Artificial Intelligence in the Mining Industry
written by: Teressa2016
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Artificial Intelligence in the Mining Industry
Mining is a centuries old process of extracting the geological materials and valuable minerals from earth for ecological and industrial goals. The process is usually used to obtain or recover those elements which can neither be agriculturally produced nor artificially cultured or manufactured in the laboratories. Such items include limestone, dimension stone, chalk, metals, oil shale, coal, sock salt, gemstones, clay, gravel and potash. In a broader perspective, mining could include any non-renewable resource such as natural gas, petroleum, and even water.
Mineral development and exploration can be defined as investigative activities carried out prior to mining. If a mineral deposit is discovered and carefully evaluated and developed, the rewards of successful development and exploration can be significant.
What is the process of mining?
For the local community, effective mineral development and exploration can generate well-paid jobs which would have otherwise not existed. Mining operations are, however, extremely complex.
These projects comprise of numerous interconnected projects which are operating simultaneously in order to deliver refined commodities such as iron ore, gold, and silver. This is a five stage process which has been broken down as follows:
Mining projects mostly begin with the exploration of hidden mineral resources. Companies hire geologists and create a team to search remote areas where the possibility of discovering mineral deposits are high. Numerous methods such as geochemical analysis, geological surface mapping, geophysical measurements and sampling are often carried out during the early stages to discover potential mineral deposits.
2. Mine Site Planning and Design:
Once the team has finished mapping and all the data regarding the mineral resource has been collected, the project can finally move on to the mine site planning and design stage. This stage typically comprises of studying the mine site to determine how the project can be made environmentally safe and sound, socially responsible and economically viable.
After thorough research has been carried out and permits have been approved, the project can move on to the construction phase. This stage involves different processes such as building of roads, employee housing, processing facilities, environmental management systems and several other facilities.
Two of the most common and well known approaches to mining are underground and surface mining. The method selected is dependent on the mineral deposits and the limits imposed by environment, technology, safety and economic concerns.
The very first stage during the production is to recover the minerals. It is a process of extracting the ore from the rock by using different machinery and tools. The second step is to process the recovered minerals. They are processed through mills or crushers in order to separate commercially valued minerals from the ores.
Once they are completely processed, the ores are then dispatched to smelting facilities. Smelting is the final step in the production process. This involves melting the ores in a furnace to extract metal. The ore is poured into different molds which produces bars of ready-to-sell bullions.
5. Termination and Reclamation:
The fifth and final stage in the process of mining is termination and reclamation. Once the resources of the mining site have been exhausted, the process of closing down the site is initiated. All the facilities/equipment present on the property is dismantled. Reclamation is then implemented. Through this process the land is returned to the original stage. The objectives of the reclamation process are as follows:
- Preserving the quality of water
- Ensuring complete public safety and health
- Stabilizing land in order to provide protection against erosion
- Minimizing harmful effects on the environment
- Removal of hazardous materials and waste
- Establishing new vegetation and landforms
What are some considerations a mining company must take into account before they build a mine?
Mineral development and exploration can be considered as an investment. Mining companies invest significant funds into the project in the hopes that future revenue will be sufficient to cover all costs and make an acceptable profit.
Investment projects in the mining sector compete with each other for investment opportunities. These opportunities are both within and outside the mineral sector. The location and level of investment can be determined through the expected costs and revenues. The higher the revenues and the lower the costs, the more attractive the investment opportunity becomes.
In the mining sector, numerous factors can influence the expected risks, costs and revenues. These factors can be grouped into 4 broad categories. These categories have been defined as follows:
1. Geological Factors:
Some of the basic questions a mining company should ask before beginning the process include:
· Does the mineral resource exist in the area?
· What is the quality and quantity of the resource?
Geological risks can be described as the degree and likelihood to which the actual mineralization (quality and quantity) differs from what is anticipated. For instance, what is the probability of a mineral deposit existing in an area undergoing initial geological investigation? Or during the process of mining, what are the chances that the quality of ores differs from what had been expected initially?
2. Environmental, Political and Social Factors:
Can the mineral resource be extracted in ways which are consistent with the policies and preferences of the local communities? Can it be extracted using methods which are consistent with the country’s policies defined for environment protection?
Risks and dangers can be defined as the degree and likelihood to which the actual recovery of minerals differs from what was initially anticipated. This also includes the probability to which the overall business environment, public policies, and public attitudes differ from what was anticipated when the initial investment was made.
3. Technical Factors:
Can a mineral resource be processed and extracted using existing technologies? Technical risks include the possibility that the mineral resource will differ from what was originally predicted. Put simply, these are the technological complications and problems which can be associated with mineral processing, mining and extractive metallurgy.
4. Economic Factors:
Can the mineral resource be extracted with a profit margin?
Economic risks can be defined as the degree to which the costs and revenues differ from what was projected at the time of investment.
Artificial Intelligence in Mining
Machine learning and artificial intelligence can revolutionize the mining industry. Artificial intelligence is a diverse and growing field that studies different algorithms which are able to automatically learn and make predictions based on the available data.
Artificial intelligence now provides numerous new opportunities, new advancements, new applications, and new technologies. Even though artificial intelligence is inspiring, it can also be extremely overwhelming. Following are a few ways through which artificial intelligence can be used for mineral discovery, mineral prospecting, mine reclamation, mine production and mine development.
