The rapid industrialization in recent decades has enormously increased demands for energy and mineral resources. At the same time, economically viable resources near the surface have been nearly depleted, and the mining industry is facing new challenges of processing low-grade ore, increasing depth, climate changes, and rigorous social and environmental constraints. Furthermore, accountability for major social and sustainable development problems in the mining sector has imposed a further economic burden on mining companies. These challenges in the mining industry can be solved through technical improvements and the optimization of the entire mining value chain using advanced information and communication technologies (ICTs) and automation techniques. This paper presents future prospects of the mining industry by reviewing the sustainable development in mining, the global trend of mining innovations and initiatives of major mining companies, and some new mining technologies in Australia. Furthermore, future prospects and blueprints of the mining industry are suggested. Highlights

Accurate prediction of mineral grades is a fundamental step in mineral exploration and resource estimation, which plays a significant role in the economic evaluation of mining projects. Currently available methods are based either on geometrical approaches or geostatistical techniques that often considers the grade as a regionalised variable. In this paper, we propose a grade estimation technique that combines multilayer feed-forward neural network (NN) and k-nearest neighbour (kNN) models to estimate the grade distribution within a mineral deposit. The models were created by using the available geological information (lithology and alteration) as well as sample locations (easting, northing, and altitude) obtained from the drill hole data. The proposed approach explicitly maintains pattern recognition over the geological features and the chemical composition (mineral grade) of the data. Prior to the estimation of grades, rock types and alterations were predicted at unsampled locations using the kNN algorithm. The presented case study demonstrates that the proposed approach can predict the grades on a test dataset with a mean absolute error (MAE) of $0.507$ and $R^2=0.528$, whereas the traditional model, which only uses the coordinates of sample points as an input, yielded an MAE value of $0.862$ and $R^2=0.112$. The proposed approach is promising and could be an alternative way to estimates grades in a similar modelling tasks.

Strategic mine planning is one of the key factors in the successful survival of large scale mining companies in the long term, and deals with billions of dollars in magnitude. It provides answers in regards to how much capital should be invested, in what resources and when, and finally what type of production strategies should be followed in terms of capacity and product specifications. Existing tools fail to provide optimum solutions to these problems, mainly due to their large scale nature and inherent technical complexities. This article presents a network linear programming (LP) model to efficiently optimise strategic planning and production scheduling by maximising net present value (NPV). The model is applied to optimise the strategic schedule over a 50-year life span for a large mining district in Western Australia which contains many mines with more than 100 pits and 13 plants.

Despite the fact that waste rock hauling and dumping comprises a large proportion of the cost of materials handling in an open pit mine, little detailed planning is devoted to optimising waste rock dumping. The lack of such planning could result in long-term exposure of potential acid forming (PAF) waste rock, causing acid mine drainage (AMD), which would incur ongoing collection and treatment costs. A waste rock dumping plan that not only minimises hauling costs, but also involves the encapsulation of PAF waste rock to minimise AMD, would benefit a mining operation economically and environmentally. In order to create such a plan, two new mixed integer programming (MIP) models are formulated and tested with a five-year mining schedule. Optimised dumping plans are automatically generated, which detail the dumping location for each mining block. A manual approach is also attempted, and comparison shows that the MIP models have obvious advantages in error prevention, faster solution time and cost saving, by up to 9.5%.