The hunt for productivity in mining
Asset optimization is already well under way in the hauling part of the mining value chain. Since transporting material within the mining site can account for as much as 40% of the total energy used in the whole mining operation (without adding any real value to the end product, it must be noted), fleet management and optimization of hauling trucks has been an obvious place to start. A similar focus on optimization and scheduling is also seen in mining operations, where transporting material to port, e.g. by train, is a key bottleneck. Huge operation centers like Rio Tinto’s in Perth are dedicated to the task.
The next obvious place for optimization is the minerals processing plant. With machine OEEs not too uncommonly in the 60% or 70% range and fluctuating recovery rates, there is great promise for large optimization potential.
Identifying barriers to increased production is a key first step to achieving production plans. However, variations in ore characteristics, mine production rates, weather, and dynamic wear of crusher and mill liners are the sources of large, unpredictable fluctuations in many parts of the process. This can result in continuous shifting in the location of the bottleneck in the plant. Adding to this, unfortunate unplanned machine downtime gives plant managers and operators seeking to maximize throughput gray hair. Furthermore, while improvements in the uptime and OEE of a single machine may make the maintenance and reliability managers happier, unless this happens to be a bottleneck resource, there may be little if any improvement in the overall plant productivity. Because of these issues, operations at many mines are over-engineered to accommodate process variability with redundant capacity.
Hence, despite the seemingly large improvement potential, making true step changes in the productivity of minerals processing plants has turned out to be difficult in practice.
The minerals processing plant of the future: complete overhaul or optimization of the existing?
However, new technologies are now enabling two new possible ways to rethink what the minerals processing plant of the future might look like:
The first approach would be a complete overhaul of the flow sheet. Emerging blasting and pre-concentration techniques, such as selective mining, ultra-high-intensity blasting (UHIB), in-pit crushing and conveying (IPCC), and ore sorting, could be used to dramatically change the entire role of the minerals processing plant. By moving comminution upstream or by selecting only part of the material for processing, these technologies have the promise to impact the whole mine-to-mill material flow, improve energy and water efficiency, and change the entire cost structure of minerals processing. Unfortunately, such a major overhaul requires significant capital investment, and can often be practically justified in a greenfield or a large brownfield expansion only. This is not of much help to the poor GM trying to maximize the productivity of his existing assets with a very tight capex budget.
Another option is to start digitally controlling and optimizing the existing minerals processing plant. The obvious benefit of this approach comes from not having to write off all the investments already made in the existing equipment and operations, but to build on them.
Of course, the idea of digital control and optimization is not completely new to minerals processing. Yet, earlier process optimization approaches such as advanced process control have delivered only a partial solution. Sustaining results has been difficult, mainly due to the high degree of maintenance and training needed to keep the optimization engine constantly tuned.
The role of data in mines
Modern mines have a tremendous resource: data collected in historians is plentiful. However, this data is seldom used or analyzed beyond the obvious trending and daily or monthly reporting. Most commonly, mines lack skilled analysts, and key sub-second information is lost due to the compressed nature of the historian data. Equally, mine personnel often lack the deep process and equipment knowledge required to understand all the thousands of data tags and alarms recorded. Any operator who has experienced an alarm flood can attest that situational awareness and understanding can be humanly impossible during a process upset.
However, today, new cloud-based solutions, edge analytics, sensor technologies, increased automation, and secure connectivity enable approaches previously unthinkable with regulatory control or supervisory systems alone. With the ability to collect, store, and analyze vast amounts of machine and process data in the cloud, the process and the machines can be remotely supervised by knowledgeable machine and process experts. Additionally, machine learning algorithms can be built to aid the plant operator, maintenance, and reliability personnel in understanding the true health and performance of each machine and the process overall. Equipment manufacturers, with their intimate knowledge of equipment, can remotely provide deep insights into performance, reliability, and operation.
IoT to deliver a step change in minerals processing
A practical first step in digitalizing the minerals processing plant is to extend the current control systems with an Internet of Things (IoT) solution. This means collecting data from individual minerals processing assets in the cloud and creating both people-based processes and computer-based algorithms to monitor the performance and health of the machines remotely. This is not very different from what some advanced mining companies are already doing with their haul trucks and trains, as described earlier.
The benefits of a correctly executed IoT strategy in minerals processing are visible as increased machine reliability, production, safety, quality, and availability. In addition, the IoT enables the optimization of maintenance processes, moving from corrective maintenance to condition-based and predictive maintenance. Data-driven shutdown planning helps to minimize planned downtime by synchronizing wear schedules, managing ordering of parts, and minimizing the critical path for the shutdown. Essentially, IoT solutions provide a backbone to create and execute a true reliability-centered maintenance strategy.
Starting first from individual units of key equipment, over time, the collected machine data will become useful for process optimization purposes, too. With new sensor technologies, previously unmeasured process loads such, as mill liner wear rate and particle size distribution of circulating loads, or even simpler things, like the unevenness of feed to a screen, can be measured and controlled. Bottleneck resource identification and optimization becomes easier with real-time constraint analysis – this alone could lead to an average 21% increase in annual production volume, according to a Bain & Company study. Granular information on overall plant-wide equipment effectiveness will help to make production planning and scheduling more reliable. Also, MPC algorithms and expert systems could be tuned remotely by an expert to compensate for process drift. Eventually, IoT and control systems will start to converge to cater for holistic process optimization.
Given this backdrop, implementing the IoT in your existing minerals processing operations becomes an important first step on the digitalization journey and your hunt for radically improved productivity. For tips on how you can go about doing this, please refer to my other blog post here.