It’s not all about exploration;
How optimizing your milling circuit can lead to big savings
Production teams have reason to feel jealous these days. It seems that their exploration colleagues get all the exciting new technology, such as A.I. for prospecting new deposits and virtual reality for 3D models. But there are huge challenges in mineral processing that are still waiting to be solved, which if addressed could result in significant improvements in throughput, revenue and profitability.
As an example, take comminution, which is the process of crushing, grinding and pulverizing ore into material small enough to liberate the underlying minerals. It is an incredibly energy intensive activity that can account for up to half of all mine-site energy consumption, and a staggering three per cent of global electricity consumption. Improvements in comminution can have a huge impact on operating costs and Greenhouse Gas (GHG) emissions. And organizations are starting to take notice; as shown by the $5 million Crush It! Challenge recently launched by the Canadian government.
In fact, the optimization of grinding circuits is not new at all and a large body of research has gone into this topic, stretching back decades. Today we also have the benefit of significantly more powerful methods to analyze and optimize these circuits. Advances in data analytics and raw data processing power enable us to integrate many different components of a mine’s operations. This means that we can now make a decision by simultaneously looking at multiple factors, such as blasting, transportation, maintenance, crushing, grinding, and flotation.
Taking a holistic approach to managing mineral processing will enable smoother operations, reduced energy consumption, and produce lower per-ton costs. But is it even possible to model an entire mine’s operations? And if so, what factors would we need to consider?
The factors working against optimization
One approach to maximizing throughput is to minimize equipment downtime. Another is to ensure equipment is running as close to full capacity as possible. SAG mills and their associated ball charge consume a significant amount of energy to move; fuller machines mean more efficient processing.
But there are many issues preventing these goals:
- The ever-changing quality of ROM ore – The mineral concentration of each load of ore delivered to your primary crusher varies for a whole host of reasons. Given that your blasting team regularly needs to make a judgment call about whether to send each load to crushing or waste, there is the potential for bias to creep into their decision making. A team managed by tons-delivered might occasionally be pressured to deliver sub-standard ore to the mill. This is especially true when they’ve already spent money, time and energy to blast it.
- Lack of integration between different teams – A blasting team unaware of upcoming mill equipment maintenance may continue to stockpile ore.
- High turnover – Some mines, particularly during periods of high commodity prices, face turnover rates of over 30 per cent. When employees leave, they take valuable process knowledge with them.
- Murphy’s Law – Perhaps it flooding from unexpected poor weather or an unexpected breakdown; at some point, something unexpected will happen to throw things off.
Given these factors, an ideal system would be one that could adjust itself to ever-changing conditions, would integrate insight between different teams, and – most importantly – it would record and track process knowledge to ensure that the mine could operate with minimal disruption due to employee turnover.
In the fast-moving, results-driven construction industry, project leaders and teams tend to feel constantly swamped and behind schedule – and over budget. It’s not surprising, therefore, that some contractors or jobsite personnel are reluctant to adopt new technologies because they simply ‘do not have the time.’
Connecting top-level targets to daily decisions
An ideal optimization system would make the connection between top-level targets and daily decisions. This is not an easy task as seemingly simple decisions can have a big impact on either a company’s top or bottom lines.
Take for example the decision to increase throughput in the mill. While speeding up production means that more ore processed, it likely also leads to higher energy consumption which – in turn – increases cost. Is it better to run the mill slower (potentially higher profitability) or faster (higher throughput)? At what point does increased wear and tear from faster-running equipment outweigh the increased revenue? What about mineral prices – does a spike in futures prices six months out justify increased production today?
The point here is to illustrate that there are many factors that a worker in the mill needs to consider to achieve optimal production levels. These are complicated calculations that are nearly impossible to make on the floor and are incredibly challenging for planning teams to make during their weekly/monthly planning cycles.
There’s more than one way to crack a rock
Broad, sweeping changes – while tempting – often fail for the simple reason that bigger projects will introduce execution risk and potentially inflated expectations from the participants. But rather, by taking an approach of continuous, incremental improvement, companies can benefit from the effect of compounding to still achieve big improvements. So how does one do this for an operating mine? We advocate two approaches; improved reporting and coordination and the development of analytics models of your operations.
The ‘manual’ approach: Improve reporting and coordination
The first approach is relatively simple. By connecting operations data from different systems, releasing clear and concise reporting to a broad audience, and improving the quality of insights in these reports, you will be able to ensure that your employees have the information they need to make correct decisions on a daily basis.
A focused effort on improving coordination between teams is also very important. For example, ensuring that your maintenance schedule is aligned with each team to minimize downtime seems obvious, but have you ensured that your maintenance team has the flexibility to achieve this? While inflexible, regular maintenance downtimes may reduce the risk of equipment breakdown, they may also come at inopportune times for the mill, cutting into throughput. Better coordination could allow the production team to build up output stockpiles for downstream teams, and ensure that at least part of the mill continues its operation during maintenance.
In a sense, the ‘manual’ approach is what planning teams, superintendents, and even front-line employees do today. But it’s very difficult – if not impossible – to coordinate everyone on a continuous basis.
The ‘automated’ approach: Modeling out your operations
Another approach is to build out an integrated model that incorporates each component of your milling operations; including crushers, grinders, SAG mills, flotation circuits, and their interactions. By modeling out constraints and tradeoffs between different components, this type of system can calculate optimal operating conditions. More importantly, it can automatically adjust to changing conditions. The trick here, however, is that any model is better than none. A basic working model that has a rough understanding of the relationship between your stockpile, truck speed, and primary crusher, or between your primary crusher, intermediate stockpile, and SAG mill will naturally be able to tell you your relationship between your trucks and SAG mill. Over time, this model will get more accurate and refined as you add in more details.Advanced analytics models exist in many industries today and interest in mining is growing, as evidenced by the winner of Goldcorp’s 2019#DisruptMining competition.
Big changes are coming for milling
In spite of not getting as much attention as sexier topic like mineral exploration; milling optimization can have a significant impact on a company’s operations. Ultimately, optimization improvements come from finding ways to maximize uptime and throughput while balancing against associated costs. That being said, companies must ensure that their teams have rock-solid inter-team communications, as well as access to insights needed to make daily improvements.
Big advances in computing power over the past decade have resulted in the ability to develop advanced analytics models that integrate various components of a mine’s milling operations. The advantage here is that these models don’t suffer from inter-team communications inefficiencies, and they can process many different factors simultaneously.By ensuring that every small action is connected to a mine’s top-line goals, operators will be able to benefit from their cumulative impact, which will result in dramatic improvements over time. Arguably, a fully-optimized mill is just as exciting – if not even more so – than any VR simulation.