Automated mineralogy helps mining teams make faster, more reliable processing decisions when ore behavior changes and operating constraints tighten. In plant environments, its value lies in turning mineral characterization into decision-ready data that connects directly to performance.
In this interview, mineralogist Braam Smit discusses how he built a career in applied mineralogy, explains what makes automated mineralogy effective in practice, where instrument downtime, cluttered reporting, or non-representative datasets can hinder decision-making, and highlights why reporting quality is just as important as measurement.
Braam Smit graduated as a geologist from the University of Pretoria and began his career in exploration geology. He later worked as a mine geologist before specializing in mineralogy. Over 30 years, he has worked on diamonds, asbestos, platinum, iron ore, copper, and gold. His experience spans five international mining companies, where he led mineralogy programs at both mine sites and centralized laboratory facilities.
Braam’s career reflects sustained engagement with applied mineralogy, instrumentation, and operational problem-solving.
"I just always had a passion for science, in particular geology and mineralogy. I have also loved microscopes from an early age. I have never worked in academia and have always held mineralogy roles in industry. Applied mineralogy is an extremely powerful tool that can make a difference in many areas; it is very rewarding to be part of that."
Early mistakes often shape the standards people carry throughout their careers. For Braam, one misidentification became a lasting reminder of the need for careful mineralogical interpretation.
"I once misidentified a mineral of significance in a sample, and that taught me to always respect the complexities of minerals."
His point is clear: automation can improve speed, but confidence still depends on understanding the limits of the data.
Asked which skill proved more important than expected, Braam pointed to the ability to work across disciplines.
"The ability to tap into various scientific disciplines when looking at minerals. You need to know your geology, but also understand chemistry, physics, and statistics."
In practice, effective mineralogy depends on combining geological knowledge with chemical, physical, and statistical understanding to produce results that are both accurate and useful.
Working close to operations sharpened Braam’s view of how strongly mineralogy influences plant performance.
"An understanding of the day-to-day operation at an ore processing plant and how significant the role of mineralogy is in its performance."
He defines effective mineralogy in practical terms: "The results need to be fit for purpose. They must be reliable, timely, and relevant to the scope of work."
This is where automated mineralogy delivers the most value: when mineralogical insight is tightly linked to operational decisions.
Across geology, metallurgy, and planning, Braam sees one recurring theme: how minerals respond to processing conditions.
"There is a great variety of questions, but they mostly center around the interaction between minerals and plant processes."
He also warns against collecting more information than the decision actually requires.
"Ore characterization can easily become too comprehensive and cluttered with information that is not relevant."
For plant teams, the value of the dataset lies not in volume, but in whether it supports a specific processing decision.
“In a production laboratory, the challenge is always to get meaningful, high-quality results to operations as soon as possible. Instrument downtime will definitely define a tough week. Good weeks happen when all systems work smoothly, and sample flow is uninterrupted.”
Braam highlights two essential checks: does the dataset answer the operational question, and is the dataset representative of the sample? These two tests help keep automated mineralogy outputs decision-ready, even under production pressure.
Asked what problem he wanted to solve by introducing the system, Braam described Tescan TIMA™ as a mature platform that supports robust, repeatable workflows. He said it stood out for its maturity, speed, robustness, reliability, and powerful reporting capabilities.
"I am always amazed by how powerful the reporting side of the software is."
Rather than prioritizing a single metric, he emphasizes the value of a complete dataset.
“Not one, but the full dataset is required: modal mineralogy, liberation, grain size, particle size, association data, and elemental deportment.”
He connects that to plant improvement. In his view, the value of Tescan TIMA™ is that it directly shows what processing is doing to the minerals, making it easier to identify where improvements are needed.
That closes the gap between analysis and action and helps teams refine processing conditions with fewer iterations.
Looking ahead, Braam points to the wider ecosystem around automated mineralogy, and not just the hardware.
“There is definitely room for better library development tools. Faster turnaround and better data systems will probably come as hardware improves over time.”
For those entering the field, Braam emphasizes strong mineralogy fundamentals and meaningful exposure to adjacent sciences.
“It is important to gain a sound understanding of mineralogy. Also, make sure you have reasonable exposure to other science disciplines like physics, chemistry, and statistics during your studies.”
He is equally direct about what cannot be overlooked. He warned that instruments fall short when developers underestimate mineralogical complexity or rely too heavily on limited proxies, such as chemical signatures alone, optical reflection alone, or measurements from too small a portion of the sample. He also argued that artificial intelligence and extrapolative algorithms cannot compensate for poor counting statistics, and that weak instrument design can financially harm end users and damage the credibility of the field.
As routine measurements become more automated, Braam sees the mineralogist’s role expanding rather than shrinking.
"As measurements become increasingly automated, mineralogists have more time to spend on stakeholder engagement."
That shift matters because mineralogists are often the link between data and decisions.
"Process engineers often have problems without realizing mineralogy can provide the answer."
Asked what separates excellent mineralogists from average ones, he gave a concise answer:
“I would say curiosity. It is curiosity that drives a mineralogist to break a problem down and determine what mineralogical information holds the key to solving a specific issue.”
For those concerned about the cyclical nature of mining, his advice is to stay committed to the field and remain resilient. Cyclicality is not unique to mining.
Braam closes with a forward-looking view of what mineralogy can contribute across geology and metallurgy.
“Mineralogists today have very powerful analytical tools and can answer many more questions than was previously possible. So, it is worth asking mineralogists the difficult questions; we have more solutions at hand than many of our colleagues realize.”
As reporting from systems such as Tescan TIMA™ becomes more advanced, more information relevant to geometallurgical modeling and mine planning becomes accessible, helping operations build stronger ore characterization models over time.
Braam’s perspective brings mineralogy back to its core purpose: supporting better decisions in complex, changing operations. As automated mineralogy systems such as Tescan TIMA™ advance, the goal is not simply to generate more data. It is to deliver results that explain plant behavior and guide the next processing decision.
For geologists, metallurgists, plant teams, and those entering the field, the message is practical: focus on the questions that matter, build datasets that reflect real operating conditions, and ensure every result is ready for use.