基于响应面法的低阶煤浮选捕收剂磁化条件研究

Optimal conditions for collector magnetization of low-rank coal flotation based on response surface method

为解决低阶难浮煤泥浮选过程中煤油消耗量大、浮选效果差的问题,对煤油-SPAN80捕收剂进行磁化处理,并考察了不同磁化条件下捕收剂用于低阶煤的浮选效果。采用响应面法对捕收剂磁化条件进行优化,建立响应面回归模型,分析磁化温度、磁场强度和磁化时间及其交互作用对精煤产率和尾煤灰分的影响规律,并对比优化前后浮选效果。结果表明,变量因素对精煤产率和尾煤灰分均有明显影响。当磁化温度为20~30℃,磁场强度为0.2~0.4T,磁化时间为4~8min时,煤泥浮选效果处于较好水平;响应面优化试验得出以精煤产率和尾煤灰分为响应值的二阶回归方程,磁化时间对精煤产率和尾煤灰分的影响最显著,磁场强度和磁化温度的影响次之;磁场强度与磁化时间的交互作用显著。优化后捕收剂最佳磁化条件为磁化温度27.5℃,磁场强度0.26T,磁化时间6.05min,此时精煤产率和尾煤灰分分别为63.41%和74.29%,试验值与预测值的误差只有0.82%和1.09%,证明该响应面优化模型用于预测实际情况具有可行性。

Aiming at the high kerosene consumption and poor flotation effect in flotation of low-rank hard-to-float coal slime,kerosine-SPAN80 collector was magnetized under different magnetization conditions,and the flotation effect was investigated.Response surface method was used to optimize the magnetization conditions of the collector,and a response surface regression model was established to analyze the effects of the magnetization temperature,magnetic field intensity,magnetization time and their interactions on the yield and coal ash content of clean coal.Finally,a comparison experiment was conducted before and after optimization.The results show that the variable factors have significant influence on the yield and ash content of clean coal.When the magnetization temperature was 20~30℃,the magnetic field intensity was 0.2~0.4 T,and the magnetization time was 4~8 min,the flotation effect of coal slime was at a good level.The second order regression equation of cleaned coal yield and tail ash is obtained by response surface optimization test.The magnetization time has the most significant influence on cleaned coal yield and tail ash,followed by magnetic field intensity and magnetization temperature.The interaction between magnetic field strength and magnetization time was significant.The optimal magnetization condition of the collector after optimization was the magnetization temperature of 27.5℃,magnetic field strength of 0.26 T,and magnetization time of 6.05 min.At this time,the cleaned coal yield and tail coal ash content were 63.41%and 74.29%,respectively.The error between the test value and the predicted value was only 0.82%and 1.09%,which proved that the response surface optimization model was feasible for predicting the actual situation.