See part 2 of 3 of a paper written by Vimal Gaba (vimal.gaba@sap.com), Senior Director- Industry Business Unit, Mining, Metals and Mill Products, SAP Labs India and Indranil Som (indranil.som@sap.com), Value Advisor - Industry Value Engineering, Mining, Metals and Mill Products, SAP India
This part will focus on Internet of Things (IoT) and Big Data in mining - aspects of (Industry 4.0: What’s Next, 2017). Future parts will focus on 3D printing, Blockchain and a strong Digital Core. Part 1 focused on Machine Learning - link.
Introduction
Despite facing the slowdowns and global commodity cyclicity, India is a fast-growing economy and demand for minerals remains robust in the country. While mining is often criticized as an industry where innovation and technological changes are resisted and implemented very slowly, the industry is looking at adapting international levels of technology with latest innovations, with a far higher focus. Historically mining is considered to be technically conservative and risk averse, often a bit less aligned with the technological innovations that could significantly impact the industry. The mining industry still conducts its business in mostly the same way as it always has. While mining machinery have become larger, equipment are now more sophisticated, but many mining operations today would be very similar to what it was years ago.
The mining industry is going through an intense period of change and the ability to innovate and improve is becoming indispensable. While the boom and recession are a feature of the mining industry, the response of the industry had been primarily around maximizing volume while continuing with inherent efficiencies in operations during boom phase, while cost-reduction initiatives takes the centre stage during the downturn. It is now being believed that true innovation will drive the next wave of productivity gains and financial growth.
Emerging technologies are set to change the way miners operate over the next decade and help them evolve with newer business models. Delivering improved productivity, cost savings, and safety advancements, these technologies could drive economic transformation in the industry in coming years and also help in becoming competitive globally. We discuss few of these technology trends and the specific impact it could bring about in the mining industry in the subsequent sections.
Internet of Things
Internet of Things (IoT) is the connection of objects such as computing machines, embedded devices, equipment, appliances, and sensors to the Internet. This emerging network technology can potentially transform the mining industry by creating new ways of maintaining mine safety and productivity. The technology involves connecting equipment, fleet and people based on radio frequency identification device (RFID) and sensor technologies while allowing them to automatically transfer and receive data over a network without requiring human intervention. The IoT platform can not only improve traceability and visibility of the entire mining operations but also automate and redefine the maintenance and operation of machines. It could help build newer collaboration models with OEM’s for monitoring via cloud connectivity and networks, enable advanced maintenance process and newer business models. This could lead to a lot of standardization in the space of OEM, operators and service providers while helping with newer automated and highly agile process at the operations level. For mining companies, the key performance indicators is asset uptime. Hence, prevention of equipment failure becomes crucial where the concept of IoT can play a major role. Sensors can detect the status of the equipment (like temperature, pressure, vibrations, speed) wherever it is and using the data that is collected from the M2M sensor along with other data collected such as maintenance history and external sources such as weather for the region, predictive analytics can use models to predict failures before they occur. So the spare parts could be ordered at the right time without requiring expensive express shipping that is spent when equipment fails without warning. This is leading to an inherent shift from routine preventive to predictive maintenance with ability to react at the right time but well in advance.
Another application of IoT technology is the remote operation and monitoring of mines. The adoption of centralized systems for operating, monitoring and controlling the mining or the processing activities from a remote location has been embraced by mining companies across the world. Such remote monitoring of operations ensures maximum efficiency, improved safety, decreased variability and better identification of performance issues. Mining majors like Rio Tinto and BHP Billiton have set up their integrated remote operations centre in Perth for monitoring operations in iron ore mines of Pilbara, about 1500 kms away. Rio Tinto also opened a processing excellence centre in Brisbane to monitor and analyse the processing data in real time from seven of its operations in Mongolia, United States and Australia with the help of huge interactive screens. A team of experts in mineral processing suggest different solutions for optimizing mineral processing at these seven sites.
