Advanced Analytics in Mining Engineering

OVERVIEW TAB REPRESENTATIVE PICTURE OF THE COURSE
Data analytics is no longer a luxury but viewed as a necessity for an industry generating trillions of dollars every year. There are many phases of the mining process where data analytics can be put to practical use. The mining industry is increasingly using advanced analytics (AA) and artificial intelligence (AI) applications to optimize processes, enhance decision-making, derive value from data, and improve safety.
Five key application areas in mining – where high operational costs and business value are created – are ore extraction, mining and handling mined materials, grinding materials in preparation for processing, separating and concentrating the usable components into saleable products, and mined material transferring. While there are many benefits to AA implementation in the mining industry, it is still a very new applied science, and there is much work needs to go into a successful application. During the Advanced Analytics in Mining Engineering course, attendances will learn all the application areas of AA
and AI in the mine value chain, from exploration to marketing.

Introduction

The 4th industrial revolution is the 21st -century convergence of digital, physical, and biotechnologies driving an unrelenting acceleration of human progress. Advances in computing power, AI, the internet of things (IoT), and machine learning (ML) enable mining companies to speed the pace of growth and create amazing experiences in different operational areas. The technology of the 4 th revolution is inseparably tied to the vast amounts of data needed to train AI algorithms and other key forms of modern technology. The need for data has led to exponential growth in gathering it, and AA has gained massive momentum in the industrial mining sector.

The main objectives of this course are presenting the scientific concepts and providing industrial case studies for different applications of AA in mining, which can be grouped into three main areas:
Descriptive Analytics: Its function is to describe, diagnose, and discover what trends and patterns occur in a given process, thanks to the real-time study of historical data.
Predictive Analytics: Based on more advanced mathematical methods that include statistical analyses, data mining, predictive models, and machine learning, among others. Its function consists of predicting events that can occur in the future, thanks to developing a predictive model.
Prescriptive analytics: Its function consists of defining the actions to obtain the best results in a process. It relies on predictive models, scenario simulations, localized rules, and technical
optimization to transform data and recommend obtaining the desired result. This level of analytics is completer and more robust. It uses complex event processing, neural networks, heuristic learning, and “machine learning,” among others.
Optimization: Optimization is one of the most important categories of advanced analytics that can potentially help mining companies to improve their productivity, safety, and energy efficiency.
Optimization also is a useful tool to decrease gas emissions, environmental issues, maintenance, and total product costs.