THE COST OF ERROR

How can a company build a functional mining and processing equipment database in the era of big data? Dmitry Przhedetsky explains.

There are topics still left for professional discussions and there are some where almost everyone feels like an expert. Probably more than 90 per cent of readers will be comfortable to share their opinion on the differences between PC and Apple computers, while barely one per cent would be able to explain the differences between the platforms and hardware or commonalities in programming.

This is where the common expertise stops and the pool of experts shrinks faster than the magic skin. Having been developing and advising on mining equipment for the last 30 years, I know for certain that mining equipment is a topic, where everyone somehow involved in the mining industry is an expert. Understandably, the modern mining and the lion’s share of operations globally is all about mining equipment.

A financier knows its capital cost well. A maintenance superintendent knows its operating cost for certain. An OEM (Original Equipment Manufacturer) knows the difference between the previous and the new model. A mining consultant thinks he knows what difference this equipment will make to a project. An operator knows what to say about the difference between the specification sheet and the real performance. A mining software developer will enable a feature to compensate for all this, and then a commodity analyst will create convincing diagrams, which will be presented to the governments. Then, occasionally, some fundamental decisions will be made, sometimes affecting not only the mining industry, but the broader economy.

It would be blatantly unprofessional to question the breadth and depth of expertise of the entire categories of mining specialists, where most of them are very knowledgeable in their fields and are universally acclaimed for their experience. However, having dealt with all these groups of experts, one could say that often their knowledge is incomplete, subjective or worse – bearing an error. We all know the results. The newly developed equipment is underperforming; the project cost exceeds the budget; the mine is shut down or reserves depleted earlier due to the wrong mining method chosen; an OEM has gone broke; an investor has lost his money; a consultancy is being sued for over-optimistic predictions – or worse – a preventable fatality has occurred.

Mining equipment and the associated mining methods are the main modifying factors that make a project viable, hence any errors are costly and sometimes irreversible. What are the remedies?

Due to sensitivity of the matter, let’s omit names of particular equipment manufacturers. Also, reading this article will not guarantee a smooth and error-free ascension to the mining equipment expertise. It may just begin a discussion about the methodology, where equal amount of contribution is needed from all groups of the mining industry professionals.

Over the last few years, I was engaged by two leading international mining advisory firms to overhaul their mining equipment databases and methodologies behind. Not only I have been able to help the clients, but clarified a lot for myself. That’s on top of 30 years of experience in the business and, well, my own errors.

EQUIPMENT SELECTION INPUTS
As we all know, each project during its transition from the conceptual to the bankable stage will go through several evaluations of mining equipment which, once selected, will be further optimised during the development and production stages. As for the latter, there are dedicated asset management software suites around, as well as some products enabling real-time monitoring and predictive maintenance. While some of these parameters could be also integrated into an Equipment Database (ED), let’s consider the very first stages of the process, (i.e. building the initial “static” database) simply containing set values, such as equipment specifications.

For example, what could seem easier than transferring the basic parameters of mine trucks of several manufacturers from the specification sheets into SQL? While the task seems simplistic for many, in reality it is not so. There are several hurdles at the very beginning and below are just a few examples to illustrate this.

• Ambiguity of parameters within specifications sheet of a model of one manufacturer (e.g. inconsistencies between the tray sizes, rated payload and bulk density of material);
• Inconsistency of parameters between models of the same manufacturer (e.g. the tray size is listed for different bulk densities for different models, or it is unclear if the listed engine power is gross or net);
• Differences in methodologies between several manufacturers (e.g. one OEM lists the tray size “struck”, while another lists it as “heaped” or “semi-heaped”);
• Scarcity of information provided by particular manufacturers or inadequate translation (e.g. a manufacturer has not listed the engine power);
• Partial or missing specification sheets for obsolete models. This is the hardest case, as there are still many machines in operations worldwide which had been commissioned in the pre-pdf era. Since then the manufacturer may have changed hands several times, the brand name disappeared and often the gap-filling work is based on individual’s intuition and common sense, rather than on any credible information.

What appears to be a “student job” often creates initial errors in the database, which are often hard to notice, unless the entire database is purposely audited, which is, in turn, a lengthy and difficult process.

APPLICATION SELECTION INPUTS
From the start the project manager responsible for the ED needs to set out what the database is going to be used for. There will be some differences in methodology if the database is used for conventional mining consulting (e.g. feasibility studies), for a broad level analysts, for equipment financiers, or for the equipment end-users.

