Yan Haoli, Zhou Xiaolei, Gao Lei, Fang Haoyu, Wang Yunpeng, Ji Haohang, Liu Shangrui
Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology, Kunming 650093, China.
Materials (Basel). 2023 Jul 24;16(14):5184. doi: 10.3390/ma16145184.
Due to the detrimental impact of steel industry emissions on the environment, countries worldwide prioritize green development. Replacing sintered iron ore with pellets holds promise for emission reduction and environmental protection. As high-grade iron ore resources decline, research on limonite pellet technology becomes crucial. However, pellets undergo rigorous mechanical actions during production and use. This study prepared a series of limonite pellet samples with varying ratios and measured their compressive strength. The influence of humic acid on the compressive strength of green and indurated pellets was explored. The results indicate that humic acid enhances the strength of green pellets but reduces that of indurated limonite pellets, which exhibit lower compressive strength compared to bentonite-based pellets. Furthermore, artificial neural networks (ANN) predicted the compressive strength of humic acid and bentonite-based pellets, establishing the relationship between input variables (binder content, pellet diameter, and weight) and output response (compressive strength). Integrating pellet technology and machine learning drives limonite pellet advancement, contributing to emission reduction and environmental preservation.
由于钢铁行业排放对环境的不利影响，世界各国都将绿色发展作为优先事项。用球团矿替代烧结铁矿石有望实现减排和环境保护。随着高品位铁矿石资源的减少，褐铁矿球团技术的研究变得至关重要。然而，球团矿在生产和使用过程中会受到严格的机械作用。本研究制备了一系列不同比例的褐铁矿球团样品，并测量了它们的抗压强度。探讨了腐殖酸对生球团和焙烧球团抗压强度的影响。结果表明，腐殖酸提高了生球团的强度，但降低了焙烧褐铁矿球团的强度，与膨润土基球团相比，其抗压强度较低。此外，人工神经网络（ANN）预测了腐殖酸和膨润土基球团的抗压强度，建立了输入变量（粘结剂含量、球团直径和重量）与输出响应（抗压强度）之间的关系。将球团技术与机器学习相结合推动了褐铁矿球团的进步，有助于减排和环境保护。
