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首页> 外文期刊>Environmental research >Optimizing the removal of strontium and cesium ions from binary solutions on magnetic nano-zeolite using response surface methodology (RSM) and artificial neural network (ANN)
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Optimizing the removal of strontium and cesium ions from binary solutions on magnetic nano-zeolite using response surface methodology (RSM) and artificial neural network (ANN)

机译：使用响应表面方法（RSM）和人工神经网络（ANN）优化磁性纳米沸石上二元溶液中锶和铯离子的去除

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The feasibility of using magnetic nano-zeolite (MNZ) to remove cesium and strontium from their binary corrosive solutions was investigated by considering the multi-variant/multi-objective nature of the process. RSM (Response Surface Methodology) and ANN (Artificial Neural Network) were used to model and optimize the removal system and assess sensitive parameters that can affect the process reliability. MNZ is characterized by its high surface area and cation exchange capacity and possesses good regeneration behavior for both elements using citric acid. Its stability is comparable to other sorbents in acidic media and the stability increases in alkaline media, where dissolution rate follow first order reaction on heterogeneous sites. MNZ removes both contaminants simultaneously with small tendency toward Cs, where MNZ is suggested for application in pretreatment of highly contaminated alkaline solutions. The percentage removal, decontamination factors, and separation factors have different dependency on the effluent/process conditions; this dependency is the same for both contaminants. Sorption kinetics is initially controlled by external mass transfer through the boundaries then infra-particle diffusion dominates the reactions. The process sensitivity to pH changes is attributed to changes in structural elements -species distribution at the solid/aqueous interface. Cs+ and Sr+2 are exchanged with Na+ and H+ , regardless the effluent pH value, and with Al and Fe cations at specific pH. Isosteric heat of sorption calculations indicated that the total heat needed to complete the reaction was considerably reduced by operating the process at optimized temperature.
机译：考虑到过程的多变量/多目标性质，研究了使用磁性纳米沸石（MNZ）从二元腐蚀性溶液中去除铯和锶的可行性。 RSM（响应表面方法）和ANN（人工神经网络）用于建模和优化去除系统，并评估可能影响工艺可靠性的敏感参数。 MNZ具有高表面积和阳离子交换能力的特点，并且使用柠檬酸对两种元素均具有良好的再生性能。它的稳定性可与酸性介质中的其他吸附剂媲美，而碱性介质的稳定性则有所提高，在碱性介质中，溶解速率遵循异质位点上的一级反应。 MNZ可以同时去除两种污染物，并且几乎没有Cs倾向，因此建议将MNZ用于高度污染的碱性溶液的预处理。去除百分比，净化系数和分离系数对废水/工艺条件的依赖性不同；两种污染物的依赖性相同。吸附动力学最初由外部质量通过边界进行控制，然后粒子内扩散控制了反应。 pH值变化的过程敏感性归因于结构元素的变化-固体/水界面处的物种分布。无论流出液的pH值如何，Cs +和Sr + 2均与Na +和H +交换，并且在特定pH下与Al和Fe阳离子交换。吸附的等位线热量计算表明，通过在最佳温度下操作该过程，可以大大减少完成反应所需的总热量。

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