Mining companies generate volumes of data from equipment and processes, but only a fraction of this data is actually used to improve decision-making. Recent advances in machine learning and data analytics in the mining industry have enabled miners to leverage data from sources within and beyond the value chain to provide real-time decision support and insights about the probability of future events. Key technologies in this space include artificial intelligence and machine learning (AI/ML), simulation modelling, digital twinning and advanced analytics for production optimization and predictive maintenance.
Artificial intelligence refers to computing systems that work and react like humans. The application of machine learning AI has far-reaching potential for the mining industry. By using satellite imagery and geophysical maps, machine learning can help answer questions such as ‘where to explore’ and ‘what lies under the ground’ during the prospecting and exploration phase. Additional applications include the ability to warn operators and maintenance crew of critical equipment downtime in advance.
Digital twinning will give mines the ability to create digitized versions of components, that are updated in real-time with sensors or tags on the physical equipment. The ability to create a digital representation of a physical object or asset can provide important data about the asset’s health. The data gathered can streamline a mine’s maintenance program, by predicting potential outages before they happen, thereby reducing the risk of downtime. Coupled with the Internet of Things, a smart digital twin can even trigger a 3D printer to have its physical replacement ready for the next scheduled operation.
Simulation modelling allows mining companies to analyze their processes in a virtual setting, and project operational performance using what-if scenarios, thereby reducing the time and cost associated with physical testing. Such analysis can highlight current performance levels, bottlenecks and opportunities for improvement.
Advanced analytics can look at immense data sets for trends, and identify opportunities for improvement that humans cannot see. By bringing together all of a mine’s extensive but underused production and process data, advanced analytical techniques can help identify operational bottlenecks or waste patterns, enhance predictive maintenance and increase efficiencies of day-to-day operations.
According to a report by the World Economic Forum on Digital Transformation in the Mining and Metals Industry, though effective analytics are still in their infancy, they are starting to add value to mining and metals operations and are expected to grow in importance after 2025. The potential value addition from Advanced Analytics and Simulation Modelling for the mining and metals industry stand at ~$10.6 billion from 2016 – 2025.
Source: https://rapidbizapps.com/intelligence-analytics-mining-industry
Mine sites are swimming in data yet starving for insights that will make dramatic improvements on their operations. Tapping into their siloed datasets will tap into more efficient ways to extract resources.
Many mining decision-makers have made strides into Industry 4.0 by installing digital measuring tools throughout production. Sites have sensors on all kinds of equipment to monitor all kinds of behavior during each stage; from drilling, to explosives loading, to hauling rock, to processing the rock. Yet, there is often no central database analyzing all of this data.
Big data will affect the mining industry companies and information such as voice recordings, pricing data, social media posts, images, and geo-location information collected are critical assets for the companies to gain new insights into business performance, opportunities and risks. Recently, Singapore government, transportation companies, and IT companies are trying to gather passenger data, which is also a form of big data, for analysis in order to gain new insights to improve commuter experience in boarding the trains to ease transportation woes.
For mining industry, big data can help them in like predicting whether their mining equipment is going to fail by using real-time analytics from both equipment sensors and operational data. These can be derived from the use of predictive analytics which helps to extract real-time data on a variety of operational parameters such as equipment settings and readings from the company’s production control systems. In the case of using the equipment sensors from the “internet of things”, “internet of things” has helped to solve the huge financial burden for mining industry companies. Thus, this can increase the efficiency in business.
Additionally, mining industry companies can apply data analytics in ensuring health, safety, and environmental protection. This is to help the companies in investigating and improving processes and forecasts on management that are related to the above protection issues. Also, the companies need to strengthen their safety efforts by using innovative approaches that extend the safety by providing training and procedures.
With the application of analytics and optimization capabilities to the data, this helps mining industry companies to make better production decisions that ultimately extend the life of mines, improve production, yields, and reduce environmental risks. Thisallows the companies to impress their clients with the best portfolio of contracts. For predictive analytics capabilities, it helps to fill in the gap between data and action by helping management produce reliable conclusions about the current conditions and future events in such areas as asset management and it helps to analyze which mines produce the most profitable output. Lastly, optimization will go through modelling of changes that lead to actionable insights where this is the part that the visualization and modelling starts to come in with the production of dashboard which helps to implement better decision. Now, “internet of things” has indeed helped mining industry increase in efficiency in business and indeed changed the working environment as technology always change the rules of business.
Source: https://blogs.sap.com/2014/06/04/mmforum-how-data-analytics-is-important-to-mining-industry/