Artificial Neural Network Approach for Modeling of Conversion Rate of Refractory Gold Concentrate Oxidation by Nitric Acid
Considering that the pretreatment of refractory gold concentrate iS a complex and nonlinear process．it iS necessary to develop a new route tor optimum control and management．In this study,artificial neural network(ANN)was adopted．Particle size(50.335 μm)，reaction temperature(25-85°C)，nitric acid concentration(10-30％，wt．)，stirring speed(400.800 rpm)and reaction time(10-90 min)were chose as input variables，while conversion rate was chose as output target．The tansig function Was USed as the transfer function in the only hidden layer with 11 neurons and Iogsig transfer function at output layer．A feed forward neural network model with back propagation algorithm was developed to predict the conversion rate of refractory gold concentrate based on l 25 experimental sets obtained in a laboratory batch study．The mean squared error(MSE)become stable at 0.00099877 1 when the numbers of epochs reach 253.The model Was evaluated by comparing the simulated results with the experimental values and Was found to be in good agreement wim a correlation cOefficient of 0.99.
人工神经网络 金精矿 最优控制 转换率
Guolong Gao Dengxin Li Zuxi Yarg Lina Sun
College of Environmental Science and Engineering,Donghua University,Shanghai201620,China
国内会议
上海
英文
927-930
2008-12-08（万方平台首次上网日期，不代表论文的发表时间）