Ammonia-nitrogen wastewater is produced during the dressing and smelting process of rare-earth ores.Such wastewater includes a very high concentration of NH4＋, as well as other ions（e.g., NH4＋, RE3＋, Al3＋, Fe3＋,...Ammonia-nitrogen wastewater is produced during the dressing and smelting process of rare-earth ores.Such wastewater includes a very high concentration of NH4＋, as well as other ions（e.g., NH4＋, RE3＋, Al3＋, Fe3＋, Ca2＋, Cl–, and Si O32–） with a p H of 5.4–5.6.Its direct discharge will pollute, yet it can be recycled and used as a leaching reagent for ionic rare-earth ores.In this study, leaching kinetics studies of both rare earth ions and impurity ion Al3＋ were conducted in the ammonia-nitrogen wastewater system with the aid of impurity inhibitors.Results showed that the leaching process of rare-earth followed the internal diffusion kinetic model.When the temperature was 298 K and the concentration of NH4＋ was 0.3 mol/L, the leaching reaction rate constant of ionic rare-earth was 1.72 and the apparent activation energy was 9.619 k J/mol.The leaching rate was higher than that of conventional leaching system with ammonium sulfate, which indicated that ammonia-nitrogen wastewater system and the addition of impurity inhibitors could promote ionic rare-earth leaching.The leaching kinetic process of impurity Al3＋ did not follow either internal diffusion kinetic model or chemical reaction control, but the hybrid control model which was affected by a number of process factors.Thus, during the industrial production the leaching of impurity ions could be reduced by increasing the concentration of impurity inhibitors, reducing the leaching temperature to a proper range, accelerating the seepage velocity of leaching solution, or increasing the leaching rate of rare earths.展开更多
通过隶属度函数确定的加权KNN-BP神经网络方法,建立PM_(2.5)浓度动态实时预测模型,以PM_(2.5)、PM_(10)、NO_2、CO、O_3、SO_2等6种污染物前1 h的浓度及天气现象、温度、气压、湿度、风速、风向等6种气象条件,以及预测时刻所在一周中天...通过隶属度函数确定的加权KNN-BP神经网络方法,建立PM_(2.5)浓度动态实时预测模型,以PM_(2.5)、PM_(10)、NO_2、CO、O_3、SO_2等6种污染物前1 h的浓度及天气现象、温度、气压、湿度、风速、风向等6种气象条件,以及预测时刻所在一周中天数和该时刻所在一天当中的小时数为KNN实例的维度,选取3个近邻,根据得到的欧氏距离确定每个近邻变量的隶属度权重,最终将所有近邻的维度作为BP神经网络的输入层数据,输出要预测的下1 h PM_(2.5)浓度,该方法避免了传统BP神经网络方法不能体现历史时间窗内的数据对当前预测影响的问题。对北京市东城区监测站2014-05-01T00:00—2014-09-10T23:00的数据进行预测试验,结果表明,加权KNN-BP神经网络预测模型相较其他方法的预测误差最低,且稳定性效果最好,是PM_(2.5)浓度实时预测的有效方法。展开更多
基金Project supported by National Natural Science Foundation of China(51164010)the Natural Science Foundation of Jiangxi Province(2010GZC0048)
文摘Ammonia-nitrogen wastewater is produced during the dressing and smelting process of rare-earth ores.Such wastewater includes a very high concentration of NH4＋, as well as other ions（e.g., NH4＋, RE3＋, Al3＋, Fe3＋, Ca2＋, Cl–, and Si O32–） with a p H of 5.4–5.6.Its direct discharge will pollute, yet it can be recycled and used as a leaching reagent for ionic rare-earth ores.In this study, leaching kinetics studies of both rare earth ions and impurity ion Al3＋ were conducted in the ammonia-nitrogen wastewater system with the aid of impurity inhibitors.Results showed that the leaching process of rare-earth followed the internal diffusion kinetic model.When the temperature was 298 K and the concentration of NH4＋ was 0.3 mol/L, the leaching reaction rate constant of ionic rare-earth was 1.72 and the apparent activation energy was 9.619 k J/mol.The leaching rate was higher than that of conventional leaching system with ammonium sulfate, which indicated that ammonia-nitrogen wastewater system and the addition of impurity inhibitors could promote ionic rare-earth leaching.The leaching kinetic process of impurity Al3＋ did not follow either internal diffusion kinetic model or chemical reaction control, but the hybrid control model which was affected by a number of process factors.Thus, during the industrial production the leaching of impurity ions could be reduced by increasing the concentration of impurity inhibitors, reducing the leaching temperature to a proper range, accelerating the seepage velocity of leaching solution, or increasing the leaching rate of rare earths.
文摘通过隶属度函数确定的加权KNN-BP神经网络方法,建立PM_(2.5)浓度动态实时预测模型,以PM_(2.5)、PM_(10)、NO_2、CO、O_3、SO_2等6种污染物前1 h的浓度及天气现象、温度、气压、湿度、风速、风向等6种气象条件,以及预测时刻所在一周中天数和该时刻所在一天当中的小时数为KNN实例的维度,选取3个近邻,根据得到的欧氏距离确定每个近邻变量的隶属度权重,最终将所有近邻的维度作为BP神经网络的输入层数据,输出要预测的下1 h PM_(2.5)浓度,该方法避免了传统BP神经网络方法不能体现历史时间窗内的数据对当前预测影响的问题。对北京市东城区监测站2014-05-01T00:00—2014-09-10T23:00的数据进行预测试验,结果表明,加权KNN-BP神经网络预测模型相较其他方法的预测误差最低,且稳定性效果最好,是PM_(2.5)浓度实时预测的有效方法。
