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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：利用响应面法（RSM）和人工神经网络（ANN）优化磁性纳米沸石上的二元溶液从二元溶液中除去锶和铯离子的去除

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摘要

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：通过考虑该过程的多变体/多目标性质，研究了使用磁性纳米沸石（MNZ）从其二元腐蚀溶液中除去铯和锶的可行性。 RSM（响应曲面方法）和ANN（人工神经网络）用于模拟和优化去除系统，并评估可能影响过程可靠性的敏感参数。 MNZ的特征在于其高表面积和阳离子交换能力，并使用柠檬酸对两种元素具有良好的再生行为。其稳定性与酸性介质中的其他吸附剂相当，碱性介质中的稳定性增加，其中溶解速率在异质位点上遵循一定顺序反应。 MNZ将两种污染物同时除去CS的小趋势，其中MNZ被建议用于应用高度污染的碱性溶液的预处理。百分比去除，去污因子和分离因子对流出物/工艺条件不同;污染物的这种依赖性是相同的。吸附动力学最初通过外部传质控制通过边界，然后红外粒子扩散占反应。对pH变化的过程敏感性归因于结构元件的变化 - 在固体/水界面处的分布分布。无论流出物pH值如何，用Na +和H +交换Cs +和Sr + 2，以及特定pH的Al和Fe阳离子。吸附的基位热量表明，通过在优化温度下操作该方法，完成反应所需的总热量显着降低。

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