Research On Prediction Of Flotation Indexes Of Medium And Low Grade Phosphate Ore Based On Neural Networ

I In actual industrial production,ore properties,dosage of reagents and parameters of flotation equipment will affect the grade and recovery of flotation concentrate.Using reverse flotation to pretreatment medium and low grade phosphate ore in Guizhou province.The effects of grinding fineness,collector GJBW dosage,regulator dosage,pulp concentration,rotor speed and flotation time on flotation indexes were studied.And single factor test,orthogonal test,MIV variable screening and neural network prediction were used to predict flotation indexes.The effect of different particle size on flotation index was studied by flotation kinetics and neural network model.The main research results are as follows:1.The results of single factor flotation test and orthogonal flotation test show:Under the conditions of grinding fineness-0.075 mm 73.9%,collector GJBW dosage 750 g/t,adjusting agent dosage 0.2 m³/t,pulp concentration 27%,rotor speed 1800 r/min,flotation time 4 min.The best concentrate P₂O₅ grade and recovery were 30.73%and93.69%.According to the orthogonal test,the influence degree of main factors on flotation index is as follows:Flotation time>Adjusting agent dosage>collector GJBW dosage>Pulp concentration>Grinding fineness>Rotor speed.The effects of grinding fineness,collector GJBW dosage,regulator dosage,pulp concentration,rotor speed and flotation time on P₂O₅ grade,recovery and flotation efficiency of concentrate were studied by MIV screening algorithm.Among them,flotation time has the greatest influence on the grade of P₂O₅ concentrate,and MIV value is 2.23.Flotation time has the greatest effect on the recovery of P₂O₅ concentrate,and MIV value is 3.43.Flotation time has the greatest influence on flotation efficiency,and MIV value is 3.68.2.Based on the single factor test and orthogonal test results,the BP neural network model of P₂O₅ concentrate grade,recovery and flotation efficiency was established.Compared with the BP neural network model,the decision coefficient（R²）of the improved LM-MIV-BP neural network was increased by 0.18,the root mean square error（MSE）was reduced by 36.85,and the prediction accuracy of the model was improved.The prediction of flotation index can be realized.3.The effects of different particle sizes on the flotation of phosphate ores were studied by flotation kinetics and neural network model.The results show that the flotation effect of-45+25μm particle size is the best,followed by-75+45μm particle size,then-25μm particle size,and finally-120+75μm particle size.By researching the change of SI value,it can be seen that the selectivity in the late flotation process is better than that in the early flotation process.The SI value of-45+25μm grain size was the largest,followed by-75+45μm grain size,next,-25μm grain size,and finally-120+75μm grain size.It can be seen that foam flotation is more suitable for the separation of phosphate ore particles with-45+25μm grain size.The study of flotation kinetics shows that the kinetic behavior of phosphate ore flotation accords with the first-order kinetic model and can objectively optimize the separation conditions.The prediction model of SI value and P2O5 recovery of concentrate can be accurately predicted by using neural network.4.Based on LM-MIV-BP neural network model,the prediction accuracy of flotation index can be improved,the flotation parameters can be effectively controlled in the actual production process,and the flotation benefit can be improved.By research the effect of phosphate ore particle size on flotation index,suitable grinding fineness can be selected so as to reduce unnecessary loss in grinding process and improve flotation efficiency.