This study investigated the selective removal of arsenic from high-arsenic dust in alkaline systems and the effects of different leaching conditions. The results indicated that the liquid–solid ratio, NaOH concentration, and sulfur dosage had a significant influence on the process. The leaching efficiency of arsenic reached 99.57%, while that of lead was as low as 0.03% under appropriate conditions. In particular, the addition of sulfur can effectively promote the leaching of arsenic and reduce the dissolution of lead in the solutions. An artificial neural network was used to model the leaching process. It consisted of a back-propagation artificial neural network model with a “6–10–2” structure that could effectively simulate and predict the value with more than 99% accuracy. Based on the difference in weights of the different parameters in the neural network model, the relative importance of the parameters related to arsenic and lead leaching efficiency was obtained, which followed the order of NaOH concentration, liquid–solid ratio, sulfur dosage, temperature, time, and stirring speed.

本研究调查了碱性系统中高砷粉尘中砷的选择性去除以及不同浸出条件的影响。结果表明，液固比、NaOH 浓度和硫磺用量对工艺有显着影响。砷的浸出率达到99.57%，而铅的浸出率在适当条件下低至0.03%。特别是硫的加入可以有效促进砷的浸出，减少溶液中铅的溶解。人工神经网络用于模拟浸出过程。它由一个具有“6-10-2”结构的反向传播人工神经网络模型组成，可以有效地模拟和预测值，准确率超过 99%。