May 17, 2020

Three key attributes for a successful mine start-up...

Mining finance
Mining technology
Dale Benton
5 min
How to solve a problem like a mine start-up? Three key attributes to a successful mine start-up...
In the current financial climate, the margins for success of a mine start-up are thinner than ever before. Mine start-ups in 2016 have gone one of two w...

In the current financial climate, the margins for success of a mine start-up are thinner than ever before. Mine start-ups in 2016 have gone one of two ways, either they have been impeccably managed, ahead of schedule and on or under budget, or they have suffered real teething problems and difficult periods, some have even fallen seriously short of expectations and need a financial bailout.

Here are three examples of mine start-ups as highlighted by finnCap. Some good, some bad. All key in highlighting the challenges faced in mine start-ups.

Atalaya Mining – Rio Tinto Copper mine, Spain

The European copper producer Atalaya acquired the Rio Tinto Copper mine in 2007 and set out to rebuild the mine site in 2015. The rebuild process was divided into two phases, phase one – the dewatering of the ld Cerro Colorado open pit and refurbishment of the modern parts of the existing plant.

Phase two, the construction if the major expansion to the plant incorporating modern equipment – the site was first developed into a substantial operation in 1873.

Through a combined 58.8percent ownership of issued equity, through shareholders who are traders in metal commodities or specialist investors, the project needed no debt financing. Financing was secured purely through copper concentrate offtake deals.

Construction was completed in May 2016, most importantly, ahead of schedule and on budget.

And the success story doesn’t end there, production has ramped up and it is expected that there will be 9.5million tonnes of ore processed per year before the end of 2016.

Aureus Mining – New Liberty gold mine, Liberia

Aureus Mining entered the mining world in February 2011 with the goal of exploring and developing gold exploration properties in Liberia. Following a feasibility study on New Liberty back in 2012, construction of a mine started that very same year.

The goal, begin producing gold by Q4 of 2014 within a budget of around US$140m.

And so, came a long and strenuous four years for Aureus.

In 2013, the company raised US$80m in new equity in parallel with agreeing a debt package of US$100m. An additional US$16m was raised in October 2013, primarily to fund exploration work in parallel with the mine construction. This was followed by a further top-up of US$15m in April 2014.

By now the company was targeting a later first production in Q1 2015.

In 2014, a massive epidemic of Ebola was identified and the whole of Liberia was in lockdown in a bid to contain it. It wasn’t until April 2015 that it was declared over.

You can imagine the impact this had on the project, most notably the port used for the import of plant and machinery ground to a complete halt. Towards the end of the epidemic, a further US$15m had been raised to stay afloat.

In May 2016, amid environmental concerns due to a discharge of cyanide solution into the environment and the plant has in fact been shut down pending an investigation.

Four years after the start of ground clearance on the project, and with more than US$270 thrown at what was originally intended to be $US140m, the mine is still not even close to “steady-state production”.

Wolf Minerals, Hemerdon Tungsten mine, UK

The UK tungsten mine became a part of Wolf Minerals’ portfolio in 2013 following two years of approvals and permitting with the landowners and construction commenced in February 2014.

The mine itself was commissioned in 2015, but it looks like it is far from being profitable after a series of major problems including technical problems with the tungsten recoveries resulting in a recovery of around 30 percent of ore as opposed to the expected 66 percent.

Not the best of starts.

As is often the case for many mining companies, the commodity price market doesn’t often cooperate nicely. The weakness in the Tungsten market proved to be more severe than many expected and worse so, there are no signs of a recovery just yet meaning Wolf Minerals are far away from operating the mine at a profitable level – and that’s if they get the mine working satisfactorily.

But there is hope. Wolf has already started work on turning things around. The company has begun to make a series of equipment changes and modifications to navigate the technical problem hurdle. Operations are expected to be underway by mid-2017.

From a financial standpoint, the company’s major shareholder Resource Capital Funds (RCF) has continued to support it to the point where it is now by far the largest shareholder, with a 56% interest. RCF has also recently agreed to provide a £20m 12-month bridge loan to cover the turnaround period.

This financial backbone should allow the company enough time to take the mine to profitability, and fingers crossed there may even be a recovery in tungsten someday soon.

A word from our expert…

Martin Potts, mining analyst at finnCap, believes that timing is critical for a mine start-up.
“Many problems are driven by the fact that these mines were commissioned at a point in the mining cycle where prices were far lower than when the decision was taken to finance and construct the mines. However, one of the certainties about the mining sector is that commodity prices will recover as the cycle moves on.”

Three key attributes for successful start-ups…

Looking at the findings, Potts has identified three key factors for a mine start-up to be successful.

         • Timing. Commissioning mines at a time of falling commodity prices will

result in stretched finances.

          • High-quality major shareholders. These will be able to support the

company should it encounter problems.

