基于动态浓密试验的深锥浓密机底流浓度预测模型

Prediction Model for Underflow Concentration of Deep Cone Thickener Based on Dynamic Thickening Experimentation

深锥浓密机制备高浓度尾砂料浆普遍用于膏体充填,通常依据静态沉降和动态浓密理论预测深锥浓密机运行规律,进行底流浓度的调控,然而模型精度难以达到要求。通过开展某铜矿全尾砂絮凝动态沉降试验研究,揭示了底流浓度随停留时间延长而上升的变化趋势,探明了网络化泥床压缩屈服应力增长随底流浓度上升的规律,解释了泥床高度随停留时间延长而逐渐压缩的现象。在考虑网络化泥床压缩屈服应力对脱水速度影响的基础上,建立了泥床高度、停留时间和底流浓度的预测模型。通过与试验数据对比,模型预测值误差为3%~5%,浓密机预测模型具有较高的准确度,可作为深锥浓密机底流预测计算依据。

High concentration tailings slurry prepared by deep cone thickener are widely used in cement paste backfill. Generally,the operating regularity of deep cone thickener is predicted to regulate and control the underflow density based on static settling and dynamic thickening theory,but the prediction accuracy of the model can＇t match the request of backfill technology. Through the experimentation of flocculated raking thickening of a copper mine with whole tailings,the vari-ation tendency of underflow concentration raising with retain time was revealed, and the regularity of the compressive yield stress of networked slime bed increased as underflow concentration raised was proved. Then,the reason that the bed height gen-erally decreases with retain time extending was given. Considering the effect of the compressive yield stress of networked bed on dewatering rate,a mathematical prediction model for underflow concentration,bed height and retain time was set up. After con-trasting with the experiments,it is found that the prediction deviation ranged from 3% to 5%. Therefore,with a higher accura-cy,the prediction model can be used as a basis for predicting the underflow concentration of deep cone thickener.