In the heart of China’s coal mining industry, a groundbreaking method is emerging to predict and prevent one of the most dangerous phenomena in underground mining: rockbursts. Led by Fan Zhang from the School of Artificial Intelligence at China University of Mining and Technology-Beijing, a novel prediction method is set to revolutionize the way mines operate, enhancing safety and efficiency.
Rockbursts, sudden and violent failures of rock, pose a significant threat to miners and equipment. Traditional prediction methods often fall short due to their static assumptions, ignoring the dynamic nature of surrounding rock loads. Zhang and his team have developed a dynamic solution: the Sparrow Search Algorithm-Random Forest (SSA–RF) prediction method, powered by digital twin technology.
At the core of this innovation is a digital twin model of the two-column support system used in coal mines. This digital twin mirrors the physical support system, providing real-time data and feedback. “By analyzing the interaction between the support system and the surrounding rock, we can achieve a more accurate prediction of potential rockbursts,” Zhang explains. The digital twin model demonstrated impressive accuracy, with an average error of just 0.14° in angle and 6.15 mm in length compared to the physical entity.
The SSA–RF method combines the power of machine learning and optimization algorithms. The Sparrow Search Algorithm fine-tunes the Random Forest model, improving its convergence speed and optimization ability. When tested against other prediction algorithms like LSTM, RF, and SVM, SSA–RF emerged as the top performer, achieving prediction accuracies of 85.89% and 91.09% on central and end support datasets, respectively.
The implications for the energy sector are profound. Accurate prediction of rockbursts can significantly enhance mine safety, reducing the risk of injuries and equipment damage. Moreover, it can optimize mining operations, leading to increased efficiency and reduced downtime. “This method provides a theoretical reference for further research on the occurrence mechanism of rockbursts and accurate prediction of potential hazards,” Zhang notes.
The research, published in Meitan kexue jishu (translated to Coal Science and Technology), marks a significant step forward in mining technology. As digital twin technology and machine learning continue to evolve, we can expect to see even more innovative solutions emerging in the field. This research not only shapes the future of coal mining but also sets a precedent for other industries facing similar challenges. The energy sector stands on the brink of a new era, where data-driven insights and advanced algorithms pave the way for safer, more efficient operations.
