1074 XXXI International Mineral Processing Congress 2024 Proceedings/Washington, DC/Sep 29–Oct 3
The adoption of AI platforms for mining applications
is on the increase. According to GlobalData, patent fillings,
grant approvals, and publications related to AI applications
for the mining sector have increased by 20-fold in the last
five years (Mining Technology, 2022). This explosion of AI
applications is primarily driven by accessibility to large vol-
umes of data, speed of connectivity (bandwidth) and com-
puting power. Most patent fillings were related to industrial
automation and monitoring—primarily by equipment
suppliers to the mining industries. The use of continuous/
online monitoring by various industrial systems exploded
since the German Government’s first conceptual initiative
of Industry 4.0 in 2011, based on their vision of a fully
automated and digitally connected manufacturing system.
Analogous to Industry 4.0, Mining 4.0 aims to deliver
similar expectations of automation and monitoring for the
mining industry. Mining 4.0 aims to connect various sys-
tems and processes with data insights and drive decisions
for increased mining productivity, Figure 4. AI applica-
tions are core to realizing the vision for Mining 4.0. Large
volumes of sensor data are captured and analyzed through
increased connectivity to drive productivity decisions.
Mining operations are becoming safer and more efficient
through autonomous operations and data insights.
One of the critical challenges of adopting AI systems
in Mining 4.0 is the accessibility of quality data. Figure 5
represents a typical response of a process controller in a
process plant. Often, in poorly operated process plants, the
Figure 4. Mining 4.0 Initiatives (Chandramohan, 2020)
Figure 5. Focusing on Stability (Chandramohan, 2021)
The adoption of AI platforms for mining applications
is on the increase. According to GlobalData, patent fillings,
grant approvals, and publications related to AI applications
for the mining sector have increased by 20-fold in the last
five years (Mining Technology, 2022). This explosion of AI
applications is primarily driven by accessibility to large vol-
umes of data, speed of connectivity (bandwidth) and com-
puting power. Most patent fillings were related to industrial
automation and monitoring—primarily by equipment
suppliers to the mining industries. The use of continuous/
online monitoring by various industrial systems exploded
since the German Government’s first conceptual initiative
of Industry 4.0 in 2011, based on their vision of a fully
automated and digitally connected manufacturing system.
Analogous to Industry 4.0, Mining 4.0 aims to deliver
similar expectations of automation and monitoring for the
mining industry. Mining 4.0 aims to connect various sys-
tems and processes with data insights and drive decisions
for increased mining productivity, Figure 4. AI applica-
tions are core to realizing the vision for Mining 4.0. Large
volumes of sensor data are captured and analyzed through
increased connectivity to drive productivity decisions.
Mining operations are becoming safer and more efficient
through autonomous operations and data insights.
One of the critical challenges of adopting AI systems
in Mining 4.0 is the accessibility of quality data. Figure 5
represents a typical response of a process controller in a
process plant. Often, in poorly operated process plants, the
Figure 4. Mining 4.0 Initiatives (Chandramohan, 2020)
Figure 5. Focusing on Stability (Chandramohan, 2021)