able to effectively process both spatial and temporal
information, resulting in precise predictions of min-
eral percentages. Our proposed approach has a direct
added value on the monitoring of flotation processes.
The model demonstrated a good level of accuracy and
precision, indicating its ability to be generalized on
the whole differential flotation. The use of the Con-
vLSTM network in flotation froth monitoring, show-
cases its potential for similar industrial applications
that require processing of spatial and temporal infor-
mation.
The real time monitoring the flotation froth is a cru-
cial aspect of optimizing and controlling the flotation
process. Our proposed approach offers significant ad-
vantages over existing monitoring techniques, as it is
not only less expensive and low-maintenance but also
provides real-time information on mineral grades.
This makes it a valuable addition to the flotation mon-
itoring process. Once implemented on the zinc cir-
cuit, our proposed soft sensor will be tested for its
ability to accurately monitor the mineral grades of the
CMG differential flotation circuit’s three base miner-
als: lead, copper, and zinc. By combining froth fea-
tures, physio-chemical sensors, and intelligent control
techniques, this innovative approach has the potential
to become a reliable and effective flotation monitor-
ing system. Our proposed approach of using Con-
vLSTM neural networks for real-time monitoring of
mineral grades in flotation froth has significant po-
tential for future industrial applications beyond lead,
copper, and zinc differential flotation sites. Specif-
ically, we will be testing the generalization of our
approach to other mineral compositions using froth
video data, an aspect that will be addressed in future
work. We acknowledge the limited experimental data
used in this study and are committed to conducting
further research to validate the effectiveness of our
proposed solution in a wider range of industrial set-
tings.
ACKNOWLEDGEMENTS
The Smart Connected Mine project, which this re-
search is a part of, was supported by the Moroccan
Ministry of Higher Education, Scientific Research
and Innovation, the Digital Development Agency
(DDA), and the National Center for Scientific and
Technical Research of Morocco (CNRST) through
the Al-Khawarizmi program. The project was a
collaboration between MASCIR (Moroccan Founda-
tion for Advanced Science, Innovation and Research),
REMINEX R&D (an engineering and project man-
agement subsidiary of the MANAGEM Group), UCA
(Cadi Ayyad University), ENSMR (National School
of Mines of Rabat), and ENSIAS (National School
of Computer Science and Systems Analysis at Mo-
hammed V University). We would like to express our
gratitude to the MANAGEM Group and its subsidiary
CMG for providing us with the opportunity to conduct
research, collect and validate data on-site, and for be-
ing an industrial partner in this project.
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