(1. 东北大学 资源与土木工程学院，沈阳 110819；
2. 广西大学 资源环境与材料学院，南宁 530004)
摘 要: 破碎产物粒度精准预测是实现选厂破碎粒度分布调节和控制的关键。基于落重试验和理论分析，对不同矿物破碎特性及其粒度分布预测模型展开研究。结果表明：矿物破碎产物粒度分布与矿物给料粒度、冲击破碎比能耗、破碎参数有关，Boltzmann-Growth方程能够较好地拟合出破碎产物粒度分布与冲击破碎比能耗、t10的回归关系，且在同样破碎比能耗下，破碎产物粒度越小，其累积效应越弱；不同矿物和不同粒度之间矿物破碎特性存在较大差异；在此基础上提出一种综合广义回归模型与粒子群算法的破碎粒度预测与优化模型，并通过试验验证模型的适用性和可靠性，可为矿物破碎粒度智能调控和优化提供理论基础。
关键字: 破碎产物；粒度分布；破碎参数；粒子群算法；预测模型
（1. College of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China;
2. College of Resources, Environment and Materials, Guangxi University, Nanning 530004, China）
Abstract:The particle size accurate prediction of crushing products is the key to realize the adjustment and control of crushing particle size distribution in a concentrator. Based on drop weight test and theoretical analysis, the crushing characteristics and the prediction models of particle size distribution of different minerals were studied. The results show that the particle size distribution of impact crushing products is related to the mineral feed size, the energy consumption of impact crushing and the crushing parameters. The Boltzmann-Growth equation can well fit the regression relationship between the particle size distribution of crushing products and the energy consumption of impact crushing and the t10. Under the same crushing energy consumption, the smaller the particle size of the crushing product is. The weaker the cumulative effect is. There are great differences in mineral crushing characteristics between different minerals and different particle size. On this basis, a comprehensive generalized regression model and particle swarm optimization model for particle size prediction and optimization are proposed, the test results show that the model has certain applicability and reliability, which can provide a theoretical basis for intelligent control and optimization of mineral crushing particle size.
Key words: crushing products; particle size distribution; crushing parameter; particle swarm optimization algorithm; prediction model