In any case, the ED has to be flexible enough to cater for various applications where the same piece of equipment used for slightly (or completely) different purposes within different commodities or parts of the mining cycle. Hence a dilemma – should the ED, or derived functions, have permissions for a user to modify individual parameters? If so, while making the database more versatile, it simultaneously makes it more prone to errors. In many cases, the OEM specification is too brief to extract any information about the mining process and the performance of the equipment.

There are too many site-specific factors to take into consideration and the functionality of the ED will depend on the accuracy of the derivatives of the mining processes, for example, manufacturers of bolter-miners do not list the advance rate of the continuous miner, nor do they give any indication of the advance rate of the bolter. There can be several approaches to reflecting each parameter in the database to enable some form of auto or manual calculation based on variable inputs.

INTERPOLATIONS, EXTRAPOLATIONS, INDICES AND REAL-TIME CORRECTIONS
As mentioned above, often the crucial information could be missing or incorrect due to obsolescence of the equipment, different measurement methodology used by manufacturers, different standards between countries, and many other factors. In this case, some of the missing or apparently incorrect parameters need to be modelled. The regression analysis, often used for this purpose, would only fill in major gaps, however it is important to try different types of variables to check the accuracy.

GETTING A FEEL FOR SPECIFICS
There are too many regional, cultural, corporate and commodity influenced specifics to list in this article, but I will just name a few for an indication of how complex the logic of the functional mining ED could be.

In Australia, large hydraulic excavators have been widely adopted in open-cut coal and have often been used not only for pre-strip, but also for overburden removal where strip ratio is low or the operation is too small to justify a dragline. This limits the application for rope shovels, as the choice will be more between a dragline and a hydraulic excavator (depending on the strip ratio) while normally rope shovels would never be used for coal excavation. In the countries of the Former Soviet Union, Mongolia, China and India, the operating practices could be totally different, that is rope shovels are still widely used for overburden removal, and also used for coal.

There are dedicated 5-10m3 bucket rope shovels produced for this purpose. There are several reasons to prefer rope shovels over hydraulic excavators in these countries, such as:

• Relatively developed design and manufacture of rope shovels, compared to large hydraulic-based machinery;
• High capital costs associated with imported hydraulic excavators built by reputable Western or Japanese manufacturers;
• Insufficient service base and stock inventory of the latter;
• Higher maintenance complexity or, at least, what is perceived to be the higher maintenance complexity of hydraulic equipment, particularly in developing countries exposed to extreme winter and summer conditions.

There is a long list of regional specifics and a good way to learn them is studying the individual projects, ideally visiting sites and talking to industry professionals from those countries.

REALITY CHECKS
A modern mining and processing equipment database will be exposed to complex reality checks. There are several strong factors currently affecting the accuracy of any ED, however perfect it might have seemed yesterday, such as:

• Rapid development of new types and classes of mining equipment.
• Mergers and acquisitions of OEMs often resulting in rebranding, change of product lines, specs alterations, outsourcing, etc.
• Price wars between OEMs.
• Substantial currency fluctuations.
• Takeovers of major mining companies, hence the need for reconsidering stockstandard options for particular mine sites, as well as often some changes of the operating and maintenance cost due to implementation of different practices.
• Widening of the gap in technology and operating culture in some regions (e.g. the nineteenth century manual labour mines, the twentieth century mobile equipment mines and the twenty-first century automated mine sites).
• Necessity to analyse and extract data for alternative purposes (e.g. a specific qualitative or quantitative reports for previously unrelated industries).
• A necessity to integrate the real-time data capturing capabilities and ability to generate predictive reports.
• A big data/digital mining friendly engine and user interface.
• A capacity to auto-update predetermined parameters and apply various modelling techniques.

For some people, mining equipment business can sound boring. However, there is a strong alternative view – it is captivating. It is a history of triumphs and disasters; a power-struggle between industrial giants; a roadmap to development of new regions and a field of major scientific and technological breakthroughs. By developing a reliable mining equipment database we build a strong foundation for avoiding costly errors and contribute to OHS of our miners. Please, remember this, while studying a specification sheet, massaging a formula or just entering numbers into your database.

DMITRY PRZHEDETSKY – DIRECTOR, ROCK COGNITION
Dmitry Przhedetsky’s experience and knowledge base covers a broad range of minerals – from iron ore to salt mining, underground coal to uranium mining, and quarrying to tunnelling. He has been involved in many major international mining projects and worked all across Australia, as well as New Zealand, New Caledonia, USA, Canada, India, Germany, France, Russia, Ukraine, Kazakhstan, Uzbekistan and Azerbaijan. Dmitry has been providing consulting services to investors, government officials, equipment manufacturers, and the broader community. He is the founding director of Rock Cognition Pty Ltd.