In the heart of China’s industrial powerhouse, Shenyang, a groundbreaking development is poised to revolutionize the way we approach open-pit mining. Researchers at Northeastern University have unveiled a cutting-edge algorithm that promises to optimize blasting effects and slash mining costs, a game-changer for the energy sector.
At the helm of this innovation is CHEN Chengzhen, a researcher at the School of Resource and Civil Engineering. Chen and his team have developed a deep learning algorithm that can rapidly calculate the size distribution of blast pile fragments, a critical factor in assessing blasting effectiveness. “The ability to quickly and accurately determine the size distribution of blast fragments allows us to fine-tune our blasting parameters,” Chen explains. “This not only enhances the efficiency of the blasting process but also significantly reduces mining costs.”
The algorithm leverages high-resolution orthophoto datasets acquired through nap-of-the-object photogrammetry, a technique that captures detailed images of the blast piles. By integrating a switchable atrous convolution module and a recursive feature pyramid refinement module, the algorithm excels in extracting features of different rock fragmentation sizes. This level of precision is unprecedented in the industry.
But the innovation doesn’t stop at feature extraction. The team utilized Fourier descriptors to establish statistical distributions of the blast piles and employed the cumulative passing volume curve to replace the traditional cumulative passing rate. This approach provides a more accurate and comprehensive assessment of the blasting outcomes.
The results speak for themselves. The mean fine fragmentation rate on the surface of the target blast pile was a mere 8.90%, while the mean large block rate was just 4.69%. These figures indicate that there is substantial room for optimizing blasting parameters, further reducing costs and enhancing efficiency.
The implications for the energy sector are profound. Open-pit mining is a cornerstone of energy production, and any advancements in this area can have a ripple effect across the industry. By optimizing blasting processes, mines can operate more efficiently, reduce waste, and lower their environmental footprint. This is not just about cost savings; it’s about sustainability and responsible resource management.
Chen’s work, published in the Journal of Mining Science, is a testament to the power of deep learning and machine vision in transforming traditional industries. As the energy sector continues to evolve, innovations like this will be crucial in meeting the growing demand for resources while minimizing environmental impact.
The future of open-pit mining is here, and it’s powered by deep learning. As Chen and his team continue to refine their algorithm, the possibilities for optimization and cost reduction are endless. The energy sector stands on the brink of a new era, one where technology and sustainability go hand in hand.
