Big Data and the coal industry

Prof. Kray Luxbacher, Virginia Tech University, US, argues for improved utilisation of big data in the coal mining industry.

It is hard to avoid the term big data in popular news and features. It is a term that transcends fields, disciplines and organisations, drawing the attention of CEOs of corporate giants, such as GE and Merrill Lynch, engaging the giants of social media, such as Twitter and Facebook, and even garnering the attention of national security entities, including the US Central Intelligence Agency. Data mining is a burgeoning field and, around the world, universities are building departments and offering formal education in data science and analytics. In most contexts, the data referred to here are the massive quantities of data that can be collected from social media and web use – these data have significant implications for marketing and security and a single set is on the order of exabytes.

However, the traditional data collected by corporations and industries also fall within the big data realm. With advances in technology, including real-time sensing and higher sensor density, these data sets are growing exponentially.

Big data: a tremendous resource for the coal industry

In an industry celebrated for extracting and moving enormous quantities of raw material around the world, we have been less than enthusiastic in embracing the big data we generate, although it has potential to be a tremendous resource for improved health, safety and efficiency. So, how do we effectively mine these data?

Examine just the data generated over the course of a single day in an underground mine: sensing of mine gases at critical locations; fan pressures and operating characteristics; barometric pressures; temperature; dust and other particulate matter (DPM) concentration and size fractions; conveyor belt monitoring; equipment location; geophysical parameters; function of cutting and hauling equipment, from load to maintenance indicators; tracking of supplies and people – and this is hardly an exhaustive list. The challenges in managing and utilising these data sets are daunting, but the opportunities are limitless.

As an example, consider only those data sets that characterise the function of mine ventilation systems and the health of the mine environment. These data are generally logged and threshold levels, set by regulatory limit or other safety standard, are generally set to trip an alarm to alert designated persons: for instance, a high carbon monoxide (CO) reading on a conveyor belt or a high methane (CH4) reading in a return air course would trigger notification of affected persons and investigation of the source.

No doubt, these applications have prevented many accidents, but rarely are these data utilised to their full capacity. Manual trending and study of these data sets is time intensive and automatic trending of so many variables rarely allows for the emergence of salient indicators. However, full application of data analytics and science to the mining industry could provide early insight into the failure of systems or allow for early identification of opportunities for improvement.

Considering the coal mine user of big data

An operation must first take a fairly sophisticated approach towards the collection and storage of data:

• Operations must implement robust and reliable systems for data collection and storage. In other words, there must be a quantity of data and confidence in the quality of these data.
• Data must be synthesised. In complex systems, data are collected over many domains. Data synthesis may include assimilating the data in time or space. The way in which data are analysed and applied must be designed with many factors in mind, with emphasis on the end user.

Analysis must allow for the emergence of the most important data, correlation and indications of causality, as well as perhaps even predicative capabilities. For instance, in examining the ventilation data referred to above, if increasing methane emissions are observed over time, can they be correlated with increased production rate? With dropping barometric pressure? And can causality be verified?

As large quantities of data for mining applications are examined, the user is one of the most important considerations. With the advances that have already been realised in underground wireless communication, the day when we can communicate data to every person working in a mine has arrived, but the demands of the underground environment certainly presents challenges. First, a handheld device for communication that is easily portable, rugged, appropriate for use in potentially explosive environments and allows for visual communication would be an achievement. Next, using the appropriate techniques for imparting knowledge about the operation visually and in relatively little time is critical – consider the information people glean with only a glance at a smart phone.

Imagine a longwall shearer operator arriving for a shift and accessing an interactive screen that provides information about methane and dust levels across the face, correlated with production and barometric pressure, as well as air provided to the face and bleeder and gob indictors. Such data could be presented in a manner that allows for rapid visual understanding. Underground, this operator has access to a rugged and portable tablet with the same information updated in real time. So do other workers and supervisors. The opportunities to enhance decision making with real implications for health, safety and efficiency are evident.

Further, these concepts can be applied to other safety indicators, such as:

• Monitoring of ground.
• Maintenance, allowing for targeted and improved preventative maintenance.
• Production, allowing for rapid identification of inefficiencies and bottlenecks with opportunity for holistic improvement.
• Quality, allowing for better analysis and assurance of products.

Empowering coal miners

Undeniably, the greatest resource in the world coal industry is its people. Everyone, from the miners at the pit face to the CEO, is entrusted with decisions each day that can impact the health and safety of coworkers and communities, as well as the viability of the operation. Improved utilisation of our own big data provides the information needed to make better informed decisions.

This article first appeared in the May 2014 issue of World Coal.

Written by Kray Luxbacher. Edited by Jonathan Rowland

Read the article online at: https://www.worldcoal.com/special-reports/18062014/big_data_and_the_coal_industry_industry_view_kray_luxbacher_coal993/