Surface roughness of quartz particles was determined by measuring the specific surface area of particles.The wettability characteristics of particles were determined by measuring the flotation rate using a laboratory ...Surface roughness of quartz particles was determined by measuring the specific surface area of particles.The wettability characteristics of particles were determined by measuring the flotation rate using a laboratory flotation cell.Experimental results show that the rod mill product has higher roughness than the ball mill product.For the particles with larger surface roughness,the flotation kinetics constant is also higher.Finally,empirical relationships between surface roughness(r) and the flotation kinetics constant(k) of quartz particles as k=A+Br+Cr0.5lnr+D/lnr+E/r and k=A+Br are presented,in which A,B,C,D and E are constants related to experimental conditions and mineralogical properties of mineral.展开更多
The effects of cyanidation conditions on gold dissolution were studied by artificial neural network (ANN) modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid pe...The effects of cyanidation conditions on gold dissolution were studied by artificial neural network (ANN) modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P80 of particle, NaCN content in cyanide media, temperature of solution and pH value were used. For selecting the best model, the outputs of models were compared with measured data. A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients (R2) of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.998 1, respectively. Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively. It is verified that the predicted values of ANN coincide well with the experimental results.展开更多
Adaptive neuro fuzzy inference system (ANFIS) procedure and regression methods were used to predict the Sauter mean bubble (bubble diameter) and surface area flux of the bubble in a flotation process. The operational ...Adaptive neuro fuzzy inference system (ANFIS) procedure and regression methods were used to predict the Sauter mean bubble (bubble diameter) and surface area flux of the bubble in a flotation process. The operational conditions of flotation, impeller peripheral speed, superficial gas velocity, and weight percent solids were used as inputs of methods. By using the mentioned operational conditions, the non linear regression results showed that Sauter mean, and surface area flux of the bubble are predictable variables, where the coefficients of determination (R 2 ) are 0.57 and 0.74, respectively. To increase the accuracy of prediction an ANFIS model with cluster radius of 0.4 was applied. ANFIS model was capable of estimating both Sauter mean, and surface area flux of the bubble, where in a testing stage, satisfactory correlations, R 2 = 0.78, and 0.86, were achieved for Sauter mean, and surface area flux of bubble, respectively. Results show that the proposed ANFIS model can accurately estimate outputs and be used in order to predict the parameters without having to conduct the new experiments in a laboratory.展开更多
文摘Surface roughness of quartz particles was determined by measuring the specific surface area of particles.The wettability characteristics of particles were determined by measuring the flotation rate using a laboratory flotation cell.Experimental results show that the rod mill product has higher roughness than the ball mill product.For the particles with larger surface roughness,the flotation kinetics constant is also higher.Finally,empirical relationships between surface roughness(r) and the flotation kinetics constant(k) of quartz particles as k=A+Br+Cr0.5lnr+D/lnr+E/r and k=A+Br are presented,in which A,B,C,D and E are constants related to experimental conditions and mineralogical properties of mineral.
文摘The effects of cyanidation conditions on gold dissolution were studied by artificial neural network (ANN) modeling. Eighty-five datasets were used to estimate the gold dissolution. Six input parameters, time, solid percentage, P80 of particle, NaCN content in cyanide media, temperature of solution and pH value were used. For selecting the best model, the outputs of models were compared with measured data. A fourth-layer ANN is found to be optimum with architecture of twenty, fifteen, ten and five neurons in the first, second, third and fourth hidden layers, respectively, and one neuron in output layer. The results of artificial neural network show that the square correlation coefficients (R2) of training, testing and validating data achieve 0.999 1, 0.996 4 and 0.998 1, respectively. Sensitivity analysis shows that the highest and lowest effects on the gold dissolution rise from time and pH, respectively. It is verified that the predicted values of ANN coincide well with the experimental results.
文摘Adaptive neuro fuzzy inference system (ANFIS) procedure and regression methods were used to predict the Sauter mean bubble (bubble diameter) and surface area flux of the bubble in a flotation process. The operational conditions of flotation, impeller peripheral speed, superficial gas velocity, and weight percent solids were used as inputs of methods. By using the mentioned operational conditions, the non linear regression results showed that Sauter mean, and surface area flux of the bubble are predictable variables, where the coefficients of determination (R 2 ) are 0.57 and 0.74, respectively. To increase the accuracy of prediction an ANFIS model with cluster radius of 0.4 was applied. ANFIS model was capable of estimating both Sauter mean, and surface area flux of the bubble, where in a testing stage, satisfactory correlations, R 2 = 0.78, and 0.86, were achieved for Sauter mean, and surface area flux of bubble, respectively. Results show that the proposed ANFIS model can accurately estimate outputs and be used in order to predict the parameters without having to conduct the new experiments in a laboratory.
