Mine Automation & Data Analytics

Foundations of Mine Automation and Autonomous Systems Automation Fundamentals: essential elements of an automated system; automation in the mine production system; principles and strategies of automation; introduction to automation productivity and levels of automation. Autonomous Mining Systems: autonomous haulage systems (AHS); automated drilling systems; tele-remote and autonomous operating modes; selection considerations for autonomous equipment. Fleet Management & Remote Operations: Fleet Management Systems — Truck Dispatch System (TDS), Computerised Maintenance Management System (CMMS) and Enterprise Resource Planning (ERP) for the mining industry; mine remote operations and control; mining robotics and “armchair mining”; remote operating centres.

Sensing, Communication, Tracking and Virtual-Reality Technologies Automated Communication & Tracking Technologies: proximity-detection systems; GNSS/UPS positioning; radar systems; RFID and geo-fencing; CCD cameras and machine vision; image processing; SCADA architecture and its applications in mines. Virtual Reality & Simulation: virtual-reality applications in mining; mining-equipment concept development; mine-safety training applications; mining-operation simulations and visualisation of the mining process for safe operations.

Statistical Foundations and Introduction to Machine Learning Descriptive Statistics: measures of central tendency and dispersion; data summarisation and visualisation; probability and probability distributions (discrete and continuous) relevant to mining data. Inferential Statistics: sampling and estimation; hypothesis testing; regression analysis; analysis of variance (ANOVA); interpretation of results for mining problems. Introduction to Machine Learning: basic concepts, terminology and the learning workflow; supervised, unsupervised and reinforcement learning; the data-analytics pipeline for mining applications.

Supervised Learning, Neural Networks and Deep Learning Supervised Learning: classification and regression; feature engineering; training, validation and testing; the bias–variance trade-off; model-evaluation metrics. Artificial Neural Networks: the perceptron and multilayer networks; activation functions; the back-propagation algorithm; network training and regularisation. Deep Learning: deep network architectures (overview); representation and feature learning; representative applications of deep learning to mining and geoscience data.

Unsupervised Learning, Big-Data Analytics and AI Applications in Mining Unsupervised Learning: clustering techniques; pattern and structure discovery in unlabelled mining data; dimensionality reduction (overview). Big-Data Analytics: characteristics and challenges of big data; data acquisition, storage and management; analytics frameworks and pipelines for large-scale mining data. Applications in Mining: application of big-data analytics and artificial intelligence in mining — predictive maintenance, production and energy optimisation, safety analytics and decision-support systems.