One of the most rapidly growing application for the IoT is in heavy equipment used in mining. Mining equipment and vehicles are usually enormous and powerful and are some of the largest machines in the world. A person near or on the path of these vehicles could lead to fatal accidents. One of the key goals of IoT in these environments is improving people and equipment safety. To prevent accidents, heavy equipment often incorporates location/proximity sensors and warning technology, such as GPS, radar, video and RF locating devices (on both personnel and equipment) to ensure the safety of construction and mining. Mine automation system, which integrates all the automated physical elements, creates real-time multi-dimensional models from a variety of data sources including the sensors on equipment as well as geological and other data. The system can then be used to optimize the mine’s layout, operation, vehicle paths and so on, co-ordinating all the moving pieces for the most efficient operation. Visualization tools can provide 3D displays of the mine and other related data for use by pit controllers, geologists, drilling and blasting teams, mine planners and supervisors.
Mining vehicles have inbuilt sensors to measures things like oil temperature, levels, contamination, tyre pressures, bearing rotation, vibrations, frame rack, bias and pitch (affected by load and road conditions), engine speed, brake pressure etc. These data are all transmitted remotely to monitoring centres that can be alerted to potential trouble before it actually takes place. Instead of going for regularly scheduled maintenance (e.g. every 1,000 hours of operation), a predictive model based on sensor data can recommend when maintenance should be performed.
IoT is changing the mining industry. It is making it safer, more efficient and more automated. It is making mining jobs more high tech and allowing people to work remotely, with fewer and fewer workers at the mine site. The benefits could be summarized into process level time savings, safety performance, automation advances, predictive maintenance and cost benefits related to energy and high value consumptions. The IoT led journey towards business transformation in mining has just begun.
Big Data Analytics
‘Big data’ could be defined as very large and complex data sets which traditional data processing applications are not able to meaningfully process. Source of big data could be multiple – machine data captured by sensors, email, social media contents, web server logs, and call data records etc. Big data analytics is thus the analytical capability which makes it possible to examine large data sets to uncover hidden patterns, unknown correlations, customer preferences and other useful information. There is large amount of big data getting generated in mining operations and this could impact almost all aspects of mining operations. Big data could be put to use in extraction and processing of ore with an objective of improving efficiency. The complicated processes specially running in mining and processing plants with multiple sensor and L1/L2 automation data can be analyzed to establish input and output correlations of process parameters leading to improved quality and reduced costs. Apart from this, accurate big data analysis can help speed up the mine feasibility stages with fewer critical mistakes made, which can help prevent cost overruns due to too little and incorrect information available at the feasibility stage.
Procurement, is critical for mining companies as it involves planning of spares and services for equipment that operate in remote places globally. The downtime of such vehicles and excavators could have significant financial impact in case of spare or services not being available at right time. Mining companies typically deal with several thousand suppliers and hundreds of thousands of spare parts, generating huge volume of data. Without data-driven systems, companies struggle to ensure right information about their current and future needs of spares and services as well as optimizing inventory of such spares parts. Big data analytics could help better negotiation of prices, do better spend analytics and reduce overall procurement costs.
Another usage for data analytics is in providing safety of the miners. The big data technology could help capture operational, people and sensor data and provides actionable insights based on real-time monitoring of people in mines (location, heart rate, temperature), environment (gas concentration, CO, coal dust, wind speed) and equipment (power, operating pressure, speed) The analysis can help identify risks such as a tunnel collapse or incidents of near misses. With real-time insights and advanced decision making, based on pre-defined safety thresholds and warning alerts, big data analytics can help reduce casualties with optimized evacuation management and thus ensure safer mining operations.
Advanced analytics and big data platforms are leading to breakthroughs in business process efficiency. Some recent reports also noted that powerful data driven analytics can also help to solve previously unsolvable and even unknown problems that undermine efficiency in complex environments. Big data used to look like a buzz word but is rapidly moving to mainstream as its importance for process optimization and business value is on the rise.
