated bias rates (validation)
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