1. Exploration and Prospecting:
Artificial intelligence in mining could be used to answer the most important question in this category – which is “Where should we begin to explore for minerals?”
i. Classifying Rock Faces:
· It can automatically identify different rock faces by making use of well logging data such as resistivity logging and spontaneous potential logging.
ii. Predicting Mineral Prospectivity:
· It can predict locations of potential ores by utilizing satellite imagery, geochemical maps, aerial photography, geological maps and geophysical maps.
iii. Classifying Lithology
· It can automatically help identify lithology (soil and rock classes) using remote sensing data or multispectral satellite data.
2. Advanced Exploration and Discovery:
Artificial intelligence in mining is used to answer the following question:
“What is present in the ground?”
i. Predicting the Core Drilling Targets
· Artificial intelligence can be used to predict targets for drilling using the previous core drill data, mine site surveys, soil samples and high impact data including diamond drilling, trenching, channel sampling and geophysics.
ii. Classifying the Surface Conditions
· Artificial intelligence can easily identify sub surface folds, fractures and minerals using acoustic signals.
3. Construction/ Development:
Artificial intelligence in mining can also be used to answer the following question:
“How should we build the mine?”
i. Predicting the Construction Phases
· It can easily model the different construction and development phases by using aerial imagery, mineral perspectivity and also the previously used mine site construction designs.
4. Production and Operation:
Artificial intelligence in mining can be used to answer the following question:
“What is the best way to mill, mine and process the ores which have been discovered?”
i. Predicting the Ore Reserves
· Artificial intelligence predicts ore reserves using the drill hole composite samples.
ii. Predicting the Mineral Output
· With artificial intelligence, it is easy to predict the impurities of the ore by using mineral production data.
iii. Automating Mining Vehicles
· Automated vehicles make use of sensors such as cameras, laser range finders, ultrasound, radar and electromagnetic antennas.
iv. Managing the Assets
· Can be used to manage the different mining assets through operational equipment and data.
v. Analyzing the Water Data
· Assess the different water usage and water source patterns by utilizing on-site data.
vi. Predicting Plant Performance, Mill Loads and Downtime
· Artificial intelligence can be used to predict the downtime through processing plant performance and SAG (semi-autogenous grinding) mill overloads along with pump pressure and geological data.
vii. Predicting Machine Failure
· It can be used to predict machine failure beforehand using the data from industrial equipment usage.
viii. Assessing the Ore Fragmentation
· Artificial intelligence can be used to assess ore fragmentation present underground and in the open-pit operations through 3D mapping point cloud data.
ix. Automating Geotechnical Inspections
· It can automatically perform underground wall assessments and inspections of mesh bagging, fractured shotcrete, plate deformation, missing plates and spalling through 3D mapping data.
x. Detecting Missing Machinery Teeth
· It can be used to prevent crusher downtime or destruction to conveyor belts ensuing from dis-engaged teeth/connecters by means of thermal camera imaging.
xi. Decreasing Machinery Blind Spots
· It can prevent equipment crashes with real-time surveillance through camera feeds.
Artificial intelligence in mining can also be used to answer the following question:
“How should we rehabilitate the mining land and protect people, animals and the environment?”
i. Monitoring all the different environmental changes:
· Artificial intelligence programs can be used to monitor the environment for fire, vegetation, water etc. by using remote-sensing technologies such as aerial photography and/or satellite imagery.
ii. Monitoring animal migration:
· It can be used to monitor all the different changes in animal migration trails through utilizing satellite imagery in addition to aerial photography.
iii. Predicting the Various Environmental Risks:
· It can predict environmental dangers using amassed sludge deposits.
iv. Automatically Assessing and Managing Risks:
· It can conduct automatic risk assessments and risk administration by making use of sensor network technologies, meteorological parameters, levels of radiation and data on pollution.
Artificial intelligence has become one of the most important technologies for the process of mining. It provides ease and a lot of different advantages to mine owners. Artificial intelligence can also be used to lower the risks to both the environment and humans. Some of the advantages of artificial intelligence are as follows:
1. Error Reduction:
Artificial intelligence helps to reduce errors and increased accuracy. It can, therefore, be applied to various projects such as the exploration of mines.
Since they are actually machines covered with metal bodies, they are highly resistant and possess the ability to withstand harsh atmospheric conditions typically prevalent underground. In other words, these machines cannot be modified, disfigured or broken down by the harsh environment.
2. Digital Assistants:
Progressive organizations are now commonly using avatars.’ These are digital assistants which can actually network with users, thus saving the necessity for human resources. For synthetic thinkers, emotions might come in the way of coherent thinking which will not be a distraction at all.
This complete absence of emotions allows the robots to think logically and take decisions based on real facts and data. Emotions are directly related to moods which can cloud judgment and also affect human efficiency– this problem is not an issue with artificial intelligence.
3. Repetitive Jobs:
Monotonous jobs which are boring in nature can be carried out easily with the help of artificial intelligence. Machines think and act faster than human beings and can also multitask efficiently. These machines can also be used to carry out hazardous tasks. Unlike humans, these machines can withstand harsh environment and weather conditions. Additionally, their response time and speed is only limited by their architectural design.
Therefore, incorporating artificial intelligence into the mining industry and process is an excellent idea. These machines can be used to decrease the different types of risks and increase the chances of a successful mining exploration.
Technology should be used to simplify life and artificial intelligence does exactly this by helping miners out.