Look for Part 3 soon on 3D printing, Blockchain and Digital Core in mining.
This part will focus on Internet of Things (IoT) and Big Data in mining - aspects of (Industry 4.0: What’s Next, 2017). Future parts will focus on 3D printing, Blockchain and a strong Digital Core. Part 1 focused on Machine Learning - link.
Introduction
Despite facing the slowdowns and global commodity cyclicity, India is a fast-growing economy and demand for minerals remains robust in the country. While mining is often criticized as an industry where innovation and technological changes are resisted and implemented very slowly, the industry is looking at adapting international levels of technology with latest innovations, with a far higher focus. Historically mining is considered to be technically conservative and risk averse, often a bit less aligned with the technological innovations that could significantly impact the industry. The mining industry still conducts its business in mostly the same way as it always has. While mining machinery have become larger, equipment are now more sophisticated, but many mining operations today would be very similar to what it was years ago.
The mining industry is going through an intense period of change and the ability to innovate and improve is becoming indispensable. While the boom and recession are a feature of the mining industry, the response of the industry had been primarily around maximizing volume while continuing with inherent efficiencies in operations during boom phase, while cost-reduction initiatives takes the centre stage during the downturn. It is now being believed that true innovation will drive the next wave of productivity gains and financial growth.
Emerging technologies are set to change the way miners operate over the next decade and help them evolve with newer business models. Delivering improved productivity, cost savings, and safety advancements, these technologies could drive economic transformation in the industry in coming years and also help in becoming competitive globally. We discuss few of these technology trends and the specific impact it could bring about in the mining industry in the subsequent sections.
Internet of Things
Internet of Things (IoT) is the connection of objects such as computing machines, embedded devices, equipment, appliances, and sensors to the Internet. This emerging network technology can potentially transform the mining industry by creating new ways of maintaining mine safety and productivity. The technology involves connecting equipment, fleet and people based on radio frequency identification device (RFID) and sensor technologies while allowing them to automatically transfer and receive data over a network without requiring human intervention. The IoT platform can not only improve traceability and visibility of the entire mining operations but also automate and redefine the maintenance and operation of machines. It could help build newer collaboration models with OEM’s for monitoring via cloud connectivity and networks, enable advanced maintenance process and newer business models. This could lead to a lot of standardization in the space of OEM, operators and service providers while helping with newer automated and highly agile process at the operations level. For mining companies, the key performance indicators is asset uptime. Hence, prevention of equipment failure becomes crucial where the concept of IoT can play a major role. Sensors can detect the status of the equipment (like temperature, pressure, vibrations, speed) wherever it is and using the data that is collected from the M2M sensor along with other data collected such as maintenance history and external sources such as weather for the region, predictive analytics can use models to predict failures before they occur. So the spare parts could be ordered at the right time without requiring expensive express shipping that is spent when equipment fails without warning. This is leading to an inherent shift from routine preventive to predictive maintenance with ability to react at the right time but well in advance.
Another application of IoT technology is the remote operation and monitoring of mines. The adoption of centralized systems for operating, monitoring and controlling the mining or the processing activities from a remote location has been embraced by mining companies across the world. Such remote monitoring of operations ensures maximum efficiency, improved safety, decreased variability and better identification of performance issues. Mining majors like Rio Tinto and BHP Billiton have set up their integrated remote operations centre in Perth for monitoring operations in iron ore mines of Pilbara, about 1500 kms away. Rio Tinto also opened a processing excellence centre in Brisbane to monitor and analyse the processing data in real time from seven of its operations in Mongolia, United States and Australia with the help of huge interactive screens. A team of experts in mineral processing suggest different solutions for optimizing mineral processing at these seven sites.
One of the most rapidly growing application for the IoT is in heavy equipment used in mining. Mining equipment and vehicles are usually enormous and powerful and are some of the largest machines in the world. A person near or on the path of these vehicles could lead to fatal accidents. One of the key goals of IoT in these environments is improving people and equipment safety. To prevent accidents, heavy equipment often incorporates location/proximity sensors and warning technology, such as GPS, radar, video and RF locating devices (on both personnel and equipment) to ensure the safety of construction and mining. Mine automation system, which integrates all the automated physical elements, creates real-time multi-dimensional models from a variety of data sources including the sensors on equipment as well as geological and other data. The system can then be used to optimize the mine’s layout, operation, vehicle paths and so on, co-ordinating all the moving pieces for the most efficient operation. Visualization tools can provide 3D displays of the mine and other related data for use by pit controllers, geologists, drilling and blasting teams, mine planners and supervisors.
Mining vehicles have inbuilt sensors to measures things like oil temperature, levels, contamination, tyre pressures, bearing rotation, vibrations, frame rack, bias and pitch (affected by load and road conditions), engine speed, brake pressure etc. These data are all transmitted remotely to monitoring centres that can be alerted to potential trouble before it actually takes place. Instead of going for regularly scheduled maintenance (e.g. every 1,000 hours of operation), a predictive model based on sensor data can recommend when maintenance should be performed.
IoT is changing the mining industry. It is making it safer, more efficient and more automated. It is making mining jobs more high tech and allowing people to work remotely, with fewer and fewer workers at the mine site. The benefits could be summarized into process level time savings, safety performance, automation advances, predictive maintenance and cost benefits related to energy and high value consumptions. The IoT led journey towards business transformation in mining has just begun.
Big Data Analytics
‘Big data’ could be defined as very large and complex data sets which traditional data processing applications are not able to meaningfully process. Source of big data could be multiple – machine data captured by sensors, email, social media contents, web server logs, and call data records etc. Big data analytics is thus the analytical capability which makes it possible to examine large data sets to uncover hidden patterns, unknown correlations, customer preferences and other useful information. There is large amount of big data getting generated in mining operations and this could impact almost all aspects of mining operations. Big data could be put to use in extraction and processing of ore with an objective of improving efficiency. The complicated processes specially running in mining and processing plants with multiple sensor and L1/L2 automation data can be analyzed to establish input and output correlations of process parameters leading to improved quality and reduced costs. Apart from this, accurate big data analysis can help speed up the mine feasibility stages with fewer critical mistakes made, which can help prevent cost overruns due to too little and incorrect information available at the feasibility stage.
Procurement, is critical for mining companies as it involves planning of spares and services for equipment that operate in remote places globally. The downtime of such vehicles and excavators could have significant financial impact in case of spare or services not being available at right time. Mining companies typically deal with several thousand suppliers and hundreds of thousands of spare parts, generating huge volume of data. Without data-driven systems, companies struggle to ensure right information about their current and future needs of spares and services as well as optimizing inventory of such spares parts. Big data analytics could help better negotiation of prices, do better spend analytics and reduce overall procurement costs.
Another usage for data analytics is in providing safety of the miners. The big data technology could help capture operational, people and sensor data and provides actionable insights based on real-time monitoring of people in mines (location, heart rate, temperature), environment (gas concentration, CO, coal dust, wind speed) and equipment (power, operating pressure, speed) The analysis can help identify risks such as a tunnel collapse or incidents of near misses. With real-time insights and advanced decision making, based on pre-defined safety thresholds and warning alerts, big data analytics can help reduce casualties with optimized evacuation management and thus ensure safer mining operations.
Advanced analytics and big data platforms are leading to breakthroughs in business process efficiency. Some recent reports also noted that powerful data driven analytics can also help to solve previously unsolvable and even unknown problems that undermine efficiency in complex environments. Big data used to look like a buzz word but is rapidly moving to mainstream as its importance for process optimization and business value is on the rise.
Look for Part 3 soon on 3D printing, Blockchain and Digital Core in mining.
- SAP Managed Tags:
1 Comment