Artificial intelligence (AI) is gradually entering the mining industry. From workflow management to mineral deposit forecasting, AI provides cost-effective and efficient extraction of minerals.
Image Credit: Anatolii Stoiko/Shutterstock.com
What is Artificial Intelligence (AI)?
Artificial intelligence (AI) refers to a machine's ability to make decisions and take actions based on data analysis and trends.
This field aims to develop systems that can learn and reason similarly to humans, use the experience to solve problems, compare and contrast data, and carry out logical tasks.
How is AI Incorporated in Mining for Processing Minerals and Exploration?
Artificial intelligence and data analytics technologies in mining exploration can swiftly and safely identify areas with high mineralization potential and save exploration costs and time.
Artificial intelligence algorithms backed by X-ray data and color sensors are already being used in the mining industry to improve the quantity and quality of the mineral exploration process. Deep neural networks, a subset of AI, can improve the quality of the ore and reduce mining costs by significantly enhancing picture and speech recognition.
With the help of AI-augmented vision technology, drilling data and rock samples can automatically identify the type of minerals with great precision, saving time and effort over manually analyzing and labeling samples from various rocks.
AI is being used by diamond mining businesses to sort and dispose of mineral waste. This method primarily increases the quantity and quality of the diamond recovery process. The algorithms combine sensor and X-ray data resulting in recovering at least 96% of the weight of all diamonds more significant than 1 mm.
Mineral distribution is not uniform in ores. As a result, they are reduced to smaller, uniform particles. Sensors equipped with artificial intelligence algorithms ensure that the size of rocks entering the mill and traveling on the conveyor belt is not too large to cause failure.
Challenges of Processing Mineral and Exploration in Mining
Mineral reserves near the surface are diminishing worldwide, and mining companies have to spend more money than ever to access increasingly thin deposits.
Mineral resources discovered recently tend to be of inferior quality, making extraction more difficult. There has been an increase in difficulty in extracting the mineral resource economically and effectively.
An increasing volume of data is becoming a challenge for mining industries. Only 10% of the data can be used to handle variability and optimize operations because the data is not connected with the value chain.
Mineral processing and exploration add significant carbon emissions to the environment. There is a growing emphasis on ethical mining to reduce the environmental and material imprint of the entire process chain.
The use of artificial intelligence in mining industries is limited by its time to implement. Due to this, it does not instantly generate a profit for shareholders. Lack of understanding and implementation of AI systems with different platforms also presents significant challenges.
What Advantages Does AI Offer?
Mining corporations are now locating economically viable minerals at great depths. Excavation from smaller ore deposits, on the other hand, can be challenging and time-consuming due to the limitations of traditional practices. AI can assist in constructing more accurate models for predicting the type of minerals and locating high concentration deposits, hence saving time and money.
During mining exploration, faster decision-making is needed to ensure the safety of frontline mineworkers. AI can discover process problems and prevent accidents and injuries using real-time quality data and analytics.
Mining operations necessitate a significant amount of data processing, which is still done manually. AI's ability to give quick results by gathering and evaluating data on-site has the potential to greatly streamline workflow while decreasing errors.
Compared with traditional approaches, firms can save up to 80% by using AI and big data to identify new mines. Furthermore, the obtained data can be used in later rehabilitation initiatives that re-establish the area's natural ecology.
IBM is developing data-driven modeling to estimate gold mineralization in a mine. Using data collected from the mining site, the model will calculate an estimate of the area's gold concentration. IBM's subsurface analytics effort in mining will minimize drilling costs, enhance forecasts with little data, and speed up geological insights.
Goldspot Discoveries Inc. is attempting to improve mineral exploration by applying artificial intelligence. These researchers have successfully predicted 86% of the Abitibi Gold Belt's gold resources utilizing geological, topographic, and mineralogical information gathered from only 4% of the region's total surface area.
Artificial intelligence-based mining exploration algorithms are being developed by an Australian startup Earth AI. The startup uses artificial intelligence techniques to locate mineral reserves in Greenfield construction sites. Drones acquire geophysical data that enables autonomous drilling, lowering exploration and drilling expenses.
Since the quantity of new mineral ores has decreased, mining companies have been looking for more efficient and inventive techniques for processing various data types at each stage in their industry. Artificial intelligence is now widely recognized as a tool for the cost-effective and efficient extraction of mineral resources.
By 2035, the mining industry is expected to benefit from a new era of smart mining. Artificial intelligence will be used to achieve new heights in mining, saving mineral raw material producers between $290 billion and $390 billion annually.
References and Further Readings
Mishra, A. K. (2021). AI4R2R (AI for Rock to Revenue): A Review of the Applications of AI in Mineral Processing. Minerals, 11(10), 1118. https://doi.org/10.3390/min11101118
IBM. (2018). Tackling Coronavirus. [Online]. Available at: https://www.ibm.com/news/ca/en/2018/11/26/i702101t77169m61.html (Accessed on 25 April 2022).
Walker, J. (2019). AI in Mining – Mineral Exploration, Autonomous Drills, and More. [Online]. Available at: https://emerj.com/ai-sector-overviews/ai-in-mining-mineral-exploration-autonomous-drills/ (Accessed on 25 April 2022).
Woetzel, J., Sellschop, R., & Chui, M. (2017). Beyond the supercycle: how technology is reshaping resources. McKinsey Global Institute. Retrieved from: http://dln.jaipuria.ac.in:8080/jspui/bitstream/123456789/2878/1/MGI-Beyond-the-Supercycle-Full-report.pdf
Bergh, L. (2016, January). Artificial intelligence in mineral processing plants: An overview. In 2016 International Conference on Artificial Intelligence: Technologies and Applications (pp. 278-281). Atlantis Press. https://doi.org/10.2991/icaita-16.2016.69
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.