神经网络优化ITO废靶酸浸回收铟(英文)

以提高铟浸出率为目标,通过工艺实验结合神经网络研究ITO废靶酸浸的优化工艺。首先,固定其他工艺因素,进行单因素如残酸度、氧化剂加入量、酸浸温度及时间对铟浸出率影响的实验研究。结果表明,增大残酸度可提高铟浸出率;氧化剂的加入可明显提高铟浸出率,但增加到一定程度后浸出率提高不明显;升高温度可明显提高浸出率,但继续升高则会降低铟浸出率;延长浸出时间也可提高铟浸出率。通过反向传播算法的人工神经网络(BPNN)研究多因素的综合作用对铟浸出率的影响规律,预测值与实验值相差很小,表明所建立的BP模型铟浸出率能比较准确地预测。最终,通过BPNN预测以及实验验证,获得高达99.5%浸出率的工艺参数:残酸度50~60 g/L、氧化剂含量10%、浸出温度70°C和浸出时间2 h。

Recovery of indium by acid leaching waste ITO target based on neural network

The optimized leaching techniques were studied by technical experiment and neural network optimization for improving indium leaching rate. Firstly, effect of single technical parameter on leaching rate was investigated experimentally with other parameters fixed as constants. The results show that increasing residual acidity can improve leaching rate of indium. Increasing the oxidant content can obviously increase leaching rate but the further addition of oxidant could not improve the leaching rate. The enhancement of temperature can improve the leaching rate while the further enhancement of temperature decreases it. Extension leaching time can improve the leaching rate. On this basis, a BPNN model was established to study the effects of multi-parameters on leaching rate. The results show that the relative error is extremely small, thus the BPNN model has a high prediction precision. At last, optimized technical parameters which can yield high leaching rate of 99.5%were obtained by experimental and BPNN studies：residual acidity 50-60 g/L, oxidant addition content 10%, leaching temperature 70 ℃ and leaching time 2 h.