Optimising drill and blast performance plays a key role in improving productivity across the entire mining process
Global mining faces unprecedented challenges to the long-term sustainability of its operations. In addition to the evolving socio-environmental challenges, realities such as ever reducing ore grades and downward pressure on ore prices mean mining operations must find innovative solutions to address these challenges.
A fundamental enabler of sustainability resides in low-impact technologies that can reduce cost while increasing productivity. In this context, a key aspect of sustainability in all mining projects is the ability to optimise the use of available resources. It is here that optimising energy consumption and controlling total costs play a fundamental role in ensuring a sustainable project.
Blasting as an optimisation strategy
Optimisation needs to be underpinned by an intrinsic understanding of the process through data capture and analysis. This enables the formulation of models that can predict the outcomes of interventions. Optimisation must be a holistic endeavour and cannot be executed in isolation. It requires in-depth knowledge of the processes that make up each stage of the operation as well as identifying and then setting the parameters for the inter-dependencies between the different unit operations in the mining process.
When developing a high-energy blasting strategy, the quality and degree of fragmentation obtained and its influence on subsequent operations will be key to optimising the energy required in the next stages of the mining process. The fragmentation and characteristics of the muck pile relating to shape and looseness generated by the blasts should be designed to maximise the performance of downstream processes like optimum load cycles and the crushing and grinding circuit.
The effect of the dynamic action of detonation during the rock fragmentation process and its influence on crushing and grinding has been researched for many years. While the effect of macro-fragmentation on the unit operations after blasting is obvious, ore pre-conditioning as a function of microfracturing is only evidenced by secondary and tertiary crushing efficiencies.
Understanding the effects of microfracturing on the efficiency of the crushing and grinding circuit requires an in-depth understanding of the crushing circuit but also requires continual tracking and review. Easier said than done in the dynamic environment of production processes, but it is vital to ensure that the correct models are built to enable a high confidence in predictions. Continuous tracking and analysis of the relationship between the amount of energy used in blasting and the performance of the crushing and grinding circuits can assist the operation to intelligently apply explosive energy to get the most out of the energy consumed in the crushing and grinding circuit, and can achieve significant reductions in the energy consumption versus material throughput.
Integrating the processes
All efforts to optimise results must form part of a holistic continuous improvement process in which all parties involved work together. It is therefore necessary to apply gradable models or tools that can predict, in the design stage, the potential impact of a blast on subsequent unit operations, thus ensuring optimal configuration of energy consumption once the actual results have been compared and calibrated in each cycle.
Higher energy blasting can have adverse effects, such as a higher risk of flyrock and an increase in vibrations, and should only be applied where downstream results have shown a benefit to the overall process. The challenge for quantifying the benefit of high energy blasting lies in generating high confidence prediction models in a variable ecosystem with many components. For illustration, maintaining the comminution equipment configuration rotation speed of grinding mills, concentration of water, solids, etc) will ensure that actual productivity and consumption readings are compatible with the prediction criteria in the optimisation model used. However, production realities may not allow for that and incorrect assumptions as to the impact of high-energy blasting can be made. Therefore, the implicit and explicit limitations of the predictive models must be taken into account when deploying an optimisation program.
In the evolving environment it is evident that mines have little choice but to mine smarter in order to meet the legion of varied mining ecosystem challenges and enhance profitability.
A reduction in energy required to beneficiate ore can result in significant savings to an operation and higher energy blasting programs are tools that can translate into downstream savings; however, these programs need to be implemented carefully and holistically to ensure that they translate into real value.