MINING INDABA •
ATTRACTING AND RETAINING GENERATION Z IN THE MINERALS INDUSTRY
By Professor Glen Nwaila , director of the Wits Mining Institute at the University of the Witwatersrand
Professor Glen Nwaila draws an analogy between mining companies and species in the evolution of an ecosystem . Mining companies must mutate and acquire new traits to survive , and the education , attraction and retention of young mining professionals is core to the advancement of this ecosystem .
Supplied Professor Glen Nwaila
The industrial world is currently under rapid evolution , driven by compounding market and regulatory pressures . Mining is particularly affected by increasing international market competition for funding , climate change , health and environmental scrutiny , and increasingly more challenging mining conditions . As with all evolutionary systems , a system under evolutionary pressure is a system that adapts by exploring a new niche . Members of that system thrive or perish under evolution , depending on whether they can adapt to new conditions effectively . By drawing an analogy between mining companies and species in an ecosystem , it becomes obvious what the mining companies must do to survive – mutate and acquire new traits while shedding old ones that are no longer relevant . Subsequently , advantageous traits are passed on . But what are ‘ desirable new traits ’ and how does this affect mining professionals ? To answer this question , we must first explore the new niche .
In the past , the mining industry was viewed as a slow-paced adopter of modern technology , but recent advances in technology and a dynamic transition to the 21st century model of mining have proven that mining is competing with several adjacent industries . This new niche is opened by the mass availability of sensors , digital computation , data , and trans-disciplinary techniques such as machine learning . An evolutionary solution had been proposed in the form of a dry lab for the mineral value chain ( Fig . 1 ). The digital transformation in mining is self-sustaining , that is , sensors generate data , which feed computation and transdisciplinary techniques . Almost all the latest innovation and research in mining reside in this new niche . For example , robotic systems and automation require sensing and computation , while
Figure 1 . A dry lab solution to the new niche containing a mass availability of sensors , data , computation and trans-disciplinary techniques . analytics and management systems require data and transdisciplinary techniques . In other words , knowledge and mastery in sensor technology , computation , data , and trans-disciplinary techniques are already highly desirable qualities in all stages of the mining value chain and the life of a mining asset . Since sensors , computation and trans-disciplinary techniques form a positive feedback loop , the niche is growing exponentially .
To build skills for mining ’ s future , it is necessary that we crosswalk desirable qualities of our mining graduates against our current capabilities .
This continues to create tremendous challenges for traditional mining disciplines , because these disciplines tend to be less adaptable and some , such as geosciences , are more physically connected to nature . To build skills for mining ’ s future , it is necessary that we crosswalk desirable qualities of our mining graduates against our current capabilities . The first component is sensor technology . It is inarguable that full-contact drilling , blasting , and sampling are key parts of a typical mining profession training because this includes field and typical laboratory work . Those activities are oriented around scientific and engineering requirements , and such knowledge can be used to guide the design and use of sensors . However , to participate in sensor development and deployment more effectively , engineering and laboratory experience would be required , for example , calibrating sensors and engineering data streams . This is probably more easily integrated into the more quantitative mining disciplines , such as metallurgy ( where mineral processing plants are equipped with both direct and indirect sensors ) and geophysics ( where acoustic sensors are used for physical property analysis already ). Non-contact or minimally contact sensing techniques should become
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African Mining • January 2023 • 35 |