(1. 東北大學(xué) 資源與土木工程學(xué)院，沈陽(yáng) 110819；
2. 廣西大學(xué) 資源環(huán)境與材料學(xué)院，南寧 530004)
摘 要: 破碎產(chǎn)物粒度精準(zhǔn)預(yù)測(cè)是實(shí)現(xiàn)選廠破碎粒度分布調(diào)節(jié)和控制的關(guān)鍵。基于落重試驗(yàn)和理論分析，對(duì)不同礦物破碎特性及其粒度分布預(yù)測(cè)模型展開研究。結(jié)果表明：礦物破碎產(chǎn)物粒度分布與礦物給料粒度、沖擊破碎比能耗、破碎參數(shù)有關(guān)，Boltzmann-Growth方程能夠較好地?cái)M合出破碎產(chǎn)物粒度分布與沖擊破碎比能耗、t10的回歸關(guān)系，且在同樣破碎比能耗下，破碎產(chǎn)物粒度越小，其累積效應(yīng)越弱；不同礦物和不同粒度之間礦物破碎特性存在較大差異；在此基礎(chǔ)上提出一種綜合廣義回歸模型與粒子群算法的破碎粒度預(yù)測(cè)與優(yōu)化模型，并通過試驗(yàn)驗(yàn)證模型的適用性和可靠性，可為礦物破碎粒度智能調(diào)控和優(yōu)化提供理論基礎(chǔ)。
關(guān)鍵字: 破碎產(chǎn)物；粒度分布；破碎參數(shù)；粒子群算法；預(yù)測(cè)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