          • High-quality owners team. There is a critical need to recognise and

differentiate between what could be a real money-saving opportunity and

what may cripple the operation.


The October issue of Mining Global Magazine is live!

Follow @MiningGlobal

Get in touch with our editor Dale Benton at [email protected]


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May 16, 2021

McKinsey: adopting a smart approach to big data

Big Data
Machine Learning
4 min
Industrial companies are using AI to improve plant operations. To be successful, they will need to leverage big data with the help of domain experts

Industrial companies are embracing artificial intelligence (AI) as part of the fourth digital revolution. AI leverages big data; it promises new insights that derive from applying machine learning to datasets with more variables, longer timescales, and higher granularity than ever.

Big Data

According to a new McKinsey report, using months or even years’ worth of information, analytics models can tease out efficient operating regimes based on controllable variables, such as pump speed, or disturbance variables, such as weather. These insights can be embedded into existing control systems, bundled into a separate advisory tool, or used for performance management.

Artificial Intelligence

McKinsey recommends that to succeed with AI, companies should leverage historical data via automation. They will need to adapt their big data into a form amenable to AI. This ‘smart data’ can improves predictive accuracy and support root-cause analysis. Additionally, bolstering and upskilling expert staff to manage the process can result in an EBITDA increase of 5 to 15%.

Smart Data

A common failure mode for companies looking to leverage AI is poor integration of operational expertise into the data-science process. McKinsey advocates applying machine learning only after process data have been analysed, enriched, and transformed with expert-driven data engineering using the following steps:

Define the process

Outline the steps of the process with experts and plant engineers, sketching out physical changes (such as grinding and heating) and chemical changes (such as oxidation and polymerization). Identify critical sensors and instruments, along with their maintenance dates, limits, units of measure, and whether they can be controlled.

Enrich the data

Raw process data nearly always contain deficiencies. Thus, creating a high-quality dataset should be the focus, rather than striving for the maximum number of observables for training. Teams should be aggressive in removing nonsteady-state information, such as the ramping up and down of equipment, along with data from unrelated plant configurations or operating regimes. Generic methods to treat missing or anomalous data should be avoided, such as imputing using averages, “clipping” to a maximum, or fitting to an assumed normal distribution. Instead, teams should start with the critical sensors identified by process experts and carefully address data gaps using virtual sensors and physically correct imputations.

Reduce the dimensionality

AI algorithms build a model by matching outputs, known as observables, to a set of inputs, known as features, which consist of raw sensor data or derivations thereof. Generally, the number of observables must greatly exceed the number of features to yield a generalized model. A common data-science approach is to engineer input combinations to produce new features. When combined with the sheer number of sensors available in modern plants, this necessitates a massive number of observations. Instead, teams should pare the features list to include only those inputs that describe the physical process, then apply deterministic equations to create features that intelligently combine sensor information (such as combining mass and flow to yield density). Often, this is an excellent way to reduce the dimensionality of and introduce relationships in the data, which minimize the number of observables required to adequately train a model.

Apply machine learning

Industrial processes can be characterized by deterministic and stochastic components. In practice, first principle–based features should provide the deterministic portion, with machine-learning models capturing the statistical portion from ancillary sensors and data. Teams should evaluate features by inspecting their importance and therefore their explanatory power. Ideally, expert-engineered features that capture, for example, the physics of the process should rank among the most important. Overall, the focus should be on creating models that drive plant improvement, as opposed to tuning a model to achieve the highest predictive accuracy. Teams should bear in mind that process data naturally exhibit high correlations. In some cases, model performance can appear excellent, but it is more important to isolate the causal components and controllable variables than to solely rely on correlations. Finally, errors in the underlying sensor data should be evaluated with respect to the objective function. It is not uncommon for data scientists to strive for higher model accuracy only to find that it is limited by sensor accuracy.

Implement and validate the models

Impact can be achieved only if models (or their findings) are implemented. Taking action is critical. Teams should continuously review model results with experts by examining important features to ensure they match the physical process, reviewing partial dependence plots (PDPs) to understand causality, and confirming what can actually be controlled. Additional meetings should be set up with operations colleagues to gauge what can be implemented and to agree on baseline performance. It is not uncommon for teams to convey model results in real time to operators in a control room or to engage in on-off testing before investing in production-grade, automated solutions.


Industrial companies are looking to AI to boost their plant operations—to reduce downtime, proactively schedule maintenance, improve product quality, and so on. However, achieving operational impact from AI is not easy. To be successful, these companies will need to engineer their big data to include knowledge of the operations (such as mass-balance or thermodynamic relationships). They will also need to form cross-functional data-science teams that include employees who are capable of bridging the gap between machine-learning approaches and process knowledge. Once these elements are combined with an agile way of working that advocates iterative improvement and a bias to implement findings, a true transformation can be achieved